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distil-whisper/meanwhile

distil-whisper

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: audio dtype: audio - name: begin dtype: string - name: end dtype: string - name: text dtype: string splits: - name: test num_bytes: 58250833.0 num_examples: 64 download_size: 58229969 dataset_size: 58250833.0 --- # Dataset Card for "meanwhile" This dataset consists of 64 segments from The Late Show with Stephen Colbert. This dataset was published as part of the Whisper release by OpenAI. See page 19 of the [Whisper paper](https://arxiv.org/pdf/2212.04356.pdf) for details.

jkot/parliament_hearings_processed

jkot

--- dataset_info: features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 51234859011.0 num_examples: 191455 - name: test num_bytes: 762989296.0 num_examples: 2726 download_size: 51507735963 dataset_size: 51997848307.0 --- # Preprocessed parliament hearings ASR dataset to truecased form. ## Original dataset: https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3126 --- dataset_info: features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: string splits: - name: train num_bytes: 53645064353.18 num_examples: 191455 - name: test num_bytes: 740331298.0 num_examples: 2726 download_size: 51507379112 dataset_size: 54385395651.18 ---

ybelkada/english_quotes_copy

ybelkada

--- dataset_info: features: - name: quote dtype: string - name: author dtype: string - name: tags sequence: string splits: - name: train num_bytes: 598359 num_examples: 2508 download_size: 349107 dataset_size: 598359 --- # Dataset Card for "english_quotes_copy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sailor2/sea-commoncrawl

sailor2

--- license: odc-by ---

autogluon/chronos_datasets

autogluon

--- annotations_creators: - no-annotation license: other source_datasets: - original task_categories: - time-series-forecasting task_ids: - univariate-time-series-forecasting - multivariate-time-series-forecasting pretty_name: Chronos datasets dataset_info: - config_name: dominick features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: im_0 dtype: int64 splits: - name: train num_bytes: 477140250 num_examples: 100014 download_size: 42290010 dataset_size: 477140250 - config_name: electricity_15min features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: consumption_kW sequence: float64 splits: - name: train num_bytes: 670989988 num_examples: 370 download_size: 284497403 dataset_size: 670989988 license: CC BY 4.0 homepage: https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014 - config_name: ercot features: - name: id dtype: string - name: timestamp sequence: timestamp[ns] - name: target sequence: float32 splits: - name: train num_examples: 8 download_size: 14504261 - config_name: exchange_rate features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 splits: - name: train num_examples: 8 download_size: 401501 license: MIT homepage: https://github.com/laiguokun/multivariate-time-series-data/tree/master/exchange_rate - config_name: m4_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 160504176 num_examples: 4227 download_size: 65546675 dataset_size: 160504176 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_hourly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 5985544 num_examples: 414 download_size: 1336971 dataset_size: 5985544 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 181372969 num_examples: 48000 download_size: 52772258 dataset_size: 181372969 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 39205397 num_examples: 24000 download_size: 13422579 dataset_size: 39205397 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 5955806 num_examples: 359 download_size: 2556691 dataset_size: 5955806 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m4_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: category dtype: string splits: - name: train num_bytes: 14410042 num_examples: 23000 download_size: 5488601 dataset_size: 14410042 homepage: https://github.com/Mcompetitions/M4-methods - config_name: m5 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: item_id dtype: string - name: target sequence: float32 - name: dept_id dtype: string - name: cat_id dtype: string - name: store_id dtype: string - name: state_id dtype: string splits: - name: train num_bytes: 574062630 num_examples: 30490 download_size: 78063286 dataset_size: 574062630 homepage: https://www.kaggle.com/competitions/m5-forecasting-accuracy/rules - config_name: mexico_city_bikes features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 618999406 num_examples: 494 download_size: 103206946 dataset_size: 618999406 homepage: https://ecobici.cdmx.gob.mx/en/open-data/ - config_name: monash_australian_electricity features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 18484319 num_examples: 5 download_size: 16856156 dataset_size: 18484319 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_car_parts features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 2232790 num_examples: 2674 download_size: 70278 dataset_size: 2232790 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_cif_2016 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 115096 num_examples: 72 download_size: 70876 dataset_size: 115096 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_covid_deaths features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 907326 num_examples: 266 download_size: 58957 dataset_size: 907326 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_electricity_hourly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 135103443 num_examples: 321 download_size: 31139117 dataset_size: 135103443 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_electricity_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 807315 num_examples: 321 download_size: 333563 dataset_size: 807315 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_fred_md features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 1248369 num_examples: 107 download_size: 412207 dataset_size: 1248369 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_hospital features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: int64 splits: - name: train num_examples: 767 download_size: 117038 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_kdd_cup_2018 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: city dtype: string - name: station dtype: string - name: measurement dtype: string splits: - name: train num_bytes: 47091540 num_examples: 270 download_size: 8780105 dataset_size: 47091540 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_london_smart_meters features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 2664567976 num_examples: 5560 download_size: 597389119 dataset_size: 2664567976 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m1_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 907691 num_examples: 617 download_size: 244372 dataset_size: 907691 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m1_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 162961 num_examples: 203 download_size: 48439 dataset_size: 162961 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m1_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 75679 num_examples: 181 download_size: 30754 dataset_size: 75679 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m3_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 2708124 num_examples: 1428 download_size: 589699 dataset_size: 2708124 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m3_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 606428 num_examples: 756 download_size: 188543 dataset_size: 606428 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_m3_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 305359 num_examples: 645 download_size: 100184 dataset_size: 305359 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_nn5_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 splits: - name: train num_examples: 111 download_size: 64620 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_pedestrian_counts features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: int64 splits: - name: train num_bytes: 50118790 num_examples: 66 download_size: 12377357 dataset_size: 50118790 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_rideshare features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: source_location dtype: string - name: provider_name dtype: string - name: provider_service dtype: string - name: price_min sequence: float64 - name: price_mean sequence: float64 - name: price_max sequence: float64 - name: distance_min sequence: float64 - name: distance_mean sequence: float64 - name: distance_max sequence: float64 - name: surge_min sequence: float64 - name: surge_mean sequence: float64 - name: surge_max sequence: float64 - name: api_calls sequence: float64 - name: temp sequence: float64 - name: rain sequence: float64 - name: humidity sequence: float64 - name: clouds sequence: float64 - name: wind sequence: float64 splits: - name: train num_bytes: 10819910 num_examples: 156 download_size: 781873 dataset_size: 10819910 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_saugeenday features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: T1 sequence: float64 splits: - name: train num_bytes: 379875 num_examples: 1 download_size: 222678 dataset_size: 379875 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_temperature_rain features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: t_mean sequence: float64 - name: prcp_sum sequence: float64 - name: t_max sequence: float64 - name: t_min sequence: float64 - name: fcst_0_dailypop sequence: float64 - name: fcst_0_dailypop1 sequence: float64 - name: fcst_0_dailypop10 sequence: float64 - name: fcst_0_dailypop15 sequence: float64 - name: fcst_0_dailypop25 sequence: float64 - name: fcst_0_dailypop5 sequence: float64 - name: fcst_0_dailypop50 sequence: float64 - name: fcst_0_dailyprecip sequence: float64 - name: fcst_0_dailyprecip10pct sequence: float64 - name: fcst_0_dailyprecip25pct sequence: float64 - name: fcst_0_dailyprecip50pct sequence: float64 - name: fcst_0_dailyprecip75pct sequence: float64 - name: fcst_1_dailypop sequence: float64 - name: fcst_1_dailypop1 sequence: float64 - name: fcst_1_dailypop10 sequence: float64 - name: fcst_1_dailypop15 sequence: float64 - name: fcst_1_dailypop25 sequence: float64 - name: fcst_1_dailypop5 sequence: float64 - name: fcst_1_dailypop50 sequence: float64 - name: fcst_1_dailyprecip sequence: float64 - name: fcst_1_dailyprecip10pct sequence: float64 - name: fcst_1_dailyprecip25pct sequence: float64 - name: fcst_1_dailyprecip50pct sequence: float64 - name: fcst_1_dailyprecip75pct sequence: float64 - name: fcst_2_dailypop sequence: float64 - name: fcst_2_dailypop1 sequence: float64 - name: fcst_2_dailypop10 sequence: float64 - name: fcst_2_dailypop15 sequence: float64 - name: fcst_2_dailypop25 sequence: float64 - name: fcst_2_dailypop5 sequence: float64 - name: fcst_2_dailypop50 sequence: float64 - name: fcst_2_dailyprecip sequence: float64 - name: fcst_2_dailyprecip10pct sequence: float64 - name: fcst_2_dailyprecip25pct sequence: float64 - name: fcst_2_dailyprecip50pct sequence: float64 - name: fcst_2_dailyprecip75pct sequence: float64 - name: fcst_3_dailypop sequence: float64 - name: fcst_3_dailypop1 sequence: float64 - name: fcst_3_dailypop10 sequence: float64 - name: fcst_3_dailypop15 sequence: float64 - name: fcst_3_dailypop25 sequence: float64 - name: fcst_3_dailypop5 sequence: float64 - name: fcst_3_dailypop50 sequence: float64 - name: fcst_3_dailyprecip sequence: float64 - name: fcst_3_dailyprecip10pct sequence: float64 - name: fcst_3_dailyprecip25pct sequence: float64 - name: fcst_3_dailyprecip50pct sequence: float64 - name: fcst_3_dailyprecip75pct sequence: float64 - name: fcst_4_dailypop sequence: float64 - name: fcst_4_dailypop1 sequence: float64 - name: fcst_4_dailypop10 sequence: float64 - name: fcst_4_dailypop15 sequence: float64 - name: fcst_4_dailypop25 sequence: float64 - name: fcst_4_dailypop5 sequence: float64 - name: fcst_4_dailypop50 sequence: float64 - name: fcst_4_dailyprecip sequence: float64 - name: fcst_4_dailyprecip10pct sequence: float64 - name: fcst_4_dailyprecip25pct sequence: float64 - name: fcst_4_dailyprecip50pct sequence: float64 - name: fcst_4_dailyprecip75pct sequence: float64 - name: fcst_5_dailypop sequence: float64 - name: fcst_5_dailypop1 sequence: float64 - name: fcst_5_dailypop10 sequence: float64 - name: fcst_5_dailypop15 sequence: float64 - name: fcst_5_dailypop25 sequence: float64 - name: fcst_5_dailypop5 sequence: float64 - name: fcst_5_dailypop50 sequence: float64 - name: fcst_5_dailyprecip sequence: float64 - name: fcst_5_dailyprecip10pct sequence: float64 - name: fcst_5_dailyprecip25pct sequence: float64 - name: fcst_5_dailyprecip50pct sequence: float64 - name: fcst_5_dailyprecip75pct sequence: float64 splits: - name: train num_bytes: 188598927 num_examples: 422 download_size: 44967856 dataset_size: 188598927 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_tourism_monthly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 1755434 num_examples: 366 download_size: 334951 dataset_size: 1755434 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_tourism_quarterly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 688817 num_examples: 427 download_size: 177407 dataset_size: 688817 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_tourism_yearly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 213954 num_examples: 518 download_size: 81479 dataset_size: 213954 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_traffic features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 241983226 num_examples: 862 download_size: 52748547 dataset_size: 241983226 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: monash_weather features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: subset dtype: string splits: - name: train num_bytes: 688598539 num_examples: 3010 download_size: 133164027 dataset_size: 688598539 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: nn5 features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 splits: - name: train num_examples: 111 download_size: 203096 homepage: http://www.neural-forecasting-competition.com/downloads/NN5/datasets/download.htm - config_name: solar features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: power_mw sequence: float64 - name: latitude dtype: float64 - name: longitude dtype: float64 - name: capacity_mw dtype: float64 - name: subset dtype: string splits: - name: train num_bytes: 8689093932 num_examples: 5166 download_size: 1507924920 dataset_size: 8689093932 homepage: https://www.nrel.gov/grid/solar-power-data.html - config_name: solar_1h features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: power_mw sequence: float64 - name: latitude dtype: float64 - name: longitude dtype: float64 - name: capacity_mw dtype: float64 - name: subset dtype: string splits: - name: train num_bytes: 724361772 num_examples: 5166 download_size: 124515417 dataset_size: 724361772 homepage: https://www.nrel.gov/grid/solar-power-data.html - config_name: taxi_1h features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: subset dtype: string - name: lat dtype: float64 - name: lng dtype: float64 splits: - name: train num_bytes: 28832500 num_examples: 2428 download_size: 2265297 dataset_size: 28832500 license: Apache 2.0 homepage: https://github.com/mbohlkeschneider/gluon-ts/tree/mv_release/datasets - config_name: taxi_30min features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: subset dtype: string - name: lat dtype: float64 - name: lng dtype: float64 splits: - name: train num_bytes: 57560596 num_examples: 2428 download_size: 4541244 dataset_size: 57560596 license: Apache 2.0 homepage: https://github.com/mbohlkeschneider/gluon-ts/tree/mv_release/datasets - config_name: training_corpus_kernel_synth_1m features: - name: target sequence: float64 - name: id dtype: string - name: timestamp sequence: timestamp[ms] splits: - name: train num_examples: 1000000 download_size: 8313239368 - config_name: training_corpus_tsmixup_10m features: - name: target sequence: float64 - name: id dtype: string - name: timestamp sequence: timestamp[ms] splits: - name: train num_examples: 10000000 download_size: 82189589906 - config_name: uber_tlc_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: int64 splits: - name: train num_examples: 262 download_size: 84747 homepage: https://github.com/fivethirtyeight/uber-tlc-foil-response - config_name: uber_tlc_hourly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: int64 splits: - name: train num_examples: 262 download_size: 1878515 homepage: https://github.com/fivethirtyeight/uber-tlc-foil-response - config_name: ushcn_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: state dtype: string - name: coop_id dtype: int64 - name: PRCP sequence: float64 - name: SNOW sequence: float64 - name: SNWD sequence: float64 - name: TMAX sequence: float64 - name: TMIN sequence: float64 splits: - name: train num_bytes: 2259905202 num_examples: 1218 download_size: 221089890 dataset_size: 2259905202 homepage: https://data.ess-dive.lbl.gov/portals/CDIAC - config_name: weatherbench_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 - name: latitude dtype: float64 - name: longitude dtype: float64 - name: level dtype: float64 - name: subset dtype: string splits: - name: train num_bytes: 39510157312 num_examples: 225280 download_size: 18924392742 dataset_size: 39510157312 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_10m_u_component_of_wind features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 7292845757 dataset_size: 8617472000 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_10m_v_component_of_wind features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 7292352508 dataset_size: 8617472000 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_2m_temperature features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 7276396852 dataset_size: 8617453568 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_geopotential features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 87305564613 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_potential_vorticity features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 92426240043 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_relative_humidity features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 94728788382 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_specific_humidity features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 85139896451 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_temperature features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 94081539079 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_toa_incident_solar_radiation features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 6057953007 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_total_cloud_cover features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 6628258398 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_total_precipitation features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: float64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 2048 download_size: 6473160755 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_u_component_of_wind features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 94801498563 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_v_component_of_wind features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 94800557482 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_hourly_vorticity features: - name: latitude dtype: float64 - name: longitude dtype: float64 - name: target sequence: float32 - name: level dtype: int64 - name: timestamp sequence: timestamp[ms] - name: subset dtype: string - name: id dtype: string splits: - name: train num_examples: 26624 download_size: 94720960560 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: weatherbench_weekly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float32 - name: latitude dtype: float64 - name: longitude dtype: float64 - name: level dtype: float64 - name: subset dtype: string splits: - name: train num_bytes: 5656029184 num_examples: 225280 download_size: 2243012083 dataset_size: 5656029184 license: MIT homepage: https://github.com/pangeo-data/WeatherBench - config_name: wiki_daily_100k features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 - name: page_name dtype: string splits: - name: train num_bytes: 4389782678 num_examples: 100000 download_size: 592554033 dataset_size: 4389782678 license: CC0 homepage: https://dumps.wikimedia.org/other/pageviews/readme.html - config_name: wind_farms_daily features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 1919187 num_examples: 337 download_size: 598834 dataset_size: 1919187 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting - config_name: wind_farms_hourly features: - name: id dtype: string - name: timestamp sequence: timestamp[ms] - name: target sequence: float64 splits: - name: train num_bytes: 45917027 num_examples: 337 download_size: 12333116 dataset_size: 45917027 license: CC BY 4.0 homepage: https://zenodo.org/communities/forecasting configs: - config_name: dominick data_files: - split: train path: dominick/train-* - config_name: electricity_15min data_files: - split: train path: electricity_15min/train-* - config_name: ercot data_files: - split: train path: ercot/train-* - config_name: exchange_rate data_files: - split: train path: exchange_rate/train-* - config_name: m4_daily data_files: - split: train path: m4_daily/train-* - config_name: m4_hourly data_files: - split: train path: m4_hourly/train-* - config_name: m4_monthly data_files: - split: train path: m4_monthly/train-* - config_name: m4_quarterly data_files: - split: train path: m4_quarterly/train-* - config_name: m4_weekly data_files: - split: train path: m4_weekly/train-* - config_name: m4_yearly data_files: - split: train path: m4_yearly/train-* - config_name: m5 data_files: - split: train path: m5/train-* - config_name: mexico_city_bikes data_files: - split: train path: mexico_city_bikes/train-* - config_name: monash_australian_electricity data_files: - split: train path: monash_australian_electricity/train-* - config_name: monash_car_parts data_files: - split: train path: monash_car_parts/train-* - config_name: monash_cif_2016 data_files: - split: train path: monash_cif_2016/train-* - config_name: monash_covid_deaths data_files: - split: train path: monash_covid_deaths/train-* - config_name: monash_electricity_hourly data_files: - split: train path: monash_electricity_hourly/train-* - config_name: monash_electricity_weekly data_files: - split: train path: monash_electricity_weekly/train-* - config_name: monash_fred_md data_files: - split: train path: monash_fred_md/train-* - config_name: monash_hospital data_files: - split: train path: monash_hospital/train-* - config_name: monash_kdd_cup_2018 data_files: - split: train path: monash_kdd_cup_2018/train-* - config_name: monash_london_smart_meters data_files: - split: train path: monash_london_smart_meters/train-* - config_name: monash_m1_monthly data_files: - split: train path: monash_m1_monthly/train-* - config_name: monash_m1_quarterly data_files: - split: train path: monash_m1_quarterly/train-* - config_name: monash_m1_yearly data_files: - split: train path: monash_m1_yearly/train-* - config_name: monash_m3_monthly data_files: - split: train path: monash_m3_monthly/train-* - config_name: monash_m3_quarterly data_files: - split: train path: monash_m3_quarterly/train-* - config_name: monash_m3_yearly data_files: - split: train path: monash_m3_yearly/train-* - config_name: monash_nn5_weekly data_files: - split: train path: monash_nn5_weekly/train-* - config_name: monash_pedestrian_counts data_files: - split: train path: monash_pedestrian_counts/train-* - config_name: monash_rideshare data_files: - split: train path: monash_rideshare/train-* - config_name: monash_saugeenday data_files: - split: train path: monash_saugeenday/train-* - config_name: monash_temperature_rain data_files: - split: train path: monash_temperature_rain/train-* - config_name: monash_tourism_monthly data_files: - split: train path: monash_tourism_monthly/train-* - config_name: monash_tourism_quarterly data_files: - split: train path: monash_tourism_quarterly/train-* - config_name: monash_tourism_yearly data_files: - split: train path: monash_tourism_yearly/train-* - config_name: monash_traffic data_files: - split: train path: monash_traffic/train-* - config_name: monash_weather data_files: - split: train path: monash_weather/train-* - config_name: nn5 data_files: - split: train path: nn5/train-* - config_name: solar data_files: - split: train path: solar/train-* - config_name: solar_1h data_files: - split: train path: solar_1h/train-* - config_name: taxi_1h data_files: - split: train path: taxi_1h/train-* - config_name: taxi_30min data_files: - split: train path: taxi_30min/train-* - config_name: training_corpus_kernel_synth_1m data_files: - split: train path: training_corpus/kernel_synth_1m/train-* - config_name: training_corpus_tsmixup_10m data_files: - split: train path: training_corpus/tsmixup_10m/train-* - config_name: uber_tlc_daily data_files: - split: train path: uber_tlc_daily/train-* - config_name: uber_tlc_hourly data_files: - split: train path: uber_tlc_hourly/train-* - config_name: ushcn_daily data_files: - split: train path: ushcn_daily/train-* - config_name: weatherbench_daily data_files: - split: train path: weatherbench_daily/train-* - config_name: weatherbench_hourly_10m_u_component_of_wind data_files: - split: train path: weatherbench_hourly/10m_u_component_of_wind/train-* - config_name: weatherbench_hourly_10m_v_component_of_wind data_files: - split: train path: weatherbench_hourly/10m_v_component_of_wind/train-* - config_name: weatherbench_hourly_2m_temperature data_files: - split: train path: weatherbench_hourly/2m_temperature/train-* - config_name: weatherbench_hourly_geopotential data_files: - split: train path: weatherbench_hourly/geopotential/train-* - config_name: weatherbench_hourly_potential_vorticity data_files: - split: train path: weatherbench_hourly/potential_vorticity/train-* - config_name: weatherbench_hourly_relative_humidity data_files: - split: train path: weatherbench_hourly/relative_humidity/train-* - config_name: weatherbench_hourly_specific_humidity data_files: - split: train path: weatherbench_hourly/specific_humidity/train-* - config_name: weatherbench_hourly_temperature data_files: - split: train path: weatherbench_hourly/temperature/train-* - config_name: weatherbench_hourly_toa_incident_solar_radiation data_files: - split: train path: weatherbench_hourly/toa_incident_solar_radiation/train-* - config_name: weatherbench_hourly_total_cloud_cover data_files: - split: train path: weatherbench_hourly/total_cloud_cover/train-* - config_name: weatherbench_hourly_total_precipitation data_files: - split: train path: weatherbench_hourly/total_precipitation/train-* - config_name: weatherbench_hourly_u_component_of_wind data_files: - split: train path: weatherbench_hourly/u_component_of_wind/train-* - config_name: weatherbench_hourly_v_component_of_wind data_files: - split: train path: weatherbench_hourly/v_component_of_wind/train-* - config_name: weatherbench_hourly_vorticity data_files: - split: train path: weatherbench_hourly/vorticity/train-* - config_name: weatherbench_weekly data_files: - split: train path: weatherbench_weekly/train-* - config_name: wiki_daily_100k data_files: - split: train path: wiki_daily_100k/train-* - config_name: wind_farms_daily data_files: - split: train path: wind_farms_daily/train-* - config_name: wind_farms_hourly data_files: - split: train path: wind_farms_hourly/train-* --- # Chronos datasets Time series datasets used for training and evaluation of the [Chronos](https://github.com/amazon-science/chronos-forecasting) forecasting models. Note that some Chronos datasets (`ETTh`, `ETTm`, `brazilian_cities_temperature` and `spanish_energy_and_weather`) that rely on a custom builder script are available in the companion repo [`autogluon/chronos_datasets_extra`](https://huggingface.co/datasets/autogluon/chronos_datasets_extra). See the [paper](https://arxiv.org/abs/2403.07815) for more information. ## Data format and usage All datasets satisfy the following high-level schema: - Each dataset row corresponds to a single (univariate or multivariate) time series. - There exists one column with name `id` and type `string` that contains the unique identifier of each time series. - There exists one column of type `Sequence` with dtype `timestamp[ms]`. This column contains the timestamps of the observations. Timestamps are guaranteed to have a regular frequency that can be obtained with [`pandas.infer_freq`](https://pandas.pydata.org/docs/reference/api/pandas.infer_freq.html). - There exists at least one column of type `Sequence` with numeric (`float`, `double`, or `int`) dtype. These columns can be interpreted as target time series. - For each row, all columns of type `Sequence` have same length. - Remaining columns of types other than `Sequence` (e.g., `string` or `float`) can be interpreted as static covariates. Datasets can be loaded using the 🤗 [`datasets`](https://huggingface.co/docs/datasets/en/index) library ```python import datasets ds = datasets.load_dataset("autogluon/chronos_datasets", "m4_daily", split="train") ds.set_format("numpy") # sequences returned as numpy arrays ``` > **NOTE:** The `train` split of all datasets contains the full time series and has no relation to the train/test split used in the Chronos paper. Example entry in the `m4_daily` dataset ```python >>> ds[0] {'id': 'T000000', 'timestamp': array(['1994-03-01T12:00:00.000', '1994-03-02T12:00:00.000', '1994-03-03T12:00:00.000', ..., '1996-12-12T12:00:00.000', '1996-12-13T12:00:00.000', '1996-12-14T12:00:00.000'], dtype='datetime64[ms]'), 'target': array([1017.1, 1019.3, 1017. , ..., 2071.4, 2083.8, 2080.6], dtype=float32), 'category': 'Macro'} ``` ### Converting to pandas We can easily convert data in such format to a long format data frame ```python def to_pandas(ds: datasets.Dataset) -> "pd.DataFrame": """Convert dataset to long data frame format.""" sequence_columns = [col for col in ds.features if isinstance(ds.features[col], datasets.Sequence)] return ds.to_pandas().explode(sequence_columns).infer_objects() ``` Example output ```python >>> print(to_pandas(ds).head()) id timestamp target category 0 T000000 1994-03-01 12:00:00 1017.1 Macro 1 T000000 1994-03-02 12:00:00 1019.3 Macro 2 T000000 1994-03-03 12:00:00 1017.0 Macro 3 T000000 1994-03-04 12:00:00 1019.2 Macro 4 T000000 1994-03-05 12:00:00 1018.7 Macro ``` ### Dealing with large datasets Note that some datasets, such as subsets of WeatherBench, are extremely large (~100GB). To work with them efficiently, we recommend either loading them from disk (files will be downloaded to disk, but won't be all loaded into memory) ```python ds = datasets.load_dataset("autogluon/chronos_datasets", "weatherbench_daily", keep_in_memory=False, split="train") ``` or, for the largest datasets like `weatherbench_hourly_temperature`, reading them in streaming format (chunks will be downloaded one at a time) ```python ds = datasets.load_dataset("autogluon/chronos_datasets", "weatherbench_hourly_temperature", streaming=True, split="train") ``` ## Chronos training corpus with TSMixup & KernelSynth The training corpus used for training the Chronos models can be loaded via the configs `training_corpus_tsmixup_10m` (10M TSMixup augmentations of real-world data) and `training_corpus_kernel_synth_1m` (1M synthetic time series generated with KernelSynth), e.g., ```python ds = datasets.load_dataset("autogluon/chronos_datasets", "training_corpus_tsmixup_10m", streaming=True, split="train") ``` Note that since data in the training corpus was obtained by combining various synthetic & real-world time series, the timestamps contain dummy values that have no connection to the original data. ## License Different datasets available in this collection are distributed under different open source licenses. Please see `ds.info.license` and `ds.info.homepage` for each individual dataset. ## Citation If you find these datasets useful for your research, please consider citing the associated paper: ```markdown @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Wang, Hao and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ```

echo840/OCRBench

echo840

--- dataset_info: features: - name: dataset dtype: string - name: question dtype: string - name: question_type dtype: string - name: answer sequence: string - name: image dtype: image splits: - name: test num_bytes: 85534416.0 num_examples: 1000 download_size: 67576988 dataset_size: 85534416.0 configs: - config_name: default data_files: - split: test path: data/test-* --- [Github](https://github.com/Yuliang-Liu/MultimodalOCR)|[Paper](https://arxiv.org/abs/2305.07895) OCRBench has been accepted by [Science China Information Sciences](https://link.springer.com/article/10.1007/s11432-024-4235-6).

diffusers/test-arrays

diffusers

--- license: apache-2.0 ---

occiglot/tokenizer-wiki-bench

occiglot

--- language: - af - ar - bg - ca - cs - da - de - el - en - es - et - eu - fa - fi - fr - ga - he - hi - hr - hu - hy - id - it - ja - ko - lt - lv - mr - nl - 'no' - pl - pt - ro - ru - sa - sk - sl - sr - sv - ta - te - tr - uk - ur - vi license: mit pretty_name: Multilingual Tokenizer Wikipedia Benchmark dataset_info: - config_name: af features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 541481060 num_examples: 112518 - name: clean num_bytes: 539551289.6071739 num_examples: 112117 download_size: 441191361 dataset_size: 1081032349.607174 - config_name: ar features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 7007645793 num_examples: 1219201 - name: clean num_bytes: 6980694657.688122 num_examples: 1214512 download_size: 4415559180 dataset_size: 13988340450.688122 - config_name: bg features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 2437923560 num_examples: 294275 - name: clean num_bytes: 2433855866.6248918 num_examples: 293784 download_size: 1805069655 dataset_size: 4871779426.624891 - config_name: ca features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 4801022979 num_examples: 737409 - name: clean num_bytes: 4766991732.959834 num_examples: 732182 download_size: 3884482903 dataset_size: 9568014711.959835 - config_name: cs features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 3740905267 num_examples: 534044 - name: clean num_bytes: 3730243864.91258 num_examples: 532522 download_size: 3671037924 dataset_size: 7471149131.9125805 - config_name: da features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 1298277678 num_examples: 295347 - name: clean num_bytes: 1292602738.074089 num_examples: 294056 download_size: 1782396281 dataset_size: 2590880416.074089 - config_name: de features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 23086869184 num_examples: 2845308 - name: clean num_bytes: 23073148386.18474 num_examples: 2843617 download_size: 21942020975 dataset_size: 46160017570.18474 - config_name: el features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 3002968703 num_examples: 226834 - name: clean num_bytes: 2973684879.714972 num_examples: 224622 download_size: 2295250961 dataset_size: 5976653582.714972 - config_name: en features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 49746869820 num_examples: 6407814 - name: clean num_bytes: 49560903666.851944 num_examples: 6383860 download_size: 40592018321 dataset_size: 99307773486.85194 - config_name: es features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 14759846818 num_examples: 1841155 - name: clean num_bytes: 14536992695.618353 num_examples: 1813356 download_size: 12175892555 dataset_size: 29296839513.618355 - config_name: et features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 1019050491 num_examples: 240397 - name: clean num_bytes: 1016723262.6254404 num_examples: 239848 download_size: 1019164563 dataset_size: 2035773753.6254404 - config_name: eu features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 1291195010 num_examples: 416347 - name: clean num_bytes: 1265327506.262949 num_examples: 408006 download_size: 968840915 dataset_size: 2556522516.262949 - config_name: fa features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 4224898253 num_examples: 979869 - name: clean num_bytes: 4213433450.6083264 num_examples: 977210 download_size: 2499698548 dataset_size: 8438331703.608326 - config_name: fi features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 2600737260 num_examples: 561598 - name: clean num_bytes: 2595874753.1481237 num_examples: 560548 download_size: 2642007766 dataset_size: 5196612013.148124 - config_name: fr features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 20069732840 num_examples: 2564646 - name: clean num_bytes: 19942544382.860683 num_examples: 2548393 download_size: 16151551755 dataset_size: 40012277222.86069 - config_name: ga features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 142209710 num_examples: 59156 - name: clean num_bytes: 141702470.68682805 num_examples: 58945 download_size: 121745838 dataset_size: 283912180.686828 - config_name: he features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 4776226234 num_examples: 333874 - name: clean num_bytes: 4760232712.702708 num_examples: 332756 download_size: 3499530576 dataset_size: 9536458946.70271 - config_name: hi features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 1452853579 num_examples: 163093 - name: clean num_bytes: 1443152625.8779714 num_examples: 162004 download_size: 785363639 dataset_size: 2896006204.8779716 - config_name: hr features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 1064630680 num_examples: 202848 - name: clean num_bytes: 1053026432.3195693 num_examples: 200637 download_size: 1028743775 dataset_size: 2117657112.3195693 - config_name: hu features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 3533169653 num_examples: 532427 - name: clean num_bytes: 3510335279.8822336 num_examples: 528986 download_size: 3558613373 dataset_size: 7043504932.882234 - config_name: hy features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 2568868378 num_examples: 303036 - name: clean num_bytes: 2555898405.394963 num_examples: 301506 download_size: 1781142597 dataset_size: 5124766783.394962 - config_name: id features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 2650288629 num_examples: 665622 - name: clean num_bytes: 2630666948.280745 num_examples: 660694 download_size: 2040186206 dataset_size: 5280955577.280745 - config_name: it features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 12188918391 num_examples: 1833639 - name: clean num_bytes: 12163279397.591763 num_examples: 1829782 download_size: 10368836428 dataset_size: 24352197788.591763 - config_name: ja features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 18752888787 num_examples: 1389467 - name: clean num_bytes: 18684866617.717476 num_examples: 1384427 download_size: 15232900753 dataset_size: 37437755404.717476 - config_name: ko features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 3160932689 num_examples: 647897 - name: clean num_bytes: 3151741108.878351 num_examples: 646013 download_size: 3074385022 dataset_size: 6312673797.878351 - config_name: lt features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 781319902 num_examples: 211292 - name: clean num_bytes: 777474168.616436 num_examples: 210252 download_size: 722780874 dataset_size: 1558794070.616436 - config_name: lv features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 531956241 num_examples: 123413 - name: clean num_bytes: 530943303.00615007 num_examples: 123178 download_size: 700342420 dataset_size: 1062899544.00615 - config_name: mr features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 547060763 num_examples: 94133 - name: clean num_bytes: 545450957.3914355 num_examples: 93856 download_size: 278141890 dataset_size: 1092511720.3914356 - config_name: nl features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 6191062892 num_examples: 2135977 - name: clean num_bytes: 6177393712.697661 num_examples: 2131261 download_size: 5179824678 dataset_size: 12368456604.697662 - config_name: 'no' features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 2437191515 num_examples: 617937 - name: clean num_bytes: 2428893175.610127 num_examples: 615833 download_size: 2175299531 dataset_size: 4866084690.6101265 - config_name: pl features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 6864626419 num_examples: 1587721 - name: clean num_bytes: 6861024883.335341 num_examples: 1586888 download_size: 6565864124 dataset_size: 13725651302.335342 - config_name: pt features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 6844185526 num_examples: 1112246 - name: clean num_bytes: 6755821527.2502985 num_examples: 1097886 download_size: 5516209748 dataset_size: 13600007053.250298 - config_name: ro features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 2023493174 num_examples: 442389 - name: clean num_bytes: 2006866635.6197736 num_examples: 438754 download_size: 1652633599 dataset_size: 4030359809.619774 - config_name: ru features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 22550679128 num_examples: 1945063 - name: clean num_bytes: 22439204702.844765 num_examples: 1935448 download_size: 18884603758 dataset_size: 44989883830.844765 - config_name: sa features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 146460109 num_examples: 12156 - name: clean num_bytes: 145435996.68797302 num_examples: 12071 download_size: 95836795 dataset_size: 291896105.687973 - config_name: sk features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 977962245 num_examples: 242235 - name: clean num_bytes: 976048590.4738994 num_examples: 241761 download_size: 1346611201 dataset_size: 1954010835.4738994 - config_name: sl features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 1106532891 num_examples: 183006 - name: clean num_bytes: 1097995332.4385757 num_examples: 181594 download_size: 1006028852 dataset_size: 2204528223.4385757 - config_name: sr features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 3755288114 num_examples: 676605 - name: clean num_bytes: 3735557179.0449376 num_examples: 673050 download_size: 2558022832 dataset_size: 7490845293.044937 - config_name: sv features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 4861956987 num_examples: 2574513 - name: clean num_bytes: 4857071448.365948 num_examples: 2571926 download_size: 3512612936 dataset_size: 9719028435.365948 - config_name: ta features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 1693909025 num_examples: 160651 - name: clean num_bytes: 1682405487.85255 num_examples: 159560 download_size: 985318775 dataset_size: 3376314512.85255 - config_name: te features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 1556095028 num_examples: 87854 - name: clean num_bytes: 1550320823.3066678 num_examples: 87528 download_size: 746686495 dataset_size: 3106415851.306668 - config_name: tr features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 2317236022 num_examples: 534988 - name: clean num_bytes: 2301578085.336879 num_examples: 531373 download_size: 2055444454 dataset_size: 4618814107.336879 - config_name: uk features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 10935662610 num_examples: 1294720 - name: clean num_bytes: 10860532296.947023 num_examples: 1285825 download_size: 8344390939 dataset_size: 21796194906.94702 - config_name: ur features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 918249794 num_examples: 200154 - name: clean num_bytes: 912616078.225986 num_examples: 198926 download_size: 534834968 dataset_size: 1830865872.225986 - config_name: vi features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 3685585608 num_examples: 1288680 - name: clean num_bytes: 3669872935.086358 num_examples: 1283186 download_size: 2646807342 dataset_size: 7355458543.086358 - config_name: zh features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: split_text sequence: string splits: - name: train num_bytes: 7820979602 num_examples: 1384748 - name: clean num_bytes: 7781957954.689285 num_examples: 1377839 download_size: 6540517932 dataset_size: 15602937556.689285 configs: - config_name: af data_files: - split: train path: af/train-* - split: clean path: af/clean-* - config_name: ar data_files: - split: train path: ar/train-* - split: clean path: ar/clean-* - config_name: bg data_files: - split: train path: bg/train-* - split: clean path: bg/clean-* - config_name: ca data_files: - split: train path: ca/train-* - split: clean path: ca/clean-* - config_name: cs data_files: - split: train path: cs/train-* - split: clean path: cs/clean-* - config_name: da data_files: - split: train path: da/train-* - split: clean path: da/clean-* - config_name: de data_files: - split: train path: de/train-* - split: clean path: de/clean-* - config_name: el data_files: - split: train path: el/train-* - split: clean path: el/clean-* - config_name: en data_files: - split: train path: en/train-* - split: clean path: en/clean-* - config_name: es data_files: - split: train path: es/train-* - split: clean path: es/clean-* - config_name: et data_files: - split: train path: et/train-* - split: clean path: et/clean-* - config_name: eu data_files: - split: train path: eu/train-* - split: clean path: eu/clean-* - config_name: fa data_files: - split: train path: fa/train-* - split: clean path: fa/clean-* - config_name: fi data_files: - split: train path: fi/train-* - split: clean path: fi/clean-* - config_name: fr data_files: - split: train path: fr/train-* - split: clean path: fr/clean-* - config_name: ga data_files: - split: train path: ga/train-* - split: clean path: ga/clean-* - config_name: he data_files: - split: train path: he/train-* - split: clean path: he/clean-* - config_name: hi data_files: - split: train path: hi/train-* - split: clean path: hi/clean-* - config_name: hr data_files: - split: train path: hr/train-* - split: clean path: hr/clean-* - config_name: hu data_files: - split: train path: hu/train-* - split: clean path: hu/clean-* - config_name: hy data_files: - split: train path: hy/train-* - split: clean path: hy/clean-* - config_name: id data_files: - split: train path: id/train-* - split: clean path: id/clean-* - config_name: it data_files: - split: train path: it/train-* - split: clean path: it/clean-* - config_name: ja data_files: - split: train path: ja/train-* - split: clean path: ja/clean-* - config_name: ko data_files: - split: train path: ko/train-* - split: clean path: ko/clean-* - config_name: lt data_files: - split: train path: lt/train-* - split: clean path: lt/clean-* - config_name: lv data_files: - split: train path: lv/train-* - split: clean path: lv/clean-* - config_name: mr data_files: - split: train path: mr/train-* - split: clean path: mr/clean-* - config_name: nl data_files: - split: train path: nl/train-* - split: clean path: nl/clean-* - config_name: 'no' data_files: - split: train path: no/train-* - split: clean path: no/clean-* - config_name: pl data_files: - split: train path: pl/train-* - split: clean path: pl/clean-* - config_name: pt data_files: - split: train path: pt/train-* - split: clean path: pt/clean-* - config_name: ro data_files: - split: train path: ro/train-* - split: clean path: ro/clean-* - config_name: ru data_files: - split: train path: ru/train-* - split: clean path: ru/clean-* - config_name: sa data_files: - split: train path: sa/train-* - split: clean path: sa/clean-* - config_name: sk data_files: - split: train path: sk/train-* - split: clean path: sk/clean-* - config_name: sl data_files: - split: train path: sl/train-* - split: clean path: sl/clean-* - config_name: sr data_files: - split: train path: sr/train-* - split: clean path: sr/clean-* - config_name: sv data_files: - split: train path: sv/train-* - split: clean path: sv/clean-* - config_name: ta data_files: - split: train path: ta/train-* - split: clean path: ta/clean-* - config_name: te data_files: - split: train path: te/train-* - split: clean path: te/clean-* - config_name: tr data_files: - split: train path: tr/train-* - split: clean path: tr/clean-* - config_name: uk data_files: - split: train path: uk/train-* - split: clean path: uk/clean-* - config_name: ur data_files: - split: train path: ur/train-* - split: clean path: ur/clean-* - config_name: vi data_files: - split: train path: vi/train-* - split: clean path: vi/clean-* - config_name: zh data_files: - split: train path: zh/train-* - split: clean path: zh/clean-* --- # Multilingual Tokenizer Benchmark This dataset includes pre-processed wikipedia data for tokenizer evaluation in [45 languages](https://huggingface.co/datasets/occiglot/tokenizer-wiki-bench/blob/main/README.md#supported-languages). We provide more information on the evaluation task in general [this blogpost](https://occiglot.github.io/occiglot/posts/eu_tokenizer_perfomance/). ## Usage The dataset allows us to easily calculate *tokenizer fertility* and the *proportion of continued words* on any of the supported languages. In the example below we take the Mistral tokenizer and evaluate its performance on Slovak. ```python from transformers import AutoTokenizer from datasets import load_dataset import numpy as np def calculate_metrics(tokens): tmp = np.array([len(y) for y in tokens]) return {'fertility': np.mean(tmp), 'cont_prop': np.count_nonzero(tmp > 1) / tmp.shape[0]} tokenizer_name = 'mistralai/Mistral-7B-v0.1' language = 'sk' #Slovak tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) ds = load_dataset('occiglot/tokenizer-wiki-bench', name=language, split='clean') remove_columns = list(set(ds.column_names) - set(["text"])) ds = ds.map(lambda x: {'tokens': tokenizer(x['split_text'], add_special_tokens=False)['input_ids']} ,num_proc=256, remove_columns=remove_columns, batched=False) remove_columns = None#list(set(ds.column_names)) ds = ds.map(lambda x: calculate_metrics(x['tokens']), num_proc=256, remove_columns=remove_columns, batched=False) df = ds.to_pandas() print('Fertility: ', df.fertility.mean()) print('Prop. continued words:', df.cont_prop.mean()) ``` ## Dataset Creation We loosely follow the approach of [Rust _et al.](https://arxiv.org/abs/2012.15613) using the fast [UDPipe](https://ufal.mff.cuni.cz/udpipe) to pre-split documents into words and subsequently run the tokenizer over isolated words. For all languages we use the respective November 2023 snapshot from [Wikipedia](wikimedia/wikipedia). Since Wikipedia, by nature, contains significantly more numbers and dates than other text and most tokenizers split those into single digits, we filtered all lone-standing numbers from the documents. Additionally, we removed any documents that still contained non-parsed HTML code (less than 1%). ## Licensing We release our curated benchmark and any associated code under [MIT](https://opensource.org/license/mit) license. However, depending on your use case, the licensing conditions of the original [Wikipedia data](https://huggingface.co/datasets/wikimedia/wikipedia#licensing-information) and [UDPipe](https://github.com/ufal/udpipe/tree/udpipe-2?tab=License-1-ov-file) may apply. ## Supported Languages This dataset currently contains pre-processed data for the following languages: | Language | Code | |:-----------|:-------| | Afrikaans | af | | Arabic | ar | | Armenian | hy | | Basque | eu | | Bulgarian | bg | | Catalan | ca | | Croatian | hr | | Czech | cs | | Danish | da | | Dutch | nl | | English | en | | Estonian | et | | Finnish | fi | | French | fr | | German | de | | Greek | el | | Hebrew | he | | Hindi | hi | | Hungarian | hu | | Indonesian | id | | Irish | ga | | Italian | it | | Japanese | ja | | Korean | ko | | Latvian | lv | | Lithuanian | lt | | Marathi | mr | | Norwegian | no | | Persian | fa | | Polish | pl | | Portuguese | pt | | Romanian | ro | | Russian | ru | | Sanskrit | sa | | Serbian | sr | | Slovak | sk | | Slovenian | sl | | Spanish | es | | Swedish | sv | | Tamil | ta | | Telugu | te | | Turkish | tr | | Ukrainian | uk | | Urdu | ur | | Vietnamese | vi |

xinrongzhang2022/InfiniteBench

xinrongzhang2022

--- configs: - config_name: default data_files: - split: passkey path: "passkey.jsonl" - split: kv_retrieval path: "kv_retrieval.jsonl" - split: number_string path: "number_string.jsonl" - split: code_run path: "code_run.jsonl" - split: code_debug path: "code_debug.jsonl" - split: math_find path: "math_find.jsonl" - split: math_calc path: "math_calc.jsonl" - split: longdialogue_qa_eng path: "longdialogue_qa_eng.jsonl" - split: longbook_qa_eng path: "longbook_qa_eng.jsonl" - split: longbook_sum_eng path: "longbook_sum_eng.jsonl" - split: longbook_choice_eng path: "longbook_choice_eng.jsonl" - split: longbook_qa_chn path: "longbook_qa_chn.jsonl" --- --- license: apache-2.0 --- --- ## Usage load with datasets ``` from datasets import load_dataset, Features, Value, Sequence # Define the features schema ft = Features({ "id": Value("int64"), "context": Value("string"), "input": Value("string"), "answer": Sequence(Value("string")), "options": Sequence(Value("string")) }) # Load the dataset with the specified features dataset = load_dataset("xinrongzhang2022/InfiniteBench", features=ft) ``` ## Citation Please cite us if you use $\infty$Bench. ```bibtex @inproceedings{zhang-etal-2024-bench, title = "$\infty${B}ench: Extending Long Context Evaluation Beyond 100{K} Tokens", author = "Zhang, Xinrong and Chen, Yingfa and Hu, Shengding and Xu, Zihang and Chen, Junhao and Hao, Moo and Han, Xu and Thai, Zhen and Wang, Shuo and Liu, Zhiyuan and Sun, Maosong", editor = "Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek", booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = aug, year = "2024", address = "Bangkok, Thailand", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.acl-long.814", pages = "15262--15277", abstract = "Processing and reasoning over long contexts is crucial for many practical applications of Large Language Models (LLMs), such as document comprehension and agent construction. Despite recent strides in making LLMs process contexts with more than 100K tokens, there is currently a lack of a standardized benchmark to evaluate this long-context capability. Existing public benchmarks typically focus on contexts around 10K tokens, limiting the assessment and comparison of LLMs in processing longer contexts. In this paper, we propose , the first LLM benchmark featuring an average data length surpassing 100K tokens. comprises synthetic and realistic tasks spanning diverse domains in English and Chinese. The tasks in are designed to require an understanding of long dependencies in contexts and make simply retrieving a limited number of passages from contexts not sufficient for these tasks. Based on , we evaluate several state-of-the-art LLMs tailored for processing long contexts. The experimental results indicate that existing long-context LLMs still require significant advancements to process 100K+ contexts effectively. Furthermore, we present three intriguing analyses regarding the behavior of LLMs processing long context. Our code and data is released.", }

facebook/multilingual_librispeech

facebook

--- annotations_creators: - expert-generated language_creators: - crowdsourced - expert-generated language: - de - nl - fr - it - es - pt - pl - en license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio paperswithcode_id: multilingual-librispeech pretty_name: MultiLingual LibriSpeech dataset_info: - config_name: dutch features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 199959986 num_examples: 3095 - name: test num_bytes: 199298575 num_examples: 3075 - name: train num_bytes: 23931679031 num_examples: 374287 - name: 9_hours num_bytes: 139884664.668 num_examples: 2153 - name: 1_hours num_bytes: 15462181 num_examples: 234 download_size: 24376256629 dataset_size: 24486284437.668 - config_name: french features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 157923970.696 num_examples: 2416 - name: test num_bytes: 158352158.582 num_examples: 2426 - name: train num_bytes: 16984935842.04 num_examples: 258213 - name: 9_hours num_bytes: 142796680.609 num_examples: 2167 - name: 1_hours num_bytes: 15675831 num_examples: 241 download_size: 17381581776 dataset_size: 17459684482.927002 - config_name: german features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 224293581.302 num_examples: 3469 - name: test num_bytes: 225756069.096 num_examples: 3394 - name: train num_bytes: 31050881388 num_examples: 469942 - name: 9_hours num_bytes: 142777983.118 num_examples: 2194 - name: 1_hours num_bytes: 15714704 num_examples: 241 download_size: 31526161821 dataset_size: 31659423725.516 - config_name: italian features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 81607596.048 num_examples: 1248 - name: test num_bytes: 83216752.046 num_examples: 1262 - name: train num_bytes: 3896742625 num_examples: 59623 - name: 9_hours num_bytes: 141671904.428 num_examples: 2173 - name: 1_hours num_bytes: 15560398 num_examples: 240 download_size: 4200633596 dataset_size: 4218799275.522 - config_name: polish features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 32746725 num_examples: 512 - name: test num_bytes: 33735044 num_examples: 520 - name: train num_bytes: 1638889846 num_examples: 25043 - name: 9_hours num_bytes: 142005461 num_examples: 2173 - name: 1_hours num_bytes: 15681216 num_examples: 238 download_size: 1855342312 dataset_size: 1863058292 - config_name: portuguese features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 57533473 num_examples: 826 - name: test num_bytes: 59141979 num_examples: 871 - name: train num_bytes: 2518553713.946 num_examples: 37533 - name: 9_hours num_bytes: 141641902.42 num_examples: 2116 - name: 1_hours num_bytes: 15697139 num_examples: 236 download_size: 2780836500 dataset_size: 2792568207.366 - config_name: spanish features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: chapter_id dtype: string - name: file dtype: string - name: id dtype: string splits: - name: dev num_bytes: 157804903.144 num_examples: 2408 - name: test num_bytes: 158526899.32 num_examples: 2385 - name: train num_bytes: 14562584188 num_examples: 220701 - name: 9_hours num_bytes: 142473624.48 num_examples: 2110 - name: 1_hours num_bytes: 15702048 num_examples: 233 download_size: 14971394533 dataset_size: 15037091662.944 configs: - config_name: dutch data_files: - split: dev path: dutch/dev-* - split: test path: dutch/test-* - split: train path: dutch/train-* - split: 9_hours path: dutch/9_hours-* - split: 1_hours path: dutch/1_hours-* - config_name: french data_files: - split: dev path: french/dev-* - split: test path: french/test-* - split: train path: french/train-* - split: 9_hours path: french/9_hours-* - split: 1_hours path: french/1_hours-* - config_name: german data_files: - split: dev path: german/dev-* - split: test path: german/test-* - split: train path: german/train-* - split: 9_hours path: german/9_hours-* - split: 1_hours path: german/1_hours-* - config_name: italian data_files: - split: dev path: italian/dev-* - split: test path: italian/test-* - split: train path: italian/train-* - split: 9_hours path: italian/9_hours-* - split: 1_hours path: italian/1_hours-* - config_name: polish data_files: - split: dev path: polish/dev-* - split: test path: polish/test-* - split: train path: polish/train-* - split: 9_hours path: polish/9_hours-* - split: 1_hours path: polish/1_hours-* - config_name: portuguese data_files: - split: dev path: portuguese/dev-* - split: test path: portuguese/test-* - split: train path: portuguese/train-* - split: 9_hours path: portuguese/9_hours-* - split: 1_hours path: portuguese/1_hours-* - config_name: spanish data_files: - split: dev path: spanish/dev-* - split: test path: spanish/test-* - split: train path: spanish/train-* - split: 9_hours path: spanish/9_hours-* - split: 1_hours path: spanish/1_hours-* --- # Dataset Card for MultiLingual LibriSpeech ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94) - **Repository:** [Needs More Information] - **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411) - **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer) ### Dataset Summary This is a streamable version of the Multilingual LibriSpeech (MLS) dataset. The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. It includes about 44.5K hours of English and a total of about 6K hours for other languages. ### Supported Tasks and Leaderboards - `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER. - `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS). ### Languages The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish ### How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German): ```python from datasets import load_dataset mls = load_dataset("facebook/multilingual_librispeech", "german", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True) print(next(iter(mls))) ``` *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). Local: ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler mls = load_dataset("facebook/multilingual_librispeech", "german", split="train") batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False) dataloader = DataLoader(mls, batch_sampler=batch_sampler) ``` Streaming: ```python from datasets import load_dataset from torch.utils.data import DataLoader mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True) dataloader = DataLoader(mls, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided. ``` {'file': '10900_6473_000030.flac', 'audio': {'path': '10900_6473_000030.flac', 'array': array([-1.52587891e-04, 6.10351562e-05, 0.00000000e+00, ..., 4.27246094e-04, 5.49316406e-04, 4.57763672e-04]), 'sampling_rate': 16000}, 'text': 'więc czego chcecie odemnie spytałem wysłuchawszy tego zadziwiającego opowiadania broń nas stary człowieku broń zakrzyknęli równocześnie obaj posłowie\n', 'speaker_id': 10900, 'chapter_id': 6473, 'id': '10900_6473_000030'} ``` ### Data Fields - file: A filename .flac format. - audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. ### Data Splits | Number of samples | Train | Train.9h | Train.1h | Dev | Test | | ----- | ------ | ----- | ---- | ---- | ---- | | german | 469942 | 2194 | 241 | 3469 | 3394 | | dutch | 374287 | 2153 | 234 | 3095 | 3075 | | french | 258213 | 2167 | 241 | 2416 | 2426 | | spanish | 220701 | 2110 | 233 | 2408 | 2385 | | italian | 59623 | 2173 | 240 | 1248 | 1262 | | portuguese | 37533 | 2116 | 236 | 826 | 871 | | polish | 25043 | 2173 | 238 | 512 | 520 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode)) ### Citation Information ``` @article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ``` ### Data Statistics | Duration (h) | Train | Dev | Test | |--------------|-----------|-------|-------| | English | 44,659.74 | 15.75 | 15.55 | | German | 1,966.51 | 14.28 | 14.29 | | Dutch | 1,554.24 | 12.76 | 12.76 | | French | 1,076.58 | 10.07 | 10.07 | | Spanish | 917.68 | 9.99 | 10 | | Italian | 247.38 | 5.18 | 5.27 | | Portuguese | 160.96 | 3.64 | 3.74 | | Polish | 103.65 | 2.08 | 2.14 | | # Speakers | Train | | Dev | | Test | | |------------|-------|------|-----|----|------|----| | Gender | M | F | M | F | M | F | | English | 2742 | 2748 | 21 | 21 | 21 | 21 | | German | 81 | 95 | 15 | 15 | 15 | 15 | | Dutch | 9 | 31 | 3 | 3 | 3 | 3 | | French | 62 | 80 | 9 | 9 | 9 | 9 | | Spanish | 36 | 50 | 10 | 10 | 10 | 10 | | Italian | 22 | 43 | 5 | 5 | 5 | 5 | | Portuguese | 26 | 16 | 5 | 5 | 5 | 5 | | Polish | 6 | 5 | 2 | 2 | 2 | 2 | | # Hours / Gender | Dev | | Test | | |------------------|------|------|------|------| | Gender | M | F | M | F | | English | 7.76 | 7.99 | 7.62 | 7.93 | | German | 7.06 | 7.22 | 7 | 7.29 | | Dutch | 6.44 | 6.32 | 6.72 | 6.04 | | French | 5.13 | 4.94 | 5.04 | 5.02 | | Spanish | 4.91 | 5.08 | 4.78 | 5.23 | | Italian | 2.5 | 2.68 | 2.38 | 2.9 | | Portuguese | 1.84 | 1.81 | 1.83 | 1.9 | | Polish | 1.12 | 0.95 | 1.09 | 1.05 | ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.

zhaoyang9425/NoisyLibriSpeechDataset-MUSAN

zhaoyang9425

--- license: afl-3.0 task_categories: - noisy_speech_recognition language: - en tags: - read book pretty_name: NoisyLibriSpeech_MUSAN --- # Dataset Card for the Noisy LibriSpeech dataset ## Dataset Description - **Homepage:** Coming Soon - **Repository:** https://huggingface.co/datasets/zhaoyang9425/NoisyLibriSpeechDataset-MUSAN - **Paper:** Coming Soon =- **Point of Contact:** [email protected] ### Dataset Summary The noisy speech corpus is constructed by randomly sampling noise clips from the MUSAN noise dataset and adding them to LibriSpeech dataset. The Signal-to-Noise Ratio (SNR) levels are sampled from a uniform distribution in 0 dB, 5 dB, 10 dB, 15 dB, and 20 dB. ## Dataset Structure same structure with LibriSpeech dataset

notable12/AICamp-2023-Skin-Conditions-Dataset

notable12

--- license: mit ---

Anonymous-Uploader1/DUET

Anonymous-Uploader1

--- language: - en --- # Dataset Overview &nbsp;&nbsp;&nbsp;&nbsp;This repository introduces a multi-modal dataset, **Dyadic User Engagement dataseT (DUET)**, which contains 12 two-person&mdash;or dyadic&mdash;activities. Theses activities are adopted from **The Repertoire of Nonverbal Behavior: Categories, Origins, Usage, and Coding** by Paul Ekman et al, which allows us to distill the semantics embedded in bodily movements. Besides increasing the number, diversity, and quality of dyadic datasets, contextualizing human activities has been proven to improve the performance of human activity recognition (HAR) tasks, as well as benefit downstream applications, such as autonomous vehicles, smart homes, healthcare, and many more. The dataset is collected via Microsoft Azure Kinect v2 and constitutes of **14,400** samples, all of which come with 4 modalities: **RGB**, **depth**, **infrared (IR)**, and **3D skeleton joints**. The following sections detail the folder structure used to categorize our data, sample frames, and the specifications of Microsoft Azure Kinect v2. # Data Collection and Management ### Data modalities and data format &nbsp;&nbsp;&nbsp;&nbsp; For the data collection, we use the high-quality and multimodal Azure Kinect, equipped with an RGB camera, a depth sensor, and an IR sensor. These sensors all operate at 30 frames per second (FPS) for three seconds for each video sample, yielding 91 frames per sample. The specification of each data format varies depending on the conventions commonly used in the research community: each RGB frame is captured with a resolution of **1,920x1,080** and is stored in a **.jpeg** format. We record depth and IR sequences with a resolution of **640x576** and store them as 24-bit **.png** files. The skeleton joints of every sample video are stored in their corresponding **.csv** files. Each file contains a **91x193** array, where each row represents a frame, and each column holds information related to that frame. The first column records the timestamp of the frame, and the following 96 columns capture the <em>x, <em>y, and <em>z coordinates of 32 joints of one subject (as illustrated in Figure 1), measured as the distance (in millimeters) from the joint to the camera. For instance, the first three columns record the <em>x, <em>y, and <em>z values of the first joint. The order of the joints follows the joint index in [Azure Kinect Body Tracking Joints](https://learn.microsoft.com/en-us/previous-versions/azure/kinect-dk/body-joints). The last 96 columns record the 32 joints of the other object. <p align="center" width="100%"> <img width="30%" src="./Figures/kinect_joints_enlarged_text.png"> Figure 1. 32 skeleton joints of a subject extracted using the Azure Kinect software development kit (SDK). </p> ### Data acquisistion arrangement &nbsp;&nbsp;&nbsp;&nbsp;After selecting the Azure Kinect as the multimodal sensing module, a setup for housing the sensor was needed to guarantee consistency throughout the experiment. We built a sensing module, illustrated in Figure 2, that situates the Azure Kinect 84 inches above the ground and tilts it 37&deg; forward to capture the interactions with a full field of view and minimal occlusions. <p align="center" width="100%"> <img width="33%" src="./Figures/testbed_configurations.png"> Figure 2. On the left, we have the bird's-eye view of the testbed configuration, whereas on the right is the sensing module used across the experiment. </p> &nbsp;&nbsp;&nbsp;&nbsp;Another important aspect of the experiment is the testbeds. Three locations across a US university campus are selected to carry out the experiment. As shown in Figure 3, these include an open indoor space, a confined indoor space, and an outdoor space. These three locations are chosen (1) to enrich the variety of backgrounds and (2) investigate the effects the ambient environment imposes on the sensors. One constraint of HAR datasets is the scarcity of diverse backgrounds, which can lead to overfitting to background noise for deep learning models. The experiment is carried out at three distinct locations to improve the generalizability of background noise. We also recognize that a contextualizable dataset should be suitable for a wide range of environments (e.g., parks, schools, nursing facilities, smart homes). Collecting our dataset at different locations&ndash;especially outdoors&ndash;encourages the exploration of the direct and indirect effects the ambient environment imposes on the sensors and algorithms. <p align="center" width="100%"> <img width="80%" src="./Figures/locations.png"> Figure 3. Data collection locations include, starting from the left to right, an open indoor space, a confined indoor space, and an open outdoor space. </p> &nbsp;&nbsp;&nbsp;&nbsp;Since the experiment is carried out at three locations, there is a need to ensure the collection process is repeatable. Towards this end, we designed a testbed arrangement, shown in Figure 2, that was used across all three environments. In the testbed, volunteers are asked to perform each interaction for 40 repetitions in a rectangular area taped to the ground. After each repetition, a beep would sound, instructing the subjects to rotate either clockwise or counterclockwise and proceed to the next repetition. This novel technique collects data on the interactions from a wide array of perspectives with respect to the camera, diversifying the way interactions are captured and ameliorating the perspective invariance quality of deep learning algorithms. ### Subjects &nbsp;&nbsp;&nbsp;&nbsp;A total of 15 male and eight female subjects participated in the experiments. The subjects were randomly paired to perform actions across the three locations. The subjects' ages range from 23 to 42 years old with a mean of 27 years old and standard deviation of 4.01 years. The subjects' heights range from 165.1cm to 185.4cm with a mean of 172.7cm and standard deviation of 8.46cm. The subjects' weights range from 55kg to 93kg with a mean of 69kg and standard deviation of 10.1kg. ### Folder structure &nbsp;&nbsp;&nbsp;&nbsp;In this repository, we have 14,400 samples that comprise RGB, depth, IR, and 3D skeleton joints, which can be very complicated. To provide simple access for users, we have organized our data into a folder structure, as shown in Figure 5. The folder structure comprises four layers: (1) modality, (2) location combination, interaction label, and subject, (3) timestamps, and (4) image or csv files. Traversing through this structure, we first classify the files based on their modality, including RGB, depth, IR, and 3D skeleton joints. The next layer classifies the location, interaction label, and subject using six-digit codes, *LLIISS*. Here, *LL* stands for the location, which can be *CM* for the indoor open space, *CC* for the indoor confined space, or *CL* for the outdoor space. Next, *II* denotes numbers ranging from 1&ndash;12, where each number corresponds to the enumeration of activities listed in the table below. Last, *SS* identifies the subject pairs ranging from 1&ndash;10. It is worth noting that the same subject pair number in different locations does not represent the same pair. In fact, only *CCII02* and *CLII07*, *CCII01* and *CMII10*, and *CCII03* and *CMII05* share the same subject pairs, respectively. Also, as previously mentioned, we ask each pair of subjects to repeat an interaction for 40 times, all of which are recorded in the same video. To temporally segment each clip, we classify each time window by the start and finish time marks. For example, a folder named 40800222\_43800211 contains a recording starting from 40800222 and ending at 43800211. The clock, which generates the timestamps in milliseconds, begins once the Azure Kinect is connected. Every timestamp folder stores the clip of the corresponding time window, frame by frame, in which all frames are chronologically ordered by numbers ranging from 0&ndash;90. <p align="center" width="100%"> <img width="60%" src="./Figures/folder_structure.png"> Figure 4. The data folder structure for our dataset, which is designed for easy user access. Here, RGB, depth, and IR modalities share an identidcal hierarchy, while 3D skeleton joint folders store all 3D coordinates of a sample clip in a single .csv file. </p> | Label ID | Dyadic interaction | | :--------: | :------- | | 1 | Waving in | | 2 | Thumbs up | | 3 | Waving | | 4 | Painting | | 5 | Showing measurements | | 6 | Nodding | | 7 | Drawing circles in the air | | 8 | Holding palms out | | 9 | Twirling or scratching hair | | 10 | Laughing | | 11 | Arm crossing | | 12 | Hugging | <p align="center" width="100%"> Table 1. Activity labels and their corresponding interactions. </p> ### Sample frames &nbsp;&nbsp;&nbsp;&nbsp;Sample frames are provided in Figure 6 to visualize the differences between different modalities, each of which possess different strengths and weaknesses. RGB frames capture information-rich features like interaction, location, and characteristic features of subjects, which are informative but fail to prioritize user privacy. However, since RGB frames compress the 3D world into a 2D plane, they often suffer from occlusion and variation in perspective. On the other hand, 3D skeleton joints reveal the placement of each joint in the 3D space. The additional dimension gives 3D skeleton joints a desirable perspective-invariant characteristic. Besides the 3D position of each joint, no further information indicative of the subject is conspicuous, prioritizing the preservation of privacy. This feature is preferred by human-centered applications, such as smart homes, CPSIS, and elder care management. Overall, the juxtaposition of different modalities exemplifies the inversely proportional relationship between privacy and value of information---the more information a modality carries, the less user privacy it typically protects. We provide four modalities in our dataset that span this full spectrum to encourage both the exploration of a single modality and the fusion of multiple modalities to strike a balance between privacy preservation and value of information. <p align="center" width="100%"> <img width="80%" src="./Figures/example_frames.png"> Figure 5. Sample data of 12 interactions. Modalities presented are, from top row to bottom row: RGB, IR, depth, and 3D skeleton joints. The 12 interactions are, from left to right: waving in, thumbs up, waving, pointing, showing measurements, nodding, drawing circles in the air, holding palms out, twirling or scratching hair, laughing, arm crossing, and hugging. </p> ### Cross-location and cross-subject evaluations One of the motivations for creating DUET is to encourage the research community to study HAR in the context of dyadic, contextualizable interactions. Hence, there is a need to provide a baseline training and test data split for algorithms to evaluate their performance. In addition to the basic cross-subject evaluation, we include a cross-location evaluation. We recognize that applications leveraging dyadic, contextualizable interactions might occur in various locations, both indoor and outdoors. Therefore, we include cross-location evaluation for HAR algorithm training to ensure resilience to location variation. For the cross-subject evaluation, we use **CCII05**, **CCII07**, **CLII01**, **CLII05**, **CMII06**, and **CMII09** for the test data, and the remainder for the training data. For cross-location evaluation, **CCIISS** is selected as the test data, while **CLIISS** and **CMIISS** are used as the training data.

BramVanroy/wikipedia_culturax_dutch

BramVanroy

--- language: - nl size_categories: - 10B<n<100B task_categories: - text-generation - text2text-generation pretty_name: Filtered CulturaX + Wikipedia for Dutch dataset_info: - config_name: 100M features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 738455828.5851797 num_examples: 1018200 - name: test num_bytes: 7458534.414820259 num_examples: 10284 download_size: 411183119 dataset_size: 745914363.0 - config_name: 100k features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 745955.3074739829 num_examples: 1047 - name: test num_bytes: 7124.692526017029 num_examples: 10 download_size: 366788 dataset_size: 753080.0 - config_name: 10B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 66539945646.34457 num_examples: 40176566 - name: test num_bytes: 105996030.65543362 num_examples: 64000 download_size: 42132184504 dataset_size: 66645941677.0 - config_name: 10M features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 76734151.72157606 num_examples: 139851 - name: test num_bytes: 774743.2784239326 num_examples: 1412 download_size: 37995388 dataset_size: 77508895.0 - config_name: 10k features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 72048.30379746835 num_examples: 78 - name: test num_bytes: 5896 num_examples: 1 download_size: 47197 dataset_size: 77944.30379746835 - config_name: 15B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 99730049355.25276 num_examples: 59584123 - name: test num_bytes: 107121206.74724333 num_examples: 64000 download_size: 63139415312 dataset_size: 99837170562.0 - config_name: 1B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 6797502496.392602 num_examples: 5102360 - name: test num_bytes: 68660322.60739774 num_examples: 51538 download_size: 4260450464 dataset_size: 6866162819.0 - config_name: 1M features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 7442665.619329753 num_examples: 10694 - name: test num_bytes: 75164.38067024625 num_examples: 108 download_size: 3845466 dataset_size: 7517830.0 - config_name: 20B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 132920704365.75093 num_examples: 78991679 - name: test num_bytes: 107693939.24907027 num_examples: 64000 download_size: 84141456153 dataset_size: 133028398305.0 - config_name: 25B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 166111586295.01904 num_examples: 98399236 - name: test num_bytes: 108040894.98094498 num_examples: 64000 download_size: 105147418131 dataset_size: 166219627190.0 - config_name: 30B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 199302582477.5805 num_examples: 117806793 - name: test num_bytes: 108273597.41950662 num_examples: 64000 download_size: 126152714564 dataset_size: 199410856075.0 - config_name: 35B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 232493644456.181 num_examples: 137214350 - name: test num_bytes: 108440503.81899258 num_examples: 64000 download_size: 147149925109 dataset_size: 232602084960.0 - config_name: 40B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 265684747781.7734 num_examples: 156621907 - name: test num_bytes: 108566063.22660531 num_examples: 64000 download_size: 168152290262 dataset_size: 265793313845.0 - config_name: 45B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 298875877641.391 num_examples: 176029463 - name: test num_bytes: 108663946.60903454 num_examples: 64000 download_size: 189159571162 dataset_size: 298984541588.0 - config_name: 50B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 332067028077.12775 num_examples: 195437020 - name: test num_bytes: 108742395.87226707 num_examples: 64000 download_size: 210160621183 dataset_size: 332175770473.0 - config_name: 55B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 365258192681.75964 num_examples: 214844577 - name: test num_bytes: 108806676.24034382 num_examples: 64000 download_size: 231164757019 dataset_size: 365366999358.0 - config_name: 5B features: - name: text dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 33351938314.309906 num_examples: 20769009 - name: test num_bytes: 102774477.69009268 num_examples: 64000 download_size: 21119808690 dataset_size: 33454712792.0 configs: - config_name: 100M data_files: - split: train path: 100M/train-* - split: test path: 100M/test-* - config_name: 100k data_files: - split: train path: 100k/train-* - split: test path: 100k/test-* - config_name: 10B data_files: - split: train path: 10B/train-* - split: test path: 10B/test-* - config_name: 10M data_files: - split: train path: 10M/train-* - split: test path: 10M/test-* - config_name: 10k data_files: - split: train path: 10k/train-* - split: test path: 10k/test-* - config_name: 15B data_files: - split: train path: 15B/train-* - split: test path: 15B/test-* - config_name: 1B data_files: - split: train path: 1B/train-* - split: test path: 1B/test-* - config_name: 1M data_files: - split: train path: 1M/train-* - split: test path: 1M/test-* - config_name: 20B data_files: - split: train path: 20B/train-* - split: test path: 20B/test-* - config_name: 25B data_files: - split: train path: 25B/train-* - split: test path: 25B/test-* - config_name: 30B data_files: - split: train path: 30B/train-* - split: test path: 30B/test-* - config_name: 35B data_files: - split: train path: 35B/train-* - split: test path: 35B/test-* - config_name: 40B data_files: - split: train path: 40B/train-* - split: test path: 40B/test-* - config_name: 45B data_files: - split: train path: 45B/train-* - split: test path: 45B/test-* - config_name: 50B data_files: - split: train path: 50B/train-* - split: test path: 50B/test-* - config_name: 55B data_files: - split: train path: 55B/train-* - split: test path: 55B/test-* - config_name: 5B data_files: - split: train path: 5B/train-* - split: test path: 5B/test-* --- # Filtered CulturaX + Wikipedia for Dutch This is a combined and filtered version of [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) and [Wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia), only including Dutch. It is intended for the training of LLMs. Different configs are available based on the number of tokens (see a section below with an overview). This can be useful if you want to know exactly how many tokens you have. Great for using as a streaming dataset, too. Tokens are counted as white-space tokens, so depending on your tokenizer, you'll likely end up with more tokens than indicated here. Every config also has a test set (for validation) of 1% the total size of the dataset, minimally 1 max. 64k samples (~16M tokens). Wikipedia and CulturaX were shuffled before merging and the test set creation was also shuffled. Priority is given to Wikipedia to prioritize knowledge and cultural content, so the smaller configs will consist exclusively of Wikipedia and for the larger configs we augment with CulturaX. Every config builds further on the previous, so this means that every config contains the same data as the smaller ones and more HOWEVER their train/test splits are not the same, so test set of one config may overlap with samples for another training set. This is usually not a problem but just be aware that you do not train on one config's training set and test with another config's test set. ## Citation If you use [Fietje](https://huggingface.co/BramVanroy/fietje-2) or the [CulturaX + Wikipedia filtered subset](https://huggingface.co/datasets/BramVanroy/wikipedia_culturax_dutch) in your work, please cite to the following paper: ```bibtex @misc{vanroy2024fietjeopenefficientllm, title={Fietje: An open, efficient LLM for Dutch}, author={Bram Vanroy}, year={2024}, eprint={2412.15450}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.15450}, } ``` ## Configs ### `10k` -- 79 samples -- 10,087 tokens - ratio_wikipedia: 100.00% - total_num_tokens: 10,087 - train_num_tokens: 9,205 - test_num_tokens: 882 - total_num_samples: 79 - train_num_samples: 78 - test_num_samples: 1 ### `100k` -- 1,057 samples -- 100,075 tokens - ratio_wikipedia: 100.00% - total_num_tokens: 100,075 - train_num_tokens: 98,044 - test_num_tokens: 2,031 - total_num_samples: 1,057 - train_num_samples: 1,047 - test_num_samples: 10 ### `1M` -- 10,802 samples -- 1,000,239 tokens - ratio_wikipedia: 100.00% - total_num_tokens: 1,000,239 - train_num_tokens: 991,119 - test_num_tokens: 9,120 - total_num_samples: 10,802 - train_num_samples: 10,694 - test_num_samples: 108 ### `10M` -- 141,263 samples -- 10,000,022 tokens - ratio_wikipedia: 100.00% - total_num_tokens: 10,000,022 - train_num_tokens: 9,874,772 - test_num_tokens: 125,250 - total_num_samples: 141,263 - train_num_samples: 139,851 - test_num_samples: 1,412 ### `100M` -- 1,028,484 samples -- 100,000,047 tokens - ratio_wikipedia: 100.00% - total_num_tokens: 100,000,047 - train_num_tokens: 99,013,372 - test_num_tokens: 986,675 - total_num_samples: 1,028,484 - train_num_samples: 1,018,200 - test_num_samples: 10,284 ### `1B` -- 5,153,898 samples -- 1,000,000,187 tokens - ratio_wikipedia: 61.21% - total_num_tokens: 1,000,000,187 - train_num_tokens: 989,990,190 - test_num_tokens: 10,009,997 - total_num_samples: 5,153,898 - train_num_samples: 5,102,360 - test_num_samples: 51,538 ### `5B` -- 20,833,009 samples -- 5,000,000,076 tokens - ratio_wikipedia: 25.35% - total_num_tokens: 5,000,000,076 - train_num_tokens: 4,984,493,654 - test_num_tokens: 15,506,422 - total_num_samples: 20,833,009 - train_num_samples: 20,769,009 - test_num_samples: 64,000 ### `10B` -- 40,240,566 samples -- 10,000,000,115 tokens - ratio_wikipedia: 18.41% - total_num_tokens: 10,000,000,115 - train_num_tokens: 9,984,156,828 - test_num_tokens: 15,843,287 - total_num_samples: 40,240,566 - train_num_samples: 40,176,566 - test_num_samples: 64,000 ### `15B` -- 59,648,123 samples -- 15,000,000,154 tokens - ratio_wikipedia: 15.98% - total_num_tokens: 15,000,000,154 - train_num_tokens: 14,983,970,518 - test_num_tokens: 16,029,636 - total_num_samples: 59,648,123 - train_num_samples: 59,584,123 - test_num_samples: 64,000 ### `20B` -- 79,055,679 samples -- 20,000,000,009 tokens - ratio_wikipedia: 14.75% - total_num_tokens: 20,000,000,009 - train_num_tokens: 19,983,799,357 - test_num_tokens: 16,200,652 - total_num_samples: 79,055,679 - train_num_samples: 78,991,679 - test_num_samples: 64,000 ### `25B` -- 98,463,236 samples -- 25,000,000,048 tokens - ratio_wikipedia: 14.00% - total_num_tokens: 25,000,000,048 - train_num_tokens: 24,983,765,326 - test_num_tokens: 16,234,722 - total_num_samples: 98,463,236 - train_num_samples: 98,399,236 - test_num_samples: 64,000 ### `30B` -- 117,870,793 samples -- 30,000,000,087 tokens - ratio_wikipedia: 13.50% - total_num_tokens: 30,000,000,087 - train_num_tokens: 29,983,707,932 - test_num_tokens: 16,292,155 - total_num_samples: 117,870,793 - train_num_samples: 117,806,793 - test_num_samples: 64,000 ### `35B` -- 137,278,350 samples -- 35,000,000,126 tokens - ratio_wikipedia: 13.14% - total_num_tokens: 35,000,000,126 - train_num_tokens: 34,983,914,739 - test_num_tokens: 16,085,387 - total_num_samples: 137,278,350 - train_num_samples: 137,214,350 - test_num_samples: 64,000 ### `40B` -- 156,685,907 samples -- 40,000,000,165 tokens - ratio_wikipedia: 12.87% - total_num_tokens: 40,000,000,165 - train_num_tokens: 39,983,508,625 - test_num_tokens: 16,491,540 - total_num_samples: 156,685,907 - train_num_samples: 156,621,907 - test_num_samples: 64,000 ### `45B` -- 176,093,463 samples -- 45,000,000,020 tokens - ratio_wikipedia: 12.66% - total_num_tokens: 45,000,000,020 - train_num_tokens: 44,983,608,118 - test_num_tokens: 16,391,902 - total_num_samples: 176,093,463 - train_num_samples: 176,029,463 - test_num_samples: 64,000 ### `50B` -- 195,501,020 samples -- 50,000,000,059 tokens - ratio_wikipedia: 12.49% - total_num_tokens: 50,000,000,059 - train_num_tokens: 49,983,567,461 - test_num_tokens: 16,432,598 - total_num_samples: 195,501,020 - train_num_samples: 195,437,020 - test_num_samples: 64,000 ### `55B` -- 214,908,577 samples -- 55,000,000,098 tokens - ratio_wikipedia: 12.35% - total_num_tokens: 55,000,000,098 - train_num_tokens: 54,983,723,278 - test_num_tokens: 16,276,820 - total_num_samples: 214,908,577 - train_num_samples: 214,844,577 - test_num_samples: 64,000 ## Filtering While CultruaX already has done a lot of filtering, some more filtering can be done to improve the quality of the corpus. These filters are described below. The baseline ratios (punctuation, uppercase, digits) were calculated on the SONAR-500 corpus (excluding WRPEA WRPED WRUEA WRUED WRUEB). **CulturaX**: - removed documents that contain the text "rechten voorbehouden" or "rights reserved" - remove documents whose URL contained "wikipedia.org" (because we include a cleaned version of Wikipedia ourselves) - removed documents that contain a "bad word" (see the section below) - removed documents that contain any non-latin characters. The idea is that "knowledge"-based information (e.g. original writing of a name) are allowed when the data comes from Wikipedia, but not from any other webcrawl, to avoid unsollicited noise. **CulturaX + Wikipedia**: - removed documents where ratio of punctuation marks vs. non-whitespace characters is higher than 0.2 - removed documents where ratio of uppercase vs. non-whitespace characters is higher than 0.22 - removed documents where ratio of digits vs. non-whitespace characters is higher than 0.16 - removed documents where the average token length is < 2 or > 20 ## Bad words ```python BAD_PHRASES_DOC_LEVEL = { # https://en.wikipedia.org/wiki/Dutch_profanity "achterlijk", "debiel", "downie", "idioot", "kankerlijer", "klere", "kolere", "minkukel", "pestkop", "pleuris", "pleuritis", "teringlijer", "tyfuslijer", "gadver", "getver", "godver", "godskolere", "godverork", "graftak", "kopvod", "verdomme", "anaalgeneraal", "bitch", "dikzak", "flikker", "fok", "fuck", "hoer", "klootzak", "klote", "kreng", "kringspiermusketier", "kut", "lamzak", "lul", "manwijf", "matennaai", "neuken", "neuker", "ouwehoer", "reet", "reetkever", "reetridder", "rotzak", "schijt", "shit", "slet", "slijmbal", "slons", "sodemieter", "stoephoer", "swaffel", "teef", "trut", "tut", "zak", "uilskuiken", "zeik", "bamivreter", "bosneger", "neger", "fransoos", "geitenneuker", "kaaskop", "kakker", "koelie", "lijp", "medelander", "mocro", "mof", "nikker", "poepchinees", "roetmop", "spaghettivreter", "loempiavouwer", "spanjool", "spleetoog", "tatta", "tokkie", "zandneger", "zwartzak", "halvezool", "kenau", "klootviool", "knuppel", "koekert", "koekwaus", "oelewapper", "smeerlap", "sukkel", "sul", "wappie", "wijf", "zooi", # xxx (a.o. https://gitlab.com/yhavinga/c4nlpreproc/-/blob/master/clean/badwords_ennl.py?ref_type=heads) "xxx", "anal", "blowjob", "buttplug", "cock", "cunt", "geil", "sex", # Standaardnederlands = seks, maybe we catch some porn or socialmedia sites with this misspelling "porn", # extra "nigger", "nigga", "hoerig", "klojo", } ``` ## Config details ## License information For CulturaX: https://huggingface.co/datasets/uonlp/CulturaX#license-information For Wikipedia: https://huggingface.co/datasets/wikimedia/wikipedia#licensing-information

DL3DV/DL3DV-Benchmark

DL3DV

--- tags: - 3D vision - novel view synthesis - NeRF - 3D Gaussian Splatting - Generalizable NeRF - Generative Methods - text-to-3d - image-to-3d pretty_name: DL3DV size_categories: - n>1T --- # DL3DV Benchmark Download Instructions This repo contains all the benchmark data, including a README, License, colmaps/images (compatible to nerfstudio and 3D gaussian splatting), scene labels and the performances of methods reported in the paper (ZipNeRF, 3D GS, MipNeRF-360, nerfacto, Instant-NGP). # Download As the whole benchmark dataset is very big (~2.1T), we provide two ways to download: full benchmark dataset download or use a script to download a subset for memory sensitive cases. ## Full benchmark dataset download If you have enough space (more than 2.1T), download the full benchmark is simple: ``` bash # Make sure you have git-lfs installed # (https://git-lfs.github.com/) git lfs install git clone https://huggingface.co/datasets/DL3DV/DL3DV-10K-Benchmark ``` ## Script download Sometimes you may just need to flexibly download a subset the benchmark, e.g. just download several scenes, or just need images with 960P resolution (images_4 level used in the paper). To provide this flexibiliy, we provide a [download.py](https://huggingface.co/datasets/DL3DV/DL3DV-10K-Benchmark/blob/main/download.py) script for use. Use this [link](https://huggingface.co/datasets/DL3DV/DL3DV-10K-Benchmark/resolve/main/download.py?download=true) to download. This download script provies several different options to use: * Download the full dataset (which is equivalent to git clone method). In total 2.1T. * Download the full dataset with only 960P images. In total 100~150G. * Download with specific scene name (hash name) ### Environment Setup The download script relies on `huggingface hub`, `tqdm`, and `pandas`. You can download by the following command in your python environment. The download script was ```bash pip install huggingface_hub tqdm pandas ``` After downloading `huggingface_hub`, remember to login first to get ready for download. ```bash # in terminal, use the following command and your huggingface token to login huggingface-cli login ``` ### Download the full benchmark To download the full dataset, use this command: ``` bash # Note, it is suggested to use --clean_cache flag as it saves space by cleaning the cache folder created by huggingface hub API. python download.py --subset full --clean_cache ``` ### Download the full benchmark with 960P resolution (same with the paper) Not all the methods can handle multi-resolution. Some methods have assumptions on the input resolution. So the paper uses 960P. ``` bash # Note, it is suggested to use --clean_cache flag as it saves space by cleaning the cache folder created by huggingface hub API. python download.py --subset full --only_level4 --clean_cache ``` ### Download with specific scene name (hash name) There is a benchmark preview page in https://github.com/DL3DV-10K/Dataset. If you just need a specific hash (e.g. 0853979305f7ecb80bd8fc2c8df916410d471ef04ed5f1a64e9651baa41d7695), use the following command: ``` bash # Note, it is suggested to use --clean_cache flag as it saves space by cleaning the cache folder created by huggingface hub API. # e.g. a scene with hash 0853979305f7ecb80bd8fc2c8df916410d471ef04ed5f1a64e9651baa41d7695 python download.py --subset hash --hash 0853979305f7ecb80bd8fc2c8df916410d471ef04ed5f1a64e9651baa41d7695 --only_level4 ```

statmt/cc100

statmt

--- annotations_creators: - no-annotation language_creators: - found language: - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - ff - fi - fr - fy - ga - gd - gl - gn - gu - ha - he - hi - hr - ht - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - ns - om - or - pa - pl - ps - pt - qu - rm - ro - ru - sa - sc - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - te - th - tl - tn - tr - ug - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu language_bcp47: - bn-Latn - hi-Latn - my-x-zawgyi - ta-Latn - te-Latn - ur-Latn - zh-Hans - zh-Hant license: - unknown multilinguality: - multilingual size_categories: - 10M<n<100M - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: cc100 pretty_name: CC-100 dataset_info: - config_name: am features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 935440775 num_examples: 3124561 download_size: 138821056 dataset_size: 935440775 - config_name: sr features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 10299427460 num_examples: 35747957 download_size: 1578989320 dataset_size: 10299427460 - config_name: ka features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 10228918845 num_examples: 31708119 download_size: 1100446372 dataset_size: 10228918845 config_names: - am - sr --- # Dataset Card for CC-100 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://data.statmt.org/cc-100/ - **Repository:** [More Information Needed] - **Paper:** https://aclanthology.org/2020.acl-main.747/ - **Paper:** https://aclanthology.org/2020.lrec-1.494/ - **Paper:** https://arxiv.org/abs/1911.02116 - **Paper:** https://arxiv.org/abs/1911.00359 - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary This corpus is an attempt to recreate the dataset used for training XLM-R. This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages (indicated by *_rom). This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. ### Supported Tasks and Leaderboards CC-100 is mainly intended to pretrain language models and word representations. ### Languages The languages in the dataset are: - af: Afrikaans (305M) - am: Amharic (133M) - ar: Arabic (5.4G) - as: Assamese (7.6M) - az: Azerbaijani (1.3G) - be: Belarusian (692M) - bg: Bulgarian (9.3G) - bn: Bengali (860M) - bn_rom: Bengali Romanized (164M) - br: Breton (21M) - bs: Bosnian (18M) - ca: Catalan (2.4G) - cs: Czech (4.4G) - cy: Welsh (179M) - da: Danish (12G) - de: German (18G) - el: Greek (7.4G) - en: English (82G) - eo: Esperanto (250M) - es: Spanish (14G) - et: Estonian (1.7G) - eu: Basque (488M) - fa: Persian (20G) - ff: Fulah (3.1M) - fi: Finnish (15G) - fr: French (14G) - fy: Frisian (38M) - ga: Irish (108M) - gd: Scottish Gaelic (22M) - gl: Galician (708M) - gn: Guarani (1.5M) - gu: Gujarati (242M) - ha: Hausa (61M) - he: Hebrew (6.1G) - hi: Hindi (2.5G) - hi_rom: Hindi Romanized (129M) - hr: Croatian (5.7G) - ht: Haitian (9.1M) - hu: Hungarian (15G) - hy: Armenian (776M) - id: Indonesian (36G) - ig: Igbo (6.6M) - is: Icelandic (779M) - it: Italian (7.8G) - ja: Japanese (15G) - jv: Javanese (37M) - ka: Georgian (1.1G) - kk: Kazakh (889M) - km: Khmer (153M) - kn: Kannada (360M) - ko: Korean (14G) - ku: Kurdish (90M) - ky: Kyrgyz (173M) - la: Latin (609M) - lg: Ganda (7.3M) - li: Limburgish (2.2M) - ln: Lingala (2.3M) - lo: Lao (63M) - lt: Lithuanian (3.4G) - lv: Latvian (2.1G) - mg: Malagasy (29M) - mk: Macedonian (706M) - ml: Malayalam (831M) - mn: Mongolian (397M) - mr: Marathi (334M) - ms: Malay (2.1G) - my: Burmese (46M) - my_zaw: Burmese (Zawgyi) (178M) - ne: Nepali (393M) - nl: Dutch (7.9G) - no: Norwegian (13G) - ns: Northern Sotho (1.8M) - om: Oromo (11M) - or: Oriya (56M) - pa: Punjabi (90M) - pl: Polish (12G) - ps: Pashto (107M) - pt: Portuguese (13G) - qu: Quechua (1.5M) - rm: Romansh (4.8M) - ro: Romanian (16G) - ru: Russian (46G) - sa: Sanskrit (44M) - sc: Sardinian (143K) - sd: Sindhi (67M) - si: Sinhala (452M) - sk: Slovak (6.1G) - sl: Slovenian (2.8G) - so: Somali (78M) - sq: Albanian (1.3G) - sr: Serbian (1.5G) - ss: Swati (86K) - su: Sundanese (15M) - sv: Swedish (21G) - sw: Swahili (332M) - ta: Tamil (1.3G) - ta_rom: Tamil Romanized (68M) - te: Telugu (536M) - te_rom: Telugu Romanized (79M) - th: Thai (8.7G) - tl: Tagalog (701M) - tn: Tswana (8.0M) - tr: Turkish (5.4G) - ug: Uyghur (46M) - uk: Ukrainian (14G) - ur: Urdu (884M) - ur_rom: Urdu Romanized (141M) - uz: Uzbek (155M) - vi: Vietnamese (28G) - wo: Wolof (3.6M) - xh: Xhosa (25M) - yi: Yiddish (51M) - yo: Yoruba (1.1M) - zh-Hans: Chinese (Simplified) (14G) - zh-Hant: Chinese (Traditional) (5.3G) - zu: Zulu (4.3M) ## Dataset Structure ### Data Instances An example from the `am` configuration: ``` {'id': '0', 'text': 'ተለዋዋጭ የግድግዳ አንግል ሙቅ አንቀሳቅሷል ቲ-አሞሌ አጥቅሼ ...\n'} ``` Each data point is a paragraph of text. The paragraphs are presented in the original (unshuffled) order. Documents are separated by a data point consisting of a single newline character. ### Data Fields The data fields are: - id: id of the example - text: content as a string ### Data Splits Sizes of some configurations: | name |train| |----------|----:| |am|3124561| |sr|35747957| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? The data comes from multiple web pages in a large variety of languages. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information Being constructed from Common Crawl, personal and sensitive information might be present. This **must** be considered before training deep learning models with CC-100, specially in the case of text-generation models. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was prepared by [Statistical Machine Translation at the University of Edinburgh](https://www.statmt.org/ued/) using the [CC-Net](https://github.com/facebookresearch/cc_net) toolkit by Facebook Research. ### Licensing Information Statistical Machine Translation at the University of Edinburgh makes no claims of intellectual property on the work of preparation of the corpus. By using this, you are also bound by the [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/) in respect of the content contained in the dataset. ### Citation Information Please cite the following if you found the resources in this corpus useful: ```bibtex @inproceedings{conneau-etal-2020-unsupervised, title = "Unsupervised Cross-lingual Representation Learning at Scale", author = "Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin", editor = "Jurafsky, Dan and Chai, Joyce and Schluter, Natalie and Tetreault, Joel", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.acl-main.747", doi = "10.18653/v1/2020.acl-main.747", pages = "8440--8451", abstract = "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6{\%} average accuracy on XNLI, +13{\%} average F1 score on MLQA, and +2.4{\%} F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7{\%} in XNLI accuracy for Swahili and 11.4{\%} for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.", } ``` ```bibtex @inproceedings{wenzek-etal-2020-ccnet, title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data", author = "Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, Edouard", editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.494", pages = "4003--4012", abstract = "Pre-training text representations have led to significant improvements in many areas of natural language processing. The quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select documents that are close to high quality corpora like Wikipedia.", language = "English", ISBN = "979-10-95546-34-4", } ``` ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

sraimund/MapPool

sraimund

--- license: cc-by-4.0 --- # MapPool - Bubbling up an extremely large corpus of maps for AI <img src="map_bubbles.png" alt="many small air bubbles containing colorful maps arising with light rays under the ocean (AI-generated image)" width="256"/> MapPool is a dataset of 75 million potential maps and textual captions. It has been derived from [CommonPool](https://www.datacomp.ai/), a dataset consisting of 12 billion text-image pairs from the Internet. The images have been encoded by a vision transformer and classified into maps and non-maps by a support vector machine. This approach outperforms previous models and yields a validation accuracy of 98.5%. The MapPool dataset may help to train data-intensive architectures in order to establish vision and language foundation models specialized in maps. The analysis of the dataset and the exploration of the embedding space offers a large potential for future work. ## How is the data structured? | Key | Meaning |----------------------------------|---------- | uid | Unique identifier | url | Link to the image | text | Textual description of the image | original_width / original_height | Dimensions of the image | sha256 | Hash of the image (to verify if the image is the same as the one in the URL) | l14_img | Embedding of the image (768 dimensions) | l14_txt | Embedding of the textual description (768 dimensions) | clip_l14_similarity_score | Similarity between the image and text (higher values indicate higher similarity) ## How can this repository be downloaded? Simply use Git (or TortoiseGit): ``` git clone https://huggingface.co/datasets/sraimund/MapPool/ ``` Alternatively use the HuggingFace API: ```python import json import os from huggingface_hub import hf_hub_download download_folder = "<your-download-folder>" repo_id = "sraimund/MapPool" # this file is given at the root of this repository with open("file_list.json") as f: file_list = json.load(f) for part, files in file_list.items(): for file in files: file_path = f"{download_folder}/{part}/{file}.parquet" if os.path.exists(file_path): continue hf_hub_download(repo_type="dataset", repo_id=repo_id, filename=f"{part}/{file}.parquet", local_dir=download_folder, token=read_token) ``` About 225 GB of space are required. The amount doubles when using Git since the files are duplicated in the .git folder. ## How can the parquet files be read? You can read parquet files with [pandas](https://pandas.pydata.org/): ```python import pandas as pd df = pd.read_parquet("<file_name>.parquet") ``` The pyarrow or fastparquet library is required additionally. ## How can the map images be downloaded? You can download the map images with [img2dataset](https://github.com/rom1504/img2dataset). ```python from img2dataset import download download( thread_count=64, url_list="<file_name>.parquet", output_folder="<folder_path>", resize_mode="no", output_format="files", input_format="parquet", url_col="url", caption_col="text", verify_hash=("sha256", "sha256"), ) ``` For Windows users: ```python import multiprocessing as mp from img2dataset import download # a small patch is also needed: https://github.com/rom1504/img2dataset/issues/347 def main(): download(...) if __name__ == "__main__": multiprocessing.freeze_support() main() ``` As the Internet is constantly changing, about two thirds of the original images (= 48 million) are still downloadable. 6TB of space are required to store them in their original formats and 100GB of space are needed when creating 128x128px thumbnails in the webm format with 60% quality. Downloading the images took 40 hours with 24 CPUs, 30GB RAM, and 40MB/s of network traffic on average. ## How was this dataset created? MapPool has been created by classifying the image embeddings included in [CommonPool](https://huggingface.co/datasets/mlfoundations/datacomp_xlarge), which have been generated by two pre-trained vision transformers (ViTs). The [L/14 model](https://github.com/mlfoundations/open_clip) with more parameters and outputting 768-dimensional embeddings has been considered since it has achieved higher classification accuracies. In this work, different map classifiers (Table 1) from [scikit-learn](https://scikit-learn.org/) with the [Intel Extension](https://intel.github.io/scikit-learn-intelex) have been trained on the embeddings of 1,860 maps and 1,860 non-maps, and have been evaluated on 1,240 maps and 1,240 non-maps ([Schnürer et al. 2021](https://doi.org/10.1080/00087041.2020.1738112)). Only simple classification models have been considered due to their efficiency and as meaningful embeddings have already been created by the vision transformer. | Model | Accuracy |----------------------------------------------------------|---------- | Xception / InceptionResNetV2 (= Baseline) | 96.7 | ViT-L/14 + L2 distance to averaged embeddings | 96.7 | ViT-L/14 + Logistic Regression | 97.9 | ViT-L/14 + Multilayer Perceptron (3x256 units) | 98.2 | ViT-L/14 + Support Vector Machine (polynomial, degree 3) | 98.5 With the Support Vector Machine, 500,000 image embeddings could be classified within 10 seconds. Downloading, classifying the whole dataset, and uploading the results took about 50 hours with 10 CPUs, 120GB RAM, and 500MB/s of network traffic on average. ## Is the inference model available? Yes, try it out and download it here: [https://huggingface.co/spaces/sraimund/MapPool](https://huggingface.co/spaces/sraimund/MapPool) ## What are the limitations? A qualitative inspection of the detected maps looks promising; however, it is not known what the actual accuracy is. Especially the false negative rate is hard to estimate due to the high number of non-maps among the CommonPool images. Mixtures between natural images and maps (e.g., a map printed on a bag, a map in a park) have not been further examined. Textual embeddings have not been considered in the separation process so far. The training dataset for the map classifier has a large visual variety, such as pictorial maps and 3D maps as well as sketches and paintings. However, the textual descriptions may be too biased since the training dataset originates only from one source. ## What are future research directions? A detailed analysis of the content and metadata of maps in MapPool, potentially resulting in a search engine, is the subject of future work. Additionally, the visual and textual embedding space may be explored to refine the map classifier and to detect duplicates among the images. It can be examined whether training with map-only images leads to better results for cartographic tasks, for instance generating maps based on textual prompts, than with a mixture of maps and other images. Feel free to contact [me](https://schnuerer.dev/contact/) in case you like to collaborate! ## Disclaimer The creator is not responsible for the content of linked external websites and will not guarantee for any damage any content of these websites may cause. ## License The dataset is published under the Creative Commons Attribution 4.0 license. Please respect the copyright of the original images when making use of MapPool. ## Citation A [short paper](https://infoscience.epfl.ch/handle/20.500.14299/240495) is available. ``` @inproceedings{Schnürer_MapPool_2024, title={MapPool - Bubbling up an extremely large corpus of maps for AI}, author={Schnürer, Raimund}, year={2024}, url={https://infoscience.epfl.ch/handle/20.500.14299/240495}} ```

AI-MO/NuminaMath-CoT

AI-MO

--- dataset_info: features: - name: source dtype: string - name: problem dtype: string - name: solution dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 2495457595.0398345 num_examples: 859494 - name: test num_bytes: 290340.31593470514 num_examples: 100 download_size: 1234351634 dataset_size: 2495747935.355769 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* license: apache-2.0 task_categories: - text-generation language: - en tags: - aimo - math pretty_name: NuminaMath CoT --- # Dataset Card for NuminaMath CoT ## Dataset Description - **Homepage:** https://projectnumina.ai - **Repository:** https://github.com/project-numina/aimo-progress-prize - **Paper:** https://github.com/project-numina/aimo-progress-prize/blob/main/report/numina_dataset.pdf - **Leaderboard:** - **Point of Contact:** [Jia Li]([email protected]) ### Dataset Summary Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation into problem-solution pairs, (c) Translation into English, (d) realignment to produce a CoT reasoning format, and (e) final answer formatting. ### Source breakdown | Source | Number of Samples | | --- | --- | | aops_forum | 30201 | | amc_aime | 4072 | | cn_k12 | 276591 | | gsm8k | 7345 | | math | 7478 | | olympiads | 150581 | | orca_math | 153334 | | synthetic_amc | 62111 | | synthetic_math | 167895 | | **Total** | **859608** | ### Licensing Information The dataset is available under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). ### Citation Information ``` @misc{numina_math_datasets, author = {Jia LI and Edward Beeching and Lewis Tunstall and Ben Lipkin and Roman Soletskyi and Shengyi Costa Huang and Kashif Rasul and Longhui Yu and Albert Jiang and Ziju Shen and Zihan Qin and Bin Dong and Li Zhou and Yann Fleureau and Guillaume Lample and Stanislas Polu}, title = {NuminaMath}, year = {2024}, publisher = {Numina}, journal = {Hugging Face repository}, howpublished = {\url{[https://huggingface.co/AI-MO/NuminaMath-CoT](https://github.com/project-numina/aimo-progress-prize/blob/main/report/numina_dataset.pdf)}} } ```

KBlueLeaf/danbooru2023-webp-4Mpixel

KBlueLeaf

--- license: mit task_categories: - image-classification - zero-shot-image-classification - text-to-image language: - en tags: - art - anime - not-for-all-audiences size_categories: - 1M<n<10M --- # Danbooru 2023 webp: A space-efficient version of Danbooru 2023 This dataset is a resized/re-encoded version of [danbooru2023](https://huggingface.co/datasets/nyanko7/danbooru2023).<br> Which removed the non-image/truncated files and resize all of them into smaller size. This dataset already be updated to latest_id = 7,832,883. Thx to DeepGHS! **Notice**: content of updates folder and deepghs/danbooru_newest-webp-4Mpixel have been merged to 2000~2999.tar, You can ignore all the content in updates folder safely! --- ## Details This dataset employs few method to reduce the size and improve the efficiency. ### Size and Format This dataset resize all the image which have more than 2048x2048 pixel into near 2048x2048 pixels with bicubic algorithm.<br> And remove all the image with longer edge larger than 16383 after resize.<br> (one reason is beacuse webp doesn't allow that, another is that aspect ratio is too large/small.) This dataset encode/save all the image with 90% quality webp with pillow library in Python. Which is half size of the 100% quality lossy webp. The total size of this dataset is around 1.3~1.4TB. Which is less than the 20% of original file size. ### Webdataset This dataset use webdataset library to save all the tarfile, therefore, you can also use webdataset to load them easily. This is also a recommended way. The `__key__` of each files is the id of it. You can use this id to query the [metadata database](https://huggingface.co/datasets/KBlueLeaf/danbooru2023-sqlite) easily.

bigcode/commitpackft

bigcode

--- license: mit pretty_name: CommitPackFT language: - code ---  # Dataset Card for CommitPackFT ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigcode-project/octopack - **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124) - **Point of Contact:** [Niklas Muennighoff](mailto:[email protected]) ### Dataset Summary > CommitPackFT is a 2GB filtered version of [CommitPack](https://huggingface.co/datasets/bigcode/commitpack) to contain only high-quality commit messages that resemble natural language instructions. > - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigcode-project/octopack). - **Languages:** 277 - **OctoPack🐙🎒:** <table> <tr> <th>Data</t> <td><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></td> <td>4TB of GitHub commits across 350 programming languages</td> </tr> <tr> <th></t> <td><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></td> <td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td> </tr> <tr> <th>Model</t> <td><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></td> <td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td> </tr> <tr> <th></t> <td><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></td> <td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td> </tr> <tr> <th>Evaluation&nbsp;&nbsp;</t> <td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td> <td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td> </tr> </table> ## Dataset Structure ### Data Instances An example looks as follows: ```json { 'commit': '0c17311f7fd511f5dae8f8e4acc2dce1a2de3cf5', 'old_file': 'main.py', 'new_file': 'main.py', 'old_contents': "import numpy as np\nimport matplotlib.pyplot as plt\n\n# generate sample data\nx_data = np.linspace(-5, 5, 20)\ny_data = np.random.normal(0.0, 1.0, x_data.size)\n\nplt.plot(x_data, y_data, 'o')\nplt.show()\n", 'new_contents': "import math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# generate sample data\nx_data = np.linspace(-math.pi, math.pi, 30)\ny_data = np.sin(x_data) + np.random.normal(0.0, 0.1, x_data.size)\n\nplt.plot(x_data, y_data, 'o')\nplt.show()\n\n", 'subject': 'Change to sin() function with noise', 'message': 'Change to sin() function with noise\n', 'lang': 'Python', 'license': 'mit', 'repos': 'MorganR/basic-gaussian-process' } ``` ### Data Fields The data fields are the same among all splits: - `commit`: unique commit id - `old_file`: name of the file before the commit - `new_file`: name of the file after the commit - `old_contents`: contents of the file before the commit - `new_contents`: contents of the file after the commit - `subject`: subject of the commit (this is used for all experiments in the paper) - `message`: message of the commit (commonly the same as the subject) - `lang`: programming language - `license`: license of the repository the code stems from, one of `['mit', 'artistic-2.0', 'isc', 'cc0-1.0', 'epl-1.0', 'mpl-2.0', 'unlicense', 'unknown', 'apache-2.0', 'bsd-3-clause', 'agpl-3.0', 'lgpl-2.1', 'bsd-2-clause']` - `repos`: name of the the repository the code stems from (if multiple, they are comma separated) ### Data Splits | Name | Megabytes | % of total | Samples | % of total | | --- | --- | --- | --- | --- | | total | 1545.02 | 100.0% | 702062 | 100.0% | | ruby | 195.292 | 12.6401% | 69413 | 9.887% | | yaml | 190.876 | 12.3543% | 114320 | 16.2835% | | python | 132.68 | 8.5876% | 56025 | 7.9801% | | markdown | 131.152 | 8.4887% | 62518 | 8.9049% | | javascript | 125.008 | 8.091% | 52989 | 7.5476% | | json | 86.744 | 5.6144% | 39777 | 5.6657% | | shell | 66.864 | 4.3277% | 31217 | 4.4465% | | text | 66.664 | 4.3148% | 46588 | 6.6359% | | php | 60.22 | 3.8977% | 24791 | 3.5312% | | java | 56.284 | 3.6429% | 20635 | 2.9392% | | html | 48.42 | 3.1339% | 20214 | 2.8792% | | c# | 26.84 | 1.7372% | 9346 | 1.3312% | | xml | 23.676 | 1.5324% | 9337 | 1.3299% | | html+erb | 23.104 | 1.4954% | 10910 | 1.554% | | c | 21.08 | 1.3644% | 8506 | 1.2116% | | ini | 21.04 | 1.3618% | 11360 | 1.6181% | | coffeescript | 16.96 | 1.0977% | 5513 | 0.7853% | | swift | 16.272 | 1.0532% | 4849 | 0.6907% | | restructuredtext | 15.728 | 1.018% | 6560 | 0.9344% | | typescript | 14.284 | 0.9245% | 5868 | 0.8358% | | c++ | 14.136 | 0.9149% | 4992 | 0.711% | | scss | 13.208 | 0.8549% | 6829 | 0.9727% | | go | 12.132 | 0.7852% | 5004 | 0.7128% | | scala | 11.184 | 0.7239% | 5040 | 0.7179% | | haml | 10.74 | 0.6951% | 4415 | 0.6289% | | css | 9.364 | 0.6061% | 5049 | 0.7192% | | rust | 7.244 | 0.4689% | 2996 | 0.4267% | | toml | 5.584 | 0.3614% | 3424 | 0.4877% | | jsx | 5.5 | 0.356% | 2199 | 0.3132% | | kotlin | 5.368 | 0.3474% | 2214 | 0.3154% | | clojure | 5.068 | 0.328% | 2403 | 0.3423% | | perl | 4.988 | 0.3228% | 2288 | 0.3259% | | bitbake | 4.464 | 0.2889% | 1308 | 0.1863% | | groovy | 4.168 | 0.2698% | 1486 | 0.2117% | | twig | 3.956 | 0.256% | 1610 | 0.2293% | | nix | 3.84 | 0.2485% | 1593 | 0.2269% | | sql | 3.74 | 0.2421% | 2069 | 0.2947% | | less | 3.724 | 0.241% | 1360 | 0.1937% | | haskell | 3.308 | 0.2141% | 1389 | 0.1978% | | handlebars | 3.292 | 0.2131% | 1429 | 0.2035% | | unknown | 3.048 | 0.1973% | 1597 | 0.2275% | | batchfile | 2.984 | 0.1931% | 1466 | 0.2088% | | cucumber | 2.588 | 0.1675% | 976 | 0.139% | | makefile | 2.528 | 0.1636% | 960 | 0.1367% | | elixir | 2.348 | 0.152% | 1150 | 0.1638% | | jade | 2.348 | 0.152% | 1119 | 0.1594% | | cmake | 2.268 | 0.1468% | 981 | 0.1397% | | powershell | 2.064 | 0.1336% | 991 | 0.1412% | | slim | 2.056 | 0.1331% | 1052 | 0.1498% | | emacs-lisp | 1.972 | 0.1276% | 1015 | 0.1446% | | dart | 1.96 | 0.1269% | 765 | 0.109% | | viml | 1.956 | 0.1266% | 1063 | 0.1514% | | asciidoc | 1.864 | 0.1206% | 523 | 0.0745% | | lua | 1.852 | 0.1199% | 920 | 0.131% | | llvm | 1.6 | 0.1036% | 780 | 0.1111% | | smarty | 1.588 | 0.1028% | 737 | 0.105% | | diff | 1.48 | 0.0958% | 680 | 0.0969% | | common-lisp | 1.448 | 0.0937% | 778 | 0.1108% | | saltstack | 1.412 | 0.0914% | 617 | 0.0879% | | vue | 1.384 | 0.0896% | 587 | 0.0836% | | sass | 1.364 | 0.0883% | 705 | 0.1004% | | fish | 1.328 | 0.086% | 813 | 0.1158% | | erlang | 1.192 | 0.0772% | 480 | 0.0684% | | freemarker | 1.028 | 0.0665% | 510 | 0.0726% | | stylus | 0.948 | 0.0614% | 480 | 0.0684% | | qml | 0.936 | 0.0606% | 368 | 0.0524% | | hcl | 0.912 | 0.059% | 421 | 0.06% | | html+django | 0.848 | 0.0549% | 399 | 0.0568% | | mako | 0.756 | 0.0489% | 170 | 0.0242% | | ada | 0.728 | 0.0471% | 265 | 0.0377% | | ocaml | 0.704 | 0.0456% | 333 | 0.0474% | | f# | 0.656 | 0.0425% | 254 | 0.0362% | | elm | 0.62 | 0.0401% | 265 | 0.0377% | | tex | 0.564 | 0.0365% | 307 | 0.0437% | | rdoc | 0.552 | 0.0357% | 270 | 0.0385% | | csv | 0.532 | 0.0344% | 375 | 0.0534% | | protocol-buffer | 0.524 | 0.0339% | 181 | 0.0258% | | smalltalk | 0.46 | 0.0298% | 284 | 0.0405% | | arduino | 0.456 | 0.0295% | 225 | 0.032% | | java-server-pages | 0.452 | 0.0293% | 173 | 0.0246% | | scheme | 0.42 | 0.0272% | 213 | 0.0303% | | groff | 0.396 | 0.0256% | 192 | 0.0273% | | objective-c++ | 0.376 | 0.0243% | 86 | 0.0122% | | desktop | 0.364 | 0.0236% | 186 | 0.0265% | | factor | 0.356 | 0.023% | 113 | 0.0161% | | crystal | 0.348 | 0.0225% | 182 | 0.0259% | | rhtml | 0.348 | 0.0225% | 135 | 0.0192% | | haxe | 0.344 | 0.0223% | 174 | 0.0248% | | glsl | 0.34 | 0.022% | 164 | 0.0234% | | gas | 0.336 | 0.0217% | 193 | 0.0275% | | html+php | 0.332 | 0.0215% | 150 | 0.0214% | | qmake | 0.32 | 0.0207% | 140 | 0.0199% | | julia | 0.312 | 0.0202% | 180 | 0.0256% | | cython | 0.308 | 0.0199% | 123 | 0.0175% | | html+eex | 0.292 | 0.0189% | 135 | 0.0192% | | tcl | 0.292 | 0.0189% | 103 | 0.0147% | | org | 0.272 | 0.0176% | 136 | 0.0194% | | perl6 | 0.268 | 0.0173% | 122 | 0.0174% | | m4 | 0.264 | 0.0171% | 101 | 0.0144% | | xslt | 0.256 | 0.0166% | 99 | 0.0141% | | svg | 0.252 | 0.0163% | 169 | 0.0241% | | nimrod | 0.236 | 0.0153% | 67 | 0.0095% | | r | 0.228 | 0.0148% | 121 | 0.0172% | | robotframework | 0.212 | 0.0137% | 85 | 0.0121% | | racket | 0.196 | 0.0127% | 117 | 0.0167% | | textile | 0.184 | 0.0119% | 61 | 0.0087% | | assembly | 0.172 | 0.0111% | 105 | 0.015% | | purescript | 0.172 | 0.0111% | 80 | 0.0114% | | unity3d-asset | 0.156 | 0.0101% | 101 | 0.0144% | | visual-basic | 0.152 | 0.0098% | 48 | 0.0068% | | dm | 0.148 | 0.0096% | 16 | 0.0023% | | pod | 0.148 | 0.0096% | 54 | 0.0077% | | standard-ml | 0.148 | 0.0096% | 72 | 0.0103% | | fortran | 0.144 | 0.0093% | 70 | 0.01% | | gettext-catalog | 0.132 | 0.0085% | 72 | 0.0103% | | idris | 0.132 | 0.0085% | 38 | 0.0054% | | livescript | 0.128 | 0.0083% | 63 | 0.009% | | xtend | 0.128 | 0.0083% | 55 | 0.0078% | | actionscript | 0.12 | 0.0078% | 49 | 0.007% | | vala | 0.116 | 0.0075% | 50 | 0.0071% | | awk | 0.104 | 0.0067% | 52 | 0.0074% | | ceylon | 0.1 | 0.0065% | 49 | 0.007% | | jupyter-notebook | 0.1 | 0.0065% | 48 | 0.0068% | | dockerfile | 0.096 | 0.0062% | 39 | 0.0056% | | rouge | 0.096 | 0.0062% | 41 | 0.0058% | | asp | 0.092 | 0.006% | 22 | 0.0031% | | sqf | 0.092 | 0.006% | 45 | 0.0064% | | edn | 0.088 | 0.0057% | 48 | 0.0068% | | liquid | 0.088 | 0.0057% | 30 | 0.0043% | | xquery | 0.084 | 0.0054% | 39 | 0.0056% | | linker-script | 0.08 | 0.0052% | 37 | 0.0053% | | mediawiki | 0.08 | 0.0052% | 33 | 0.0047% | | parrot-internal-representation | 0.08 | 0.0052% | 23 | 0.0033% | | solidity | 0.08 | 0.0052% | 37 | 0.0053% | | json5 | 0.076 | 0.0049% | 33 | 0.0047% | | systemverilog | 0.076 | 0.0049% | 35 | 0.005% | | thrift | 0.076 | 0.0049% | 28 | 0.004% | | groovy-server-pages | 0.072 | 0.0047% | 25 | 0.0036% | | processing | 0.072 | 0.0047% | 35 | 0.005% | | cuda | 0.068 | 0.0044% | 25 | 0.0036% | | graphviz-dot | 0.068 | 0.0044% | 35 | 0.005% | | inno-setup | 0.064 | 0.0041% | 16 | 0.0023% | | api-blueprint | 0.06 | 0.0039% | 23 | 0.0033% | | nsis | 0.06 | 0.0039% | 15 | 0.0021% | | gentoo-ebuild | 0.056 | 0.0036% | 16 | 0.0023% | | logtalk | 0.056 | 0.0036% | 21 | 0.003% | | jasmin | 0.052 | 0.0034% | 9 | 0.0013% | | literate-coffeescript | 0.052 | 0.0034% | 19 | 0.0027% | | webidl | 0.052 | 0.0034% | 6 | 0.0009% | | coldfusion-cfc | 0.048 | 0.0031% | 20 | 0.0028% | | opencl | 0.048 | 0.0031% | 23 | 0.0033% | | openscad | 0.048 | 0.0031% | 21 | 0.003% | | pan | 0.048 | 0.0031% | 23 | 0.0033% | | pascal | 0.048 | 0.0031% | 25 | 0.0036% | | pony | 0.048 | 0.0031% | 16 | 0.0023% | | turtle | 0.048 | 0.0031% | 21 | 0.003% | | chapel | 0.044 | 0.0028% | 20 | 0.0028% | | ioke | 0.044 | 0.0028% | 25 | 0.0036% | | ooc | 0.044 | 0.0028% | 15 | 0.0021% | | sparql | 0.044 | 0.0028% | 23 | 0.0033% | | applescript | 0.04 | 0.0026% | 19 | 0.0027% | | augeas | 0.04 | 0.0026% | 13 | 0.0019% | | g-code | 0.04 | 0.0026% | 7 | 0.001% | | mirah | 0.04 | 0.0026% | 16 | 0.0023% | | capn-proto | 0.036 | 0.0023% | 12 | 0.0017% | | digital-command-language | 0.036 | 0.0023% | 19 | 0.0027% | | hy | 0.036 | 0.0023% | 12 | 0.0017% | | logos | 0.036 | 0.0023% | 19 | 0.0027% | | modelica | 0.036 | 0.0023% | 15 | 0.0021% | | vcl | 0.036 | 0.0023% | 18 | 0.0026% | | antlr | 0.032 | 0.0021% | 15 | 0.0021% | | gdscript | 0.032 | 0.0021% | 9 | 0.0013% | | graphql | 0.032 | 0.0021% | 17 | 0.0024% | | hlsl | 0.032 | 0.0021% | 11 | 0.0016% | | gnuplot | 0.028 | 0.0018% | 17 | 0.0024% | | http | 0.028 | 0.0018% | 19 | 0.0027% | | ninja | 0.028 | 0.0018% | 14 | 0.002% | | oz | 0.028 | 0.0018% | 8 | 0.0011% | | raml | 0.028 | 0.0018% | 9 | 0.0013% | | aspectj | 0.024 | 0.0016% | 8 | 0.0011% | | autohotkey | 0.024 | 0.0016% | 15 | 0.0021% | | fancy | 0.024 | 0.0016% | 8 | 0.0011% | | moonscript | 0.024 | 0.0016% | 10 | 0.0014% | | piglatin | 0.024 | 0.0016% | 11 | 0.0016% | | stata | 0.024 | 0.0016% | 10 | 0.0014% | | urweb | 0.024 | 0.0016% | 6 | 0.0009% | | xs | 0.024 | 0.0016% | 7 | 0.001% | | yang | 0.024 | 0.0016% | 6 | 0.0009% | | agda | 0.02 | 0.0013% | 10 | 0.0014% | | coldfusion | 0.02 | 0.0013% | 9 | 0.0013% | | emberscript | 0.02 | 0.0013% | 7 | 0.001% | | latte | 0.02 | 0.0013% | 7 | 0.001% | | literate-haskell | 0.02 | 0.0013% | 7 | 0.001% | | postscript | 0.02 | 0.0013% | 9 | 0.0013% | | scilab | 0.02 | 0.0013% | 10 | 0.0014% | | tcsh | 0.02 | 0.0013% | 10 | 0.0014% | | volt | 0.02 | 0.0013% | 9 | 0.0013% | | apl | 0.016 | 0.001% | 7 | 0.001% | | genshi | 0.016 | 0.001% | 3 | 0.0004% | | jsonld | 0.016 | 0.001% | 6 | 0.0009% | | krl | 0.016 | 0.001% | 4 | 0.0006% | | lean | 0.016 | 0.001% | 3 | 0.0004% | | lfe | 0.016 | 0.001% | 6 | 0.0009% | | metal | 0.016 | 0.001% | 4 | 0.0006% | | monkey | 0.016 | 0.001% | 4 | 0.0006% | | mupad | 0.016 | 0.001% | 4 | 0.0006% | | nesc | 0.016 | 0.001% | 7 | 0.001% | | nit | 0.016 | 0.001% | 3 | 0.0004% | | pike | 0.016 | 0.001% | 6 | 0.0009% | | purebasic | 0.016 | 0.001% | 5 | 0.0007% | | renpy | 0.016 | 0.001% | 3 | 0.0004% | | vhdl | 0.016 | 0.001% | 5 | 0.0007% | | xproc | 0.016 | 0.001% | 3 | 0.0004% | | zephir | 0.016 | 0.001% | 4 | 0.0006% | | apacheconf | 0.012 | 0.0008% | 2 | 0.0003% | | boo | 0.012 | 0.0008% | 2 | 0.0003% | | brainfuck | 0.012 | 0.0008% | 2 | 0.0003% | | bro | 0.012 | 0.0008% | 3 | 0.0004% | | cartocss | 0.012 | 0.0008% | 3 | 0.0004% | | creole | 0.012 | 0.0008% | 2 | 0.0003% | | csound | 0.012 | 0.0008% | 4 | 0.0006% | | dylan | 0.012 | 0.0008% | 2 | 0.0003% | | eagle | 0.012 | 0.0008% | 4 | 0.0006% | | ecl | 0.012 | 0.0008% | 4 | 0.0006% | | eiffel | 0.012 | 0.0008% | 2 | 0.0003% | | flux | 0.012 | 0.0008% | 3 | 0.0004% | | io | 0.012 | 0.0008% | 4 | 0.0006% | | jsoniq | 0.012 | 0.0008% | 6 | 0.0009% | | lilypond | 0.012 | 0.0008% | 6 | 0.0009% | | lsl | 0.012 | 0.0008% | 3 | 0.0004% | | mask | 0.012 | 0.0008% | 4 | 0.0006% | | nginx | 0.012 | 0.0008% | 2 | 0.0003% | | nu | 0.012 | 0.0008% | 2 | 0.0003% | | pov-ray-sdl | 0.012 | 0.0008% | 5 | 0.0007% | | ragel-in-ruby-host | 0.012 | 0.0008% | 4 | 0.0006% | | slash | 0.012 | 0.0008% | 4 | 0.0006% | | sourcepawn | 0.012 | 0.0008% | 3 | 0.0004% | | squirrel | 0.012 | 0.0008% | 4 | 0.0006% | | ston | 0.012 | 0.0008% | 6 | 0.0009% | | uno | 0.012 | 0.0008% | 2 | 0.0003% | | wisp | 0.012 | 0.0008% | 3 | 0.0004% | | xbase | 0.012 | 0.0008% | 3 | 0.0004% | | yacc | 0.012 | 0.0008% | 3 | 0.0004% | | zig | 0.012 | 0.0008% | 4 | 0.0006% | | abap | 0.008 | 0.0005% | 1 | 0.0001% | | arc | 0.008 | 0.0005% | 2 | 0.0003% | | ats | 0.008 | 0.0005% | 3 | 0.0004% | | blitzmax | 0.008 | 0.0005% | 1 | 0.0001% | | bluespec | 0.008 | 0.0005% | 2 | 0.0003% | | c2hs-haskell | 0.008 | 0.0005% | 2 | 0.0003% | | clean | 0.008 | 0.0005% | 1 | 0.0001% | | dns-zone | 0.008 | 0.0005% | 2 | 0.0003% | | forth | 0.008 | 0.0005% | 2 | 0.0003% | | harbour | 0.008 | 0.0005% | 1 | 0.0001% | | igor-pro | 0.008 | 0.0005% | 1 | 0.0001% | | inform-7 | 0.008 | 0.0005% | 2 | 0.0003% | | isabelle | 0.008 | 0.0005% | 2 | 0.0003% | | jflex | 0.008 | 0.0005% | 1 | 0.0001% | | literate-agda | 0.008 | 0.0005% | 1 | 0.0001% | | maple | 0.008 | 0.0005% | 2 | 0.0003% | | mathematica | 0.008 | 0.0005% | 1 | 0.0001% | | module-management-system | 0.008 | 0.0005% | 1 | 0.0001% | | mtml | 0.008 | 0.0005% | 2 | 0.0003% | | netlinx | 0.008 | 0.0005% | 1 | 0.0001% | | parrot-assembly | 0.008 | 0.0005% | 2 | 0.0003% | | pawn | 0.008 | 0.0005% | 3 | 0.0004% | | propeller-spin | 0.008 | 0.0005% | 1 | 0.0001% | | pure-data | 0.008 | 0.0005% | 1 | 0.0001% | | rebol | 0.008 | 0.0005% | 3 | 0.0004% | | red | 0.008 | 0.0005% | 1 | 0.0001% | | sage | 0.008 | 0.0005% | 1 | 0.0001% | | sas | 0.008 | 0.0005% | 1 | 0.0001% | | scaml | 0.008 | 0.0005% | 1 | 0.0001% | | smt | 0.008 | 0.0005% | 3 | 0.0004% | | supercollider | 0.008 | 0.0005% | 2 | 0.0003% | | unrealscript | 0.008 | 0.0005% | 1 | 0.0001% | | xpages | 0.008 | 0.0005% | 1 | 0.0001% | ## Additional Information ### Licensing Information Each sample comes from a code repository with a permissive license. The license is provided by the `license` field for each sample. ### Citation Information ```bibtex @article{muennighoff2023octopack, title={OctoPack: Instruction Tuning Code Large Language Models}, author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre}, journal={arXiv preprint arXiv:2308.07124}, year={2023} } ```

mteb/biblenlp-corpus-mmteb

mteb

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - aai - aak - aau - aaz - abt - abx - aby - acf - acr - acu - adz - aer - aey - agd - agg - agm - agn - agr - agt - agu - aia - aii - aka - ake - alp - alq - als - aly - ame - amf - amk - amm - amn - amo - amp - amr - amu - amx - anh - anv - aoi - aoj - aom - aon - apb - ape - apn - apr - apu - apw - apz - arb - are - arl - arn - arp - asm - aso - ata - atb - atd - atg - att - auc - aui - auy - avt - awb - awk - awx - azb - azg - azz - bao - bba - bbb - bbr - bch - bco - bdd - bea - bef - bel - ben - beo - beu - bgs - bgt - bhg - bhl - big - bjk - bjp - bjr - bjv - bjz - bkd - bki - bkq - bkx - bla - blw - blz - bmh - bmk - bmr - bmu - bnp - boa - boj - bon - box - bpr - bps - bqc - bqp - bre - bsj - bsn - bsp - bss - buk - bus - bvd - bvr - bxh - byr - byx - bzd - bzh - bzj - caa - cab - cac - caf - cak - cao - cap - car - cav - cax - cbc - cbi - cbk - cbr - cbs - cbt - cbu - cbv - cco - ceb - cek - ces - cgc - cha - chd - chf - chk - chq - chz - cjo - cjv - ckb - cle - clu - cme - cmn - cni - cnl - cnt - cof - con - cop - cot - cpa - cpb - cpc - cpu - cpy - crn - crx - cso - csy - cta - cth - ctp - ctu - cub - cuc - cui - cuk - cut - cux - cwe - cya - daa - dad - dah - dan - ded - deu - dgc - dgr - dgz - dhg - dif - dik - dji - djk - djr - dob - dop - dov - dwr - dww - dwy - ebk - eko - emi - emp - eng - enq - epo - eri - ese - esk - etr - ewe - faa - fai - far - ffm - for - fra - fue - fuf - fuh - gah - gai - gam - gaw - gdn - gdr - geb - gfk - ghs - glk - gmv - gng - gnn - gnw - gof - grc - gub - guh - gui - guj - gul - gum - gun - guo - gup - gux - gvc - gvf - gvn - gvs - gwi - gym - gyr - hat - hau - haw - hbo - hch - heb - heg - hin - hix - hla - hlt - hmo - hns - hop - hot - hrv - hto - hub - hui - hun - hus - huu - huv - hvn - ian - ign - ikk - ikw - ilo - imo - inb - ind - ino - iou - ipi - isn - ita - iws - ixl - jac - jae - jao - jic - jid - jiv - jni - jpn - jvn - kan - kaq - kbc - kbh - kbm - kbq - kdc - kde - kdl - kek - ken - kew - kgf - kgk - kgp - khs - khz - kik - kiw - kiz - kje - kjn - kjs - kkc - kkl - klt - klv - kmg - kmh - kmk - kmo - kms - kmu - kne - knf - knj - knv - kos - kpf - kpg - kpj - kpr - kpw - kpx - kqa - kqc - kqf - kql - kqw - ksd - ksj - ksr - ktm - kto - kud - kue - kup - kvg - kvn - kwd - kwf - kwi - kwj - kyc - kyf - kyg - kyq - kyz - kze - lac - lat - lbb - lbk - lcm - leu - lex - lgl - lid - lif - lin - lit - llg - lug - luo - lww - maa - maj - mal - mam - maq - mar - mau - mav - maz - mbb - mbc - mbh - mbj - mbl - mbs - mbt - mca - mcb - mcd - mcf - mco - mcp - mcq - mcr - mdy - med - mee - mek - meq - met - meu - mgc - mgh - mgw - mhl - mib - mic - mie - mig - mih - mil - mio - mir - mit - miz - mjc - mkj - mkl - mkn - mks - mle - mlh - mlp - mmo - mmx - mna - mop - mox - mph - mpj - mpm - mpp - mps - mpt - mpx - mqb - mqj - msb - msc - msk - msm - msy - mti - mto - mux - muy - mva - mvn - mwc - mwe - mwf - mwp - mxb - mxp - mxq - mxt - mya - myk - myu - myw - myy - mzz - nab - naf - nak - nas - nay - nbq - nca - nch - ncj - ncl - ncu - ndg - ndj - nfa - ngp - ngu - nhe - nhg - nhi - nho - nhr - nhu - nhw - nhy - nif - nii - nin - nko - nld - nlg - nmw - nna - nnq - noa - nop - not - nou - npi - npl - nsn - nss - ntj - ntp - ntu - nuy - nvm - nwi - nya - nys - nyu - obo - okv - omw - ong - ons - ood - opm - ory - ote - otm - otn - otq - ots - pab - pad - pah - pan - pao - pes - pib - pio - pir - piu - pjt - pls - plu - pma - poe - poh - poi - pol - pon - por - poy - ppo - prf - pri - ptp - ptu - pwg - qub - quc - quf - quh - qul - qup - qvc - qve - qvh - qvm - qvn - qvs - qvw - qvz - qwh - qxh - qxn - qxo - rai - reg - rgu - rkb - rmc - rmy - ron - roo - rop - row - rro - ruf - rug - rus - rwo - sab - san - sbe - sbk - sbs - seh - sey - sgb - sgz - shj - shp - sim - sja - sll - smk - snc - snn - snp - snx - sny - som - soq - soy - spa - spl - spm - spp - sps - spy - sri - srm - srn - srp - srq - ssd - ssg - ssx - stp - sua - sue - sus - suz - swe - swh - swp - sxb - tac - taj - tam - tav - taw - tbc - tbf - tbg - tbl - tbo - tbz - tca - tcs - tcz - tdt - tee - tel - ter - tet - tew - tfr - tgk - tgl - tgo - tgp - tha - thd - tif - tim - tiw - tiy - tke - tku - tlf - tmd - tna - tnc - tnk - tnn - tnp - toc - tod - tof - toj - ton - too - top - tos - tpa - tpi - tpt - tpz - trc - tsw - ttc - tte - tuc - tue - tuf - tuo - tur - tvk - twi - txq - txu - tzj - tzo - ubr - ubu - udu - uig - ukr - uli - ulk - upv - ura - urb - urd - uri - urt - urw - usa - usp - uvh - uvl - vid - vie - viv - vmy - waj - wal - wap - wat - wbi - wbp - wed - wer - wim - wiu - wiv - wmt - wmw - wnc - wnu - wol - wos - wrk - wro - wrs - wsk - wuv - xav - xbi - xed - xla - xnn - xon - xsi - xtd - xtm - yaa - yad - yal - yap - yaq - yby - ycn - yka - yle - yml - yon - yor - yrb - yre - yss - yuj - yut - yuw - yva - zaa - zab - zac - zad - zai - zaj - zam - zao - zap - zar - zas - zat - zav - zaw - zca - zga - zia - ziw - zlm - zos - zpc - zpl - zpm - zpo - zpq - zpu - zpv - zpz - zsr - ztq - zty - zyp - be - br - cs - ch - zh - de - en - eo - fr - ht - he - hr - id - it - ja - la - nl - ru - sa - so - es - sr - sv - to - uk - vi license: - cc-by-4.0 - other multilinguality: - translation - multilingual pretty_name: biblenlp-corpus-mmteb size_categories: - 1M<n<10M configs: - config_name: default data_files: - path: train/*.jsonl.gz split: train - path: test/*.jsonl.gz split: test - path: validation/*.jsonl.gz split: validation - config_name: eng_Latn-zac_Latn data_files: - path: train/eng_Latn-zac_Latn.jsonl.gz split: train - path: test/eng_Latn-zac_Latn.jsonl.gz split: test - path: validation/eng_Latn-zac_Latn.jsonl.gz split: validation - config_name: eng_Latn-lit_Latn data_files: - path: train/eng_Latn-lit_Latn.jsonl.gz split: train - path: test/eng_Latn-lit_Latn.jsonl.gz split: test - path: validation/eng_Latn-lit_Latn.jsonl.gz split: validation - config_name: eng_Latn-cgc_Latn data_files: - path: train/eng_Latn-cgc_Latn.jsonl.gz split: train - path: test/eng_Latn-cgc_Latn.jsonl.gz split: test - path: validation/eng_Latn-cgc_Latn.jsonl.gz split: validation - config_name: eng_Latn-guh_Latn data_files: - path: train/eng_Latn-guh_Latn.jsonl.gz split: train - path: test/eng_Latn-guh_Latn.jsonl.gz split: test - path: validation/eng_Latn-guh_Latn.jsonl.gz split: validation - config_name: eng_Latn-ckb_Arab data_files: - path: train/eng_Latn-ckb_Arab.jsonl.gz split: train - path: test/eng_Latn-ckb_Arab.jsonl.gz split: test - path: validation/eng_Latn-ckb_Arab.jsonl.gz split: validation - config_name: eng_Latn-cop_Copt data_files: - path: train/eng_Latn-cop_Copt.jsonl.gz split: train - path: test/eng_Latn-cop_Copt.jsonl.gz split: test - path: validation/eng_Latn-cop_Copt.jsonl.gz split: validation - config_name: eng_Latn-lif_Deva data_files: - path: train/eng_Latn-lif_Deva.jsonl.gz split: train - path: test/eng_Latn-lif_Deva.jsonl.gz split: test - path: validation/eng_Latn-lif_Deva.jsonl.gz split: validation - config_name: eng_Latn-cwe_Latn data_files: - path: train/eng_Latn-cwe_Latn.jsonl.gz split: train - path: test/eng_Latn-cwe_Latn.jsonl.gz split: test - path: validation/eng_Latn-cwe_Latn.jsonl.gz split: validation - config_name: eng_Latn-kwj_Latn data_files: - path: train/eng_Latn-kwj_Latn.jsonl.gz split: train - path: test/eng_Latn-kwj_Latn.jsonl.gz split: test - path: validation/eng_Latn-kwj_Latn.jsonl.gz split: validation - config_name: eng_Latn-srp_Latn data_files: - path: train/eng_Latn-srp_Latn.jsonl.gz split: train - path: test/eng_Latn-srp_Latn.jsonl.gz split: test - path: validation/eng_Latn-srp_Latn.jsonl.gz split: validation - config_name: eng_Latn-qvn_Latn data_files: - path: train/eng_Latn-qvn_Latn.jsonl.gz split: train - path: test/eng_Latn-qvn_Latn.jsonl.gz split: test - path: validation/eng_Latn-qvn_Latn.jsonl.gz split: validation - config_name: eng_Latn-tsw_Latn data_files: - path: train/eng_Latn-tsw_Latn.jsonl.gz split: train - path: test/eng_Latn-tsw_Latn.jsonl.gz split: test - path: validation/eng_Latn-tsw_Latn.jsonl.gz split: validation - config_name: eng_Latn-wro_Latn data_files: - path: train/eng_Latn-wro_Latn.jsonl.gz split: train - path: test/eng_Latn-wro_Latn.jsonl.gz split: test - path: validation/eng_Latn-wro_Latn.jsonl.gz split: validation - config_name: eng_Latn-tod_Latn data_files: - path: train/eng_Latn-tod_Latn.jsonl.gz split: train - path: test/eng_Latn-tod_Latn.jsonl.gz split: test - path: validation/eng_Latn-tod_Latn.jsonl.gz split: validation - config_name: eng_Latn-bco_Latn data_files: - path: train/eng_Latn-bco_Latn.jsonl.gz split: train - path: test/eng_Latn-bco_Latn.jsonl.gz split: test - path: validation/eng_Latn-bco_Latn.jsonl.gz split: validation - config_name: eng_Latn-ikk_Latn data_files: - path: train/eng_Latn-ikk_Latn.jsonl.gz split: train - path: test/eng_Latn-ikk_Latn.jsonl.gz split: test - path: validation/eng_Latn-ikk_Latn.jsonl.gz split: validation - config_name: eng_Latn-tna_Latn data_files: - path: train/eng_Latn-tna_Latn.jsonl.gz split: train - path: test/eng_Latn-tna_Latn.jsonl.gz split: test - path: validation/eng_Latn-tna_Latn.jsonl.gz split: validation - config_name: eng_Latn-swp_Latn data_files: - path: train/eng_Latn-swp_Latn.jsonl.gz split: train - path: test/eng_Latn-swp_Latn.jsonl.gz split: test - path: validation/eng_Latn-swp_Latn.jsonl.gz split: validation - config_name: eng_Latn-agm_Latn data_files: - path: train/eng_Latn-agm_Latn.jsonl.gz split: train - path: test/eng_Latn-agm_Latn.jsonl.gz split: test - path: validation/eng_Latn-agm_Latn.jsonl.gz split: validation - config_name: eng_Latn-con_Latn data_files: - path: train/eng_Latn-con_Latn.jsonl.gz split: train - path: test/eng_Latn-con_Latn.jsonl.gz split: test - path: validation/eng_Latn-con_Latn.jsonl.gz split: validation - config_name: eng_Latn-sgz_Latn data_files: - path: train/eng_Latn-sgz_Latn.jsonl.gz split: train - path: test/eng_Latn-sgz_Latn.jsonl.gz split: test - path: validation/eng_Latn-sgz_Latn.jsonl.gz split: validation - config_name: eng_Latn-mwc_Latn data_files: - path: train/eng_Latn-mwc_Latn.jsonl.gz split: train - path: test/eng_Latn-mwc_Latn.jsonl.gz split: test - path: validation/eng_Latn-mwc_Latn.jsonl.gz split: validation - config_name: eng_Latn-azb_Arab data_files: - path: train/eng_Latn-azb_Arab.jsonl.gz split: train - path: test/eng_Latn-azb_Arab.jsonl.gz split: test - path: validation/eng_Latn-azb_Arab.jsonl.gz split: validation - config_name: eng_Latn-aon_Latn data_files: - path: train/eng_Latn-aon_Latn.jsonl.gz split: train - path: test/eng_Latn-aon_Latn.jsonl.gz split: test - path: validation/eng_Latn-aon_Latn.jsonl.gz split: validation - config_name: eng_Latn-mvn_Latn data_files: - path: train/eng_Latn-mvn_Latn.jsonl.gz split: train - path: test/eng_Latn-mvn_Latn.jsonl.gz split: test - path: validation/eng_Latn-mvn_Latn.jsonl.gz split: validation - config_name: eng_Latn-mpj_Latn data_files: - path: train/eng_Latn-mpj_Latn.jsonl.gz split: train - path: test/eng_Latn-mpj_Latn.jsonl.gz split: test - path: validation/eng_Latn-mpj_Latn.jsonl.gz split: validation - config_name: eng_Latn-cot_Latn data_files: - path: train/eng_Latn-cot_Latn.jsonl.gz split: train - path: test/eng_Latn-cot_Latn.jsonl.gz split: test - path: validation/eng_Latn-cot_Latn.jsonl.gz split: validation - config_name: eng_Latn-tuo_Latn data_files: - path: train/eng_Latn-tuo_Latn.jsonl.gz split: train - path: test/eng_Latn-tuo_Latn.jsonl.gz split: test - path: validation/eng_Latn-tuo_Latn.jsonl.gz split: validation - config_name: eng_Latn-iou_Latn data_files: - path: train/eng_Latn-iou_Latn.jsonl.gz split: train - path: test/eng_Latn-iou_Latn.jsonl.gz split: test - path: validation/eng_Latn-iou_Latn.jsonl.gz split: validation - config_name: eng_Latn-big_Latn data_files: - path: train/eng_Latn-big_Latn.jsonl.gz split: train - path: test/eng_Latn-big_Latn.jsonl.gz split: test - path: validation/eng_Latn-big_Latn.jsonl.gz split: validation - config_name: eng_Latn-apw_Latn data_files: - path: train/eng_Latn-apw_Latn.jsonl.gz split: train - path: test/eng_Latn-apw_Latn.jsonl.gz split: test - path: validation/eng_Latn-apw_Latn.jsonl.gz split: validation - config_name: eng_Latn-kpx_Latn data_files: - path: train/eng_Latn-kpx_Latn.jsonl.gz split: train - path: test/eng_Latn-kpx_Latn.jsonl.gz split: test - path: validation/eng_Latn-kpx_Latn.jsonl.gz split: validation - config_name: eng_Latn-cui_Latn data_files: - path: train/eng_Latn-cui_Latn.jsonl.gz split: train - path: test/eng_Latn-cui_Latn.jsonl.gz split: test - path: validation/eng_Latn-cui_Latn.jsonl.gz split: validation - config_name: eng_Latn-bnp_Latn data_files: - path: train/eng_Latn-bnp_Latn.jsonl.gz split: train - path: test/eng_Latn-bnp_Latn.jsonl.gz split: test - path: validation/eng_Latn-bnp_Latn.jsonl.gz split: validation - config_name: eng_Latn-ngp_Latn data_files: - path: train/eng_Latn-ngp_Latn.jsonl.gz split: train - path: test/eng_Latn-ngp_Latn.jsonl.gz split: test - path: validation/eng_Latn-ngp_Latn.jsonl.gz split: validation - config_name: eng_Latn-mkj_Latn data_files: - path: train/eng_Latn-mkj_Latn.jsonl.gz split: train - path: test/eng_Latn-mkj_Latn.jsonl.gz split: test - path: validation/eng_Latn-mkj_Latn.jsonl.gz split: validation - config_name: eng_Latn-chf_Latn data_files: - path: train/eng_Latn-chf_Latn.jsonl.gz split: train - path: test/eng_Latn-chf_Latn.jsonl.gz split: test - path: validation/eng_Latn-chf_Latn.jsonl.gz split: validation - config_name: eng_Latn-tca_Latn data_files: - path: train/eng_Latn-tca_Latn.jsonl.gz split: train - path: test/eng_Latn-tca_Latn.jsonl.gz split: test - path: validation/eng_Latn-tca_Latn.jsonl.gz split: validation - config_name: eng_Latn-poh_Latn data_files: - path: train/eng_Latn-poh_Latn.jsonl.gz split: train - path: test/eng_Latn-poh_Latn.jsonl.gz split: test - path: validation/eng_Latn-poh_Latn.jsonl.gz split: validation - config_name: eng_Latn-ese_Latn data_files: - path: train/eng_Latn-ese_Latn.jsonl.gz split: train - path: test/eng_Latn-ese_Latn.jsonl.gz split: test - path: validation/eng_Latn-ese_Latn.jsonl.gz split: validation - config_name: eng_Latn-plu_Latn data_files: - path: train/eng_Latn-plu_Latn.jsonl.gz split: train - path: test/eng_Latn-plu_Latn.jsonl.gz split: test - path: validation/eng_Latn-plu_Latn.jsonl.gz split: validation - config_name: eng_Latn-crn_Latn data_files: - path: train/eng_Latn-crn_Latn.jsonl.gz split: train - path: test/eng_Latn-crn_Latn.jsonl.gz split: test - path: validation/eng_Latn-crn_Latn.jsonl.gz split: validation - config_name: eng_Latn-mxt_Latn data_files: - path: train/eng_Latn-mxt_Latn.jsonl.gz split: train - path: test/eng_Latn-mxt_Latn.jsonl.gz split: test - path: validation/eng_Latn-mxt_Latn.jsonl.gz split: validation - config_name: eng_Latn-tnk_Latn data_files: - path: train/eng_Latn-tnk_Latn.jsonl.gz split: train - path: test/eng_Latn-tnk_Latn.jsonl.gz split: test - path: validation/eng_Latn-tnk_Latn.jsonl.gz split: validation - config_name: eng_Latn-zar_Latn data_files: - path: train/eng_Latn-zar_Latn.jsonl.gz split: train - path: test/eng_Latn-zar_Latn.jsonl.gz split: test - path: validation/eng_Latn-zar_Latn.jsonl.gz split: validation - config_name: eng_Latn-sri_Latn data_files: - path: train/eng_Latn-sri_Latn.jsonl.gz split: train - path: test/eng_Latn-sri_Latn.jsonl.gz split: test - path: validation/eng_Latn-sri_Latn.jsonl.gz split: validation - config_name: eng_Latn-pan_Guru data_files: - path: train/eng_Latn-pan_Guru.jsonl.gz split: train - path: test/eng_Latn-pan_Guru.jsonl.gz split: test - path: validation/eng_Latn-pan_Guru.jsonl.gz split: validation - config_name: eng_Latn-kik_Latn data_files: - path: train/eng_Latn-kik_Latn.jsonl.gz split: train - path: test/eng_Latn-kik_Latn.jsonl.gz split: test - path: validation/eng_Latn-kik_Latn.jsonl.gz split: validation - config_name: eng_Latn-yby_Latn data_files: - path: train/eng_Latn-yby_Latn.jsonl.gz split: train - path: test/eng_Latn-yby_Latn.jsonl.gz split: test - path: validation/eng_Latn-yby_Latn.jsonl.gz split: validation - config_name: eng_Latn-qup_Latn data_files: - path: train/eng_Latn-qup_Latn.jsonl.gz split: train - path: test/eng_Latn-qup_Latn.jsonl.gz split: test - path: validation/eng_Latn-qup_Latn.jsonl.gz split: validation - config_name: eng_Latn-mco_Latn data_files: - path: train/eng_Latn-mco_Latn.jsonl.gz split: train - path: test/eng_Latn-mco_Latn.jsonl.gz split: test - path: validation/eng_Latn-mco_Latn.jsonl.gz split: validation - config_name: eng_Latn-gux_Latn data_files: - path: train/eng_Latn-gux_Latn.jsonl.gz split: train - path: test/eng_Latn-gux_Latn.jsonl.gz split: test - path: validation/eng_Latn-gux_Latn.jsonl.gz split: validation - config_name: eng_Latn-spa_Latn data_files: - path: train/eng_Latn-spa_Latn.jsonl.gz split: train - path: test/eng_Latn-spa_Latn.jsonl.gz split: test - path: validation/eng_Latn-spa_Latn.jsonl.gz split: validation - config_name: eng_Latn-heg_Latn data_files: - path: train/eng_Latn-heg_Latn.jsonl.gz split: train - path: test/eng_Latn-heg_Latn.jsonl.gz split: test - path: validation/eng_Latn-heg_Latn.jsonl.gz split: validation - config_name: eng_Latn-gwi_Latn data_files: - path: train/eng_Latn-gwi_Latn.jsonl.gz split: train - path: test/eng_Latn-gwi_Latn.jsonl.gz split: test - path: validation/eng_Latn-gwi_Latn.jsonl.gz split: validation - config_name: eng_Latn-ttc_Latn data_files: - path: train/eng_Latn-ttc_Latn.jsonl.gz split: train - path: test/eng_Latn-ttc_Latn.jsonl.gz split: test - path: validation/eng_Latn-ttc_Latn.jsonl.gz split: validation - config_name: eng_Latn-mqj_Latn data_files: - path: train/eng_Latn-mqj_Latn.jsonl.gz split: train - path: test/eng_Latn-mqj_Latn.jsonl.gz split: test - path: validation/eng_Latn-mqj_Latn.jsonl.gz split: validation - config_name: eng_Latn-pjt_Latn data_files: - path: train/eng_Latn-pjt_Latn.jsonl.gz split: train - path: test/eng_Latn-pjt_Latn.jsonl.gz split: test - path: validation/eng_Latn-pjt_Latn.jsonl.gz split: validation - config_name: eng_Latn-gui_Latn data_files: - path: train/eng_Latn-gui_Latn.jsonl.gz split: train - path: test/eng_Latn-gui_Latn.jsonl.gz split: test - path: validation/eng_Latn-gui_Latn.jsonl.gz split: validation - config_name: eng_Latn-tel_Telu data_files: - path: train/eng_Latn-tel_Telu.jsonl.gz split: train - path: test/eng_Latn-tel_Telu.jsonl.gz split: test - path: validation/eng_Latn-tel_Telu.jsonl.gz split: validation - config_name: eng_Latn-lbb_Latn data_files: - path: train/eng_Latn-lbb_Latn.jsonl.gz split: train - path: test/eng_Latn-lbb_Latn.jsonl.gz split: test - path: validation/eng_Latn-lbb_Latn.jsonl.gz split: validation - config_name: eng_Latn-cbr_Latn data_files: - path: train/eng_Latn-cbr_Latn.jsonl.gz split: train - path: test/eng_Latn-cbr_Latn.jsonl.gz split: test - path: validation/eng_Latn-cbr_Latn.jsonl.gz split: validation - config_name: eng_Latn-jvn_Latn data_files: - path: train/eng_Latn-jvn_Latn.jsonl.gz split: train - path: test/eng_Latn-jvn_Latn.jsonl.gz split: test - path: validation/eng_Latn-jvn_Latn.jsonl.gz split: validation - config_name: eng_Latn-huu_Latn data_files: - path: train/eng_Latn-huu_Latn.jsonl.gz split: train - path: test/eng_Latn-huu_Latn.jsonl.gz split: test - path: validation/eng_Latn-huu_Latn.jsonl.gz split: validation - config_name: eng_Latn-kyq_Latn data_files: - path: train/eng_Latn-kyq_Latn.jsonl.gz split: train - path: test/eng_Latn-kyq_Latn.jsonl.gz split: test - path: validation/eng_Latn-kyq_Latn.jsonl.gz split: validation - config_name: eng_Latn-lex_Latn data_files: - path: train/eng_Latn-lex_Latn.jsonl.gz split: train - path: test/eng_Latn-lex_Latn.jsonl.gz split: test - path: validation/eng_Latn-lex_Latn.jsonl.gz split: validation - config_name: eng_Latn-lug_Latn data_files: - path: train/eng_Latn-lug_Latn.jsonl.gz split: train - path: test/eng_Latn-lug_Latn.jsonl.gz split: test - path: validation/eng_Latn-lug_Latn.jsonl.gz split: validation - config_name: eng_Latn-tbc_Latn data_files: - path: train/eng_Latn-tbc_Latn.jsonl.gz split: train - path: test/eng_Latn-tbc_Latn.jsonl.gz split: test - path: validation/eng_Latn-tbc_Latn.jsonl.gz split: validation - config_name: eng_Latn-srm_Latn data_files: - path: train/eng_Latn-srm_Latn.jsonl.gz split: train - path: test/eng_Latn-srm_Latn.jsonl.gz split: test - path: validation/eng_Latn-srm_Latn.jsonl.gz split: validation - config_name: eng_Latn-ztq_Latn data_files: - path: train/eng_Latn-ztq_Latn.jsonl.gz split: train - path: test/eng_Latn-ztq_Latn.jsonl.gz split: test - path: validation/eng_Latn-ztq_Latn.jsonl.gz split: validation - config_name: eng_Latn-clu_Latn data_files: - path: train/eng_Latn-clu_Latn.jsonl.gz split: train - path: test/eng_Latn-clu_Latn.jsonl.gz split: test - path: validation/eng_Latn-clu_Latn.jsonl.gz split: validation - config_name: eng_Latn-wol_Latn data_files: - path: train/eng_Latn-wol_Latn.jsonl.gz split: train - path: test/eng_Latn-wol_Latn.jsonl.gz split: test - path: validation/eng_Latn-wol_Latn.jsonl.gz split: validation - config_name: eng_Latn-wrk_Latn data_files: - path: train/eng_Latn-wrk_Latn.jsonl.gz split: train - path: test/eng_Latn-wrk_Latn.jsonl.gz split: test - path: validation/eng_Latn-wrk_Latn.jsonl.gz split: validation - config_name: eng_Latn-ssg_Latn data_files: - path: train/eng_Latn-ssg_Latn.jsonl.gz split: train - path: test/eng_Latn-ssg_Latn.jsonl.gz split: test - path: validation/eng_Latn-ssg_Latn.jsonl.gz split: validation - config_name: eng_Latn-tha_Thai data_files: - path: train/eng_Latn-tha_Thai.jsonl.gz split: train - path: test/eng_Latn-tha_Thai.jsonl.gz split: test - path: validation/eng_Latn-tha_Thai.jsonl.gz split: validation - config_name: eng_Latn-gub_Latn data_files: - path: train/eng_Latn-gub_Latn.jsonl.gz split: train - path: test/eng_Latn-gub_Latn.jsonl.gz split: test - path: validation/eng_Latn-gub_Latn.jsonl.gz split: validation - config_name: eng_Latn-rop_Latn data_files: - path: train/eng_Latn-rop_Latn.jsonl.gz split: train - path: test/eng_Latn-rop_Latn.jsonl.gz split: test - path: validation/eng_Latn-rop_Latn.jsonl.gz split: validation - config_name: eng_Latn-ind_Latn data_files: - path: train/eng_Latn-ind_Latn.jsonl.gz split: train - path: test/eng_Latn-ind_Latn.jsonl.gz split: test - path: validation/eng_Latn-ind_Latn.jsonl.gz split: validation - config_name: eng_Latn-urb_Latn data_files: - path: train/eng_Latn-urb_Latn.jsonl.gz split: train - path: test/eng_Latn-urb_Latn.jsonl.gz split: test - path: validation/eng_Latn-urb_Latn.jsonl.gz split: validation - config_name: eng_Latn-ziw_Latn data_files: - path: train/eng_Latn-ziw_Latn.jsonl.gz split: train - path: test/eng_Latn-ziw_Latn.jsonl.gz split: test - path: validation/eng_Latn-ziw_Latn.jsonl.gz split: validation - config_name: eng_Latn-waj_Latn data_files: - path: train/eng_Latn-waj_Latn.jsonl.gz split: train - path: test/eng_Latn-waj_Latn.jsonl.gz split: test - path: validation/eng_Latn-waj_Latn.jsonl.gz split: validation - config_name: eng_Latn-tku_Latn data_files: - path: train/eng_Latn-tku_Latn.jsonl.gz split: train - path: test/eng_Latn-tku_Latn.jsonl.gz split: test - path: validation/eng_Latn-tku_Latn.jsonl.gz split: validation - config_name: eng_Latn-pao_Latn data_files: - path: train/eng_Latn-pao_Latn.jsonl.gz split: train - path: test/eng_Latn-pao_Latn.jsonl.gz split: test - path: validation/eng_Latn-pao_Latn.jsonl.gz split: validation - config_name: eng_Latn-tet_Latn data_files: - path: train/eng_Latn-tet_Latn.jsonl.gz split: train - path: test/eng_Latn-tet_Latn.jsonl.gz split: test - path: validation/eng_Latn-tet_Latn.jsonl.gz split: validation - config_name: eng_Latn-msc_Latn data_files: - path: train/eng_Latn-msc_Latn.jsonl.gz split: train - path: test/eng_Latn-msc_Latn.jsonl.gz split: test - path: validation/eng_Latn-msc_Latn.jsonl.gz split: validation - config_name: eng_Latn-wal_Ethi data_files: - path: train/eng_Latn-wal_Ethi.jsonl.gz split: train - path: test/eng_Latn-wal_Ethi.jsonl.gz split: test - path: validation/eng_Latn-wal_Ethi.jsonl.gz split: validation - config_name: eng_Latn-bmu_Latn data_files: - path: train/eng_Latn-bmu_Latn.jsonl.gz split: train - path: test/eng_Latn-bmu_Latn.jsonl.gz split: test - path: validation/eng_Latn-bmu_Latn.jsonl.gz split: validation - config_name: eng_Latn-yaq_Latn data_files: - path: train/eng_Latn-yaq_Latn.jsonl.gz split: train - path: test/eng_Latn-yaq_Latn.jsonl.gz split: test - path: validation/eng_Latn-yaq_Latn.jsonl.gz split: validation - config_name: eng_Latn-bgt_Latn data_files: - path: train/eng_Latn-bgt_Latn.jsonl.gz split: train - path: test/eng_Latn-bgt_Latn.jsonl.gz split: test - path: validation/eng_Latn-bgt_Latn.jsonl.gz split: validation - config_name: eng_Latn-atb_Latn data_files: - path: train/eng_Latn-atb_Latn.jsonl.gz split: train - path: test/eng_Latn-atb_Latn.jsonl.gz split: test - path: validation/eng_Latn-atb_Latn.jsonl.gz split: validation - config_name: eng_Latn-apn_Latn data_files: - path: train/eng_Latn-apn_Latn.jsonl.gz split: train - path: test/eng_Latn-apn_Latn.jsonl.gz split: test - path: validation/eng_Latn-apn_Latn.jsonl.gz split: validation - config_name: eng_Latn-por_Latn data_files: - path: train/eng_Latn-por_Latn.jsonl.gz split: train - path: test/eng_Latn-por_Latn.jsonl.gz split: test - path: validation/eng_Latn-por_Latn.jsonl.gz split: validation - config_name: eng_Latn-quf_Latn data_files: - path: train/eng_Latn-quf_Latn.jsonl.gz split: train - path: test/eng_Latn-quf_Latn.jsonl.gz split: test - path: validation/eng_Latn-quf_Latn.jsonl.gz split: validation - config_name: eng_Latn-prf_Latn data_files: - path: train/eng_Latn-prf_Latn.jsonl.gz split: train - path: test/eng_Latn-prf_Latn.jsonl.gz split: test - path: validation/eng_Latn-prf_Latn.jsonl.gz split: validation - config_name: eng_Latn-ndj_Latn data_files: - path: train/eng_Latn-ndj_Latn.jsonl.gz split: train - path: test/eng_Latn-ndj_Latn.jsonl.gz split: test - path: validation/eng_Latn-ndj_Latn.jsonl.gz split: validation - config_name: eng_Latn-hub_Latn data_files: - path: train/eng_Latn-hub_Latn.jsonl.gz split: train - path: test/eng_Latn-hub_Latn.jsonl.gz split: test - path: validation/eng_Latn-hub_Latn.jsonl.gz split: validation - config_name: eng_Latn-kvn_Latn data_files: - path: train/eng_Latn-kvn_Latn.jsonl.gz split: train - path: test/eng_Latn-kvn_Latn.jsonl.gz split: test - path: validation/eng_Latn-kvn_Latn.jsonl.gz split: validation - config_name: eng_Latn-swe_Latn data_files: - path: train/eng_Latn-swe_Latn.jsonl.gz split: train - path: test/eng_Latn-swe_Latn.jsonl.gz split: test - path: validation/eng_Latn-swe_Latn.jsonl.gz split: validation - config_name: eng_Latn-xtd_Latn data_files: - path: train/eng_Latn-xtd_Latn.jsonl.gz split: train - path: test/eng_Latn-xtd_Latn.jsonl.gz split: test - path: validation/eng_Latn-xtd_Latn.jsonl.gz split: validation - config_name: eng_Latn-blz_Latn data_files: - path: train/eng_Latn-blz_Latn.jsonl.gz split: train - path: test/eng_Latn-blz_Latn.jsonl.gz split: test - path: validation/eng_Latn-blz_Latn.jsonl.gz split: validation - config_name: eng_Latn-arb_Arab data_files: - path: train/eng_Latn-arb_Arab.jsonl.gz split: train - path: test/eng_Latn-arb_Arab.jsonl.gz split: test - path: validation/eng_Latn-arb_Arab.jsonl.gz split: validation - config_name: eng_Latn-gdr_Latn data_files: - path: train/eng_Latn-gdr_Latn.jsonl.gz split: train - path: test/eng_Latn-gdr_Latn.jsonl.gz split: test - path: validation/eng_Latn-gdr_Latn.jsonl.gz split: validation - config_name: eng_Latn-ksd_Latn data_files: - path: train/eng_Latn-ksd_Latn.jsonl.gz split: train - path: test/eng_Latn-ksd_Latn.jsonl.gz split: test - path: validation/eng_Latn-ksd_Latn.jsonl.gz split: validation - config_name: eng_Latn-toj_Latn data_files: - path: train/eng_Latn-toj_Latn.jsonl.gz split: train - path: test/eng_Latn-toj_Latn.jsonl.gz split: test - path: validation/eng_Latn-toj_Latn.jsonl.gz split: validation - config_name: eng_Latn-arp_Latn data_files: - path: train/eng_Latn-arp_Latn.jsonl.gz split: train - path: test/eng_Latn-arp_Latn.jsonl.gz split: test - path: validation/eng_Latn-arp_Latn.jsonl.gz split: validation - config_name: eng_Latn-cnt_Latn data_files: - path: train/eng_Latn-cnt_Latn.jsonl.gz split: train - path: test/eng_Latn-cnt_Latn.jsonl.gz split: test - path: validation/eng_Latn-cnt_Latn.jsonl.gz split: validation - config_name: eng_Latn-aoj_Latn data_files: - path: train/eng_Latn-aoj_Latn.jsonl.gz split: train - path: test/eng_Latn-aoj_Latn.jsonl.gz split: test - path: validation/eng_Latn-aoj_Latn.jsonl.gz split: validation - config_name: eng_Latn-fai_Latn data_files: - path: train/eng_Latn-fai_Latn.jsonl.gz split: train - path: test/eng_Latn-fai_Latn.jsonl.gz split: test - path: validation/eng_Latn-fai_Latn.jsonl.gz split: validation - config_name: eng_Latn-far_Latn data_files: - path: train/eng_Latn-far_Latn.jsonl.gz split: train - path: test/eng_Latn-far_Latn.jsonl.gz split: test - path: validation/eng_Latn-far_Latn.jsonl.gz split: validation - config_name: eng_Latn-ons_Latn data_files: - path: train/eng_Latn-ons_Latn.jsonl.gz split: train - path: test/eng_Latn-ons_Latn.jsonl.gz split: test - path: validation/eng_Latn-ons_Latn.jsonl.gz split: validation - config_name: eng_Latn-emi_Latn data_files: - path: train/eng_Latn-emi_Latn.jsonl.gz split: train - path: test/eng_Latn-emi_Latn.jsonl.gz split: test - path: validation/eng_Latn-emi_Latn.jsonl.gz split: validation - config_name: eng_Latn-yre_Latn data_files: - path: train/eng_Latn-yre_Latn.jsonl.gz split: train - path: test/eng_Latn-yre_Latn.jsonl.gz split: test - path: validation/eng_Latn-yre_Latn.jsonl.gz split: validation - config_name: eng_Latn-zpz_Latn data_files: - path: train/eng_Latn-zpz_Latn.jsonl.gz split: train - path: test/eng_Latn-zpz_Latn.jsonl.gz split: test - path: validation/eng_Latn-zpz_Latn.jsonl.gz split: validation - config_name: eng_Latn-yss_Latn data_files: - path: train/eng_Latn-yss_Latn.jsonl.gz split: train - path: test/eng_Latn-yss_Latn.jsonl.gz split: test - path: validation/eng_Latn-yss_Latn.jsonl.gz split: validation - config_name: eng_Latn-kos_Latn data_files: - path: train/eng_Latn-kos_Latn.jsonl.gz split: train - path: test/eng_Latn-kos_Latn.jsonl.gz split: test - path: validation/eng_Latn-kos_Latn.jsonl.gz split: validation - config_name: eng_Latn-reg_Latn data_files: - path: train/eng_Latn-reg_Latn.jsonl.gz split: train - path: test/eng_Latn-reg_Latn.jsonl.gz split: test - path: validation/eng_Latn-reg_Latn.jsonl.gz split: validation - config_name: eng_Latn-rro_Latn data_files: - path: train/eng_Latn-rro_Latn.jsonl.gz split: train - path: test/eng_Latn-rro_Latn.jsonl.gz split: test - path: validation/eng_Latn-rro_Latn.jsonl.gz split: validation - config_name: eng_Latn-apz_Latn data_files: - path: train/eng_Latn-apz_Latn.jsonl.gz split: train - path: test/eng_Latn-apz_Latn.jsonl.gz split: test - path: validation/eng_Latn-apz_Latn.jsonl.gz split: validation - config_name: eng_Latn-boj_Latn data_files: - path: train/eng_Latn-boj_Latn.jsonl.gz split: train - path: test/eng_Latn-boj_Latn.jsonl.gz split: test - path: validation/eng_Latn-boj_Latn.jsonl.gz split: validation - config_name: eng_Latn-hla_Latn data_files: - path: train/eng_Latn-hla_Latn.jsonl.gz split: train - path: test/eng_Latn-hla_Latn.jsonl.gz split: test - path: validation/eng_Latn-hla_Latn.jsonl.gz split: validation - config_name: eng_Latn-gyr_Latn data_files: - path: train/eng_Latn-gyr_Latn.jsonl.gz split: train - path: test/eng_Latn-gyr_Latn.jsonl.gz split: test - path: validation/eng_Latn-gyr_Latn.jsonl.gz split: validation - config_name: eng_Latn-ukr_Cyrl data_files: - path: train/eng_Latn-ukr_Cyrl.jsonl.gz split: train - path: test/eng_Latn-ukr_Cyrl.jsonl.gz split: test - path: validation/eng_Latn-ukr_Cyrl.jsonl.gz split: validation - config_name: eng_Latn-gvs_Latn data_files: - path: train/eng_Latn-gvs_Latn.jsonl.gz split: train - path: test/eng_Latn-gvs_Latn.jsonl.gz split: test - path: validation/eng_Latn-gvs_Latn.jsonl.gz split: validation - config_name: eng_Latn-mil_Latn data_files: - path: train/eng_Latn-mil_Latn.jsonl.gz split: train - path: test/eng_Latn-mil_Latn.jsonl.gz split: test - path: validation/eng_Latn-mil_Latn.jsonl.gz split: validation - config_name: eng_Latn-gul_Latn data_files: - path: train/eng_Latn-gul_Latn.jsonl.gz split: train - path: test/eng_Latn-gul_Latn.jsonl.gz split: test - path: validation/eng_Latn-gul_Latn.jsonl.gz split: validation - config_name: eng_Latn-ood_Latn data_files: - path: train/eng_Latn-ood_Latn.jsonl.gz split: train - path: test/eng_Latn-ood_Latn.jsonl.gz split: test - path: validation/eng_Latn-ood_Latn.jsonl.gz split: validation - config_name: eng_Latn-ewe_Latn data_files: - path: train/eng_Latn-ewe_Latn.jsonl.gz split: train - path: test/eng_Latn-ewe_Latn.jsonl.gz split: test - path: validation/eng_Latn-ewe_Latn.jsonl.gz split: validation - config_name: eng_Latn-qul_Latn data_files: - path: train/eng_Latn-qul_Latn.jsonl.gz split: train - path: test/eng_Latn-qul_Latn.jsonl.gz split: test - path: validation/eng_Latn-qul_Latn.jsonl.gz split: validation - config_name: eng_Latn-kbm_Latn data_files: - path: train/eng_Latn-kbm_Latn.jsonl.gz split: train - path: test/eng_Latn-kbm_Latn.jsonl.gz split: test - path: validation/eng_Latn-kbm_Latn.jsonl.gz split: validation - config_name: eng_Latn-mih_Latn data_files: - path: train/eng_Latn-mih_Latn.jsonl.gz split: train - path: test/eng_Latn-mih_Latn.jsonl.gz split: test - path: validation/eng_Latn-mih_Latn.jsonl.gz split: validation - config_name: eng_Latn-maq_Latn data_files: - path: train/eng_Latn-maq_Latn.jsonl.gz split: train - path: test/eng_Latn-maq_Latn.jsonl.gz split: test - path: validation/eng_Latn-maq_Latn.jsonl.gz split: validation - config_name: eng_Latn-bbr_Latn data_files: - path: train/eng_Latn-bbr_Latn.jsonl.gz split: train - path: test/eng_Latn-bbr_Latn.jsonl.gz split: test - path: validation/eng_Latn-bbr_Latn.jsonl.gz split: validation - config_name: eng_Latn-cbu_Latn data_files: - path: train/eng_Latn-cbu_Latn.jsonl.gz split: train - path: test/eng_Latn-cbu_Latn.jsonl.gz split: test - path: validation/eng_Latn-cbu_Latn.jsonl.gz split: validation - config_name: eng_Latn-meq_Latn data_files: - path: train/eng_Latn-meq_Latn.jsonl.gz split: train - path: test/eng_Latn-meq_Latn.jsonl.gz split: test - path: validation/eng_Latn-meq_Latn.jsonl.gz split: validation - config_name: eng_Latn-bmk_Latn data_files: - path: train/eng_Latn-bmk_Latn.jsonl.gz split: train - path: test/eng_Latn-bmk_Latn.jsonl.gz split: test - path: validation/eng_Latn-bmk_Latn.jsonl.gz split: validation - config_name: eng_Latn-hui_Latn data_files: - path: train/eng_Latn-hui_Latn.jsonl.gz split: train - path: test/eng_Latn-hui_Latn.jsonl.gz split: test - path: validation/eng_Latn-hui_Latn.jsonl.gz split: validation - config_name: eng_Latn-tgl_Latn data_files: - path: train/eng_Latn-tgl_Latn.jsonl.gz split: train - path: test/eng_Latn-tgl_Latn.jsonl.gz split: test - path: validation/eng_Latn-tgl_Latn.jsonl.gz split: validation - config_name: eng_Latn-lgl_Latn data_files: - path: train/eng_Latn-lgl_Latn.jsonl.gz split: train - path: test/eng_Latn-lgl_Latn.jsonl.gz split: test - path: validation/eng_Latn-lgl_Latn.jsonl.gz split: validation - config_name: eng_Latn-zpq_Latn data_files: - path: train/eng_Latn-zpq_Latn.jsonl.gz split: train - path: test/eng_Latn-zpq_Latn.jsonl.gz split: test - path: validation/eng_Latn-zpq_Latn.jsonl.gz split: validation - config_name: eng_Latn-mti_Latn data_files: - path: train/eng_Latn-mti_Latn.jsonl.gz split: train - path: test/eng_Latn-mti_Latn.jsonl.gz split: test - path: validation/eng_Latn-mti_Latn.jsonl.gz split: validation - config_name: eng_Latn-pah_Latn data_files: - path: train/eng_Latn-pah_Latn.jsonl.gz split: train - path: test/eng_Latn-pah_Latn.jsonl.gz split: test - path: validation/eng_Latn-pah_Latn.jsonl.gz split: validation - config_name: eng_Latn-nch_Latn data_files: - path: train/eng_Latn-nch_Latn.jsonl.gz split: train - path: test/eng_Latn-nch_Latn.jsonl.gz split: test - path: validation/eng_Latn-nch_Latn.jsonl.gz split: validation - config_name: eng_Latn-mjc_Latn data_files: - path: train/eng_Latn-mjc_Latn.jsonl.gz split: train - path: test/eng_Latn-mjc_Latn.jsonl.gz split: test - path: validation/eng_Latn-mjc_Latn.jsonl.gz split: validation - config_name: eng_Latn-zty_Latn data_files: - path: train/eng_Latn-zty_Latn.jsonl.gz split: train - path: test/eng_Latn-zty_Latn.jsonl.gz split: test - path: validation/eng_Latn-zty_Latn.jsonl.gz split: validation - config_name: eng_Latn-ksj_Latn data_files: - path: train/eng_Latn-ksj_Latn.jsonl.gz split: train - path: test/eng_Latn-ksj_Latn.jsonl.gz split: test - path: validation/eng_Latn-ksj_Latn.jsonl.gz split: validation - config_name: eng_Latn-nvm_Latn data_files: - path: train/eng_Latn-nvm_Latn.jsonl.gz split: train - path: test/eng_Latn-nvm_Latn.jsonl.gz split: test - path: validation/eng_Latn-nvm_Latn.jsonl.gz split: validation - config_name: eng_Latn-kyc_Latn data_files: - path: train/eng_Latn-kyc_Latn.jsonl.gz split: train - path: test/eng_Latn-kyc_Latn.jsonl.gz split: test - path: validation/eng_Latn-kyc_Latn.jsonl.gz split: validation - config_name: eng_Latn-bao_Latn data_files: - path: train/eng_Latn-bao_Latn.jsonl.gz split: train - path: test/eng_Latn-bao_Latn.jsonl.gz split: test - path: validation/eng_Latn-bao_Latn.jsonl.gz split: validation - config_name: eng_Latn-zas_Latn data_files: - path: train/eng_Latn-zas_Latn.jsonl.gz split: train - path: test/eng_Latn-zas_Latn.jsonl.gz split: test - path: validation/eng_Latn-zas_Latn.jsonl.gz split: validation - config_name: eng_Latn-djr_Latn data_files: - path: train/eng_Latn-djr_Latn.jsonl.gz split: train - path: test/eng_Latn-djr_Latn.jsonl.gz split: test - path: validation/eng_Latn-djr_Latn.jsonl.gz split: validation - config_name: eng_Latn-bpr_Latn data_files: - path: train/eng_Latn-bpr_Latn.jsonl.gz split: train - path: test/eng_Latn-bpr_Latn.jsonl.gz split: test - path: validation/eng_Latn-bpr_Latn.jsonl.gz split: validation - config_name: eng_Latn-ndg_Latn data_files: - path: train/eng_Latn-ndg_Latn.jsonl.gz split: train - path: test/eng_Latn-ndg_Latn.jsonl.gz split: test - path: validation/eng_Latn-ndg_Latn.jsonl.gz split: validation - config_name: eng_Latn-ots_Latn data_files: - path: train/eng_Latn-ots_Latn.jsonl.gz split: train - path: test/eng_Latn-ots_Latn.jsonl.gz split: test - path: validation/eng_Latn-ots_Latn.jsonl.gz split: validation - config_name: eng_Latn-miz_Latn data_files: - path: train/eng_Latn-miz_Latn.jsonl.gz split: train - path: test/eng_Latn-miz_Latn.jsonl.gz split: test - path: validation/eng_Latn-miz_Latn.jsonl.gz split: validation - config_name: eng_Latn-cco_Latn data_files: - path: train/eng_Latn-cco_Latn.jsonl.gz split: train - path: test/eng_Latn-cco_Latn.jsonl.gz split: test - path: validation/eng_Latn-cco_Latn.jsonl.gz split: validation - config_name: eng_Latn-mbc_Latn data_files: - path: train/eng_Latn-mbc_Latn.jsonl.gz split: train - path: test/eng_Latn-mbc_Latn.jsonl.gz split: test - path: validation/eng_Latn-mbc_Latn.jsonl.gz split: validation - config_name: eng_Latn-myy_Latn data_files: - path: train/eng_Latn-myy_Latn.jsonl.gz split: train - path: test/eng_Latn-myy_Latn.jsonl.gz split: test - path: validation/eng_Latn-myy_Latn.jsonl.gz split: validation - config_name: eng_Latn-att_Latn data_files: - path: train/eng_Latn-att_Latn.jsonl.gz split: train - path: test/eng_Latn-att_Latn.jsonl.gz split: test - path: validation/eng_Latn-att_Latn.jsonl.gz split: validation - config_name: eng_Latn-aly_Latn data_files: - path: train/eng_Latn-aly_Latn.jsonl.gz split: train - path: test/eng_Latn-aly_Latn.jsonl.gz split: test - path: validation/eng_Latn-aly_Latn.jsonl.gz split: validation - config_name: eng_Latn-mgh_Latn data_files: - path: train/eng_Latn-mgh_Latn.jsonl.gz split: train - path: test/eng_Latn-mgh_Latn.jsonl.gz split: test - path: validation/eng_Latn-mgh_Latn.jsonl.gz split: validation - config_name: eng_Latn-mqb_Latn data_files: - path: train/eng_Latn-mqb_Latn.jsonl.gz split: train - path: test/eng_Latn-mqb_Latn.jsonl.gz split: test - path: validation/eng_Latn-mqb_Latn.jsonl.gz split: validation - config_name: eng_Latn-sps_Latn data_files: - path: train/eng_Latn-sps_Latn.jsonl.gz split: train - path: test/eng_Latn-sps_Latn.jsonl.gz split: test - path: validation/eng_Latn-sps_Latn.jsonl.gz split: validation - config_name: eng_Latn-wbi_Latn data_files: - path: train/eng_Latn-wbi_Latn.jsonl.gz split: train - path: test/eng_Latn-wbi_Latn.jsonl.gz split: test - path: validation/eng_Latn-wbi_Latn.jsonl.gz split: validation - config_name: eng_Latn-rai_Latn data_files: - path: train/eng_Latn-rai_Latn.jsonl.gz split: train - path: test/eng_Latn-rai_Latn.jsonl.gz split: test - path: validation/eng_Latn-rai_Latn.jsonl.gz split: validation - config_name: eng_Latn-knf_Latn data_files: - path: train/eng_Latn-knf_Latn.jsonl.gz split: train - path: test/eng_Latn-knf_Latn.jsonl.gz split: test - path: validation/eng_Latn-knf_Latn.jsonl.gz split: validation - config_name: eng_Latn-txq_Latn data_files: - path: train/eng_Latn-txq_Latn.jsonl.gz split: train - path: test/eng_Latn-txq_Latn.jsonl.gz split: test - path: validation/eng_Latn-txq_Latn.jsonl.gz split: validation - config_name: eng_Latn-cuk_Latn data_files: - path: train/eng_Latn-cuk_Latn.jsonl.gz split: train - path: test/eng_Latn-cuk_Latn.jsonl.gz split: test - path: validation/eng_Latn-cuk_Latn.jsonl.gz split: validation - config_name: eng_Latn-tew_Latn data_files: - path: train/eng_Latn-tew_Latn.jsonl.gz split: train - path: test/eng_Latn-tew_Latn.jsonl.gz split: test - path: validation/eng_Latn-tew_Latn.jsonl.gz split: validation - config_name: eng_Latn-aia_Latn data_files: - path: train/eng_Latn-aia_Latn.jsonl.gz split: train - path: test/eng_Latn-aia_Latn.jsonl.gz split: test - path: validation/eng_Latn-aia_Latn.jsonl.gz split: validation - config_name: eng_Latn-ghs_Latn data_files: - path: train/eng_Latn-ghs_Latn.jsonl.gz split: train - path: test/eng_Latn-ghs_Latn.jsonl.gz split: test - path: validation/eng_Latn-ghs_Latn.jsonl.gz split: validation - config_name: eng_Latn-kne_Latn data_files: - path: train/eng_Latn-kne_Latn.jsonl.gz split: train - path: test/eng_Latn-kne_Latn.jsonl.gz split: test - path: validation/eng_Latn-kne_Latn.jsonl.gz split: validation - config_name: eng_Latn-snp_Latn data_files: - path: train/eng_Latn-snp_Latn.jsonl.gz split: train - path: test/eng_Latn-snp_Latn.jsonl.gz split: test - path: validation/eng_Latn-snp_Latn.jsonl.gz split: validation - config_name: eng_Latn-yon_Latn data_files: - path: train/eng_Latn-yon_Latn.jsonl.gz split: train - path: test/eng_Latn-yon_Latn.jsonl.gz split: test - path: validation/eng_Latn-yon_Latn.jsonl.gz split: validation - config_name: eng_Latn-rkb_Latn data_files: - path: train/eng_Latn-rkb_Latn.jsonl.gz split: train - path: test/eng_Latn-rkb_Latn.jsonl.gz split: test - path: validation/eng_Latn-rkb_Latn.jsonl.gz split: validation - config_name: eng_Latn-mam_Latn data_files: - path: train/eng_Latn-mam_Latn.jsonl.gz split: train - path: test/eng_Latn-mam_Latn.jsonl.gz split: test - path: validation/eng_Latn-mam_Latn.jsonl.gz split: validation - config_name: eng_Latn-ffm_Latn data_files: - path: train/eng_Latn-ffm_Latn.jsonl.gz split: train - path: test/eng_Latn-ffm_Latn.jsonl.gz split: test - path: validation/eng_Latn-ffm_Latn.jsonl.gz split: validation - config_name: eng_Latn-tbo_Latn data_files: - path: train/eng_Latn-tbo_Latn.jsonl.gz split: train - path: test/eng_Latn-tbo_Latn.jsonl.gz split: test - path: validation/eng_Latn-tbo_Latn.jsonl.gz split: validation - config_name: eng_Latn-cta_Latn data_files: - path: train/eng_Latn-cta_Latn.jsonl.gz split: train - path: test/eng_Latn-cta_Latn.jsonl.gz split: test - path: validation/eng_Latn-cta_Latn.jsonl.gz split: validation - config_name: eng_Latn-mca_Latn data_files: - path: train/eng_Latn-mca_Latn.jsonl.gz split: train - path: test/eng_Latn-mca_Latn.jsonl.gz split: test - path: validation/eng_Latn-mca_Latn.jsonl.gz split: validation - config_name: eng_Latn-smk_Latn data_files: - path: train/eng_Latn-smk_Latn.jsonl.gz split: train - path: test/eng_Latn-smk_Latn.jsonl.gz split: test - path: validation/eng_Latn-smk_Latn.jsonl.gz split: validation - config_name: eng_Latn-bqc_Latn data_files: - path: train/eng_Latn-bqc_Latn.jsonl.gz split: train - path: test/eng_Latn-bqc_Latn.jsonl.gz split: test - path: validation/eng_Latn-bqc_Latn.jsonl.gz split: validation - config_name: eng_Latn-khz_Latn data_files: - path: train/eng_Latn-khz_Latn.jsonl.gz split: train - path: test/eng_Latn-khz_Latn.jsonl.gz split: test - path: validation/eng_Latn-khz_Latn.jsonl.gz split: validation - config_name: eng_Latn-ceb_Latn data_files: - path: train/eng_Latn-ceb_Latn.jsonl.gz split: train - path: test/eng_Latn-ceb_Latn.jsonl.gz split: test - path: validation/eng_Latn-ceb_Latn.jsonl.gz split: validation - config_name: eng_Latn-nyu_Latn data_files: - path: train/eng_Latn-nyu_Latn.jsonl.gz split: train - path: test/eng_Latn-nyu_Latn.jsonl.gz split: test - path: validation/eng_Latn-nyu_Latn.jsonl.gz split: validation - config_name: eng_Latn-hlt_Latn data_files: - path: train/eng_Latn-hlt_Latn.jsonl.gz split: train - path: test/eng_Latn-hlt_Latn.jsonl.gz split: test - path: validation/eng_Latn-hlt_Latn.jsonl.gz split: validation - config_name: eng_Latn-qvw_Latn data_files: - path: train/eng_Latn-qvw_Latn.jsonl.gz split: train - path: test/eng_Latn-qvw_Latn.jsonl.gz split: test - path: validation/eng_Latn-qvw_Latn.jsonl.gz split: validation - config_name: eng_Latn-poy_Latn data_files: - path: train/eng_Latn-poy_Latn.jsonl.gz split: train - path: test/eng_Latn-poy_Latn.jsonl.gz split: test - path: validation/eng_Latn-poy_Latn.jsonl.gz split: validation - config_name: eng_Latn-jiv_Latn data_files: - path: train/eng_Latn-jiv_Latn.jsonl.gz split: train - path: test/eng_Latn-jiv_Latn.jsonl.gz split: test - path: validation/eng_Latn-jiv_Latn.jsonl.gz split: validation - config_name: eng_Latn-mna_Latn data_files: - path: train/eng_Latn-mna_Latn.jsonl.gz split: train - path: test/eng_Latn-mna_Latn.jsonl.gz split: test - path: validation/eng_Latn-mna_Latn.jsonl.gz split: validation - config_name: eng_Latn-xsi_Latn data_files: - path: train/eng_Latn-xsi_Latn.jsonl.gz split: train - path: test/eng_Latn-xsi_Latn.jsonl.gz split: test - path: validation/eng_Latn-xsi_Latn.jsonl.gz split: validation - config_name: eng_Latn-crx_Latn data_files: - path: train/eng_Latn-crx_Latn.jsonl.gz split: train - path: test/eng_Latn-crx_Latn.jsonl.gz split: test - path: validation/eng_Latn-crx_Latn.jsonl.gz split: validation - config_name: eng_Latn-apb_Latn data_files: - path: train/eng_Latn-apb_Latn.jsonl.gz split: train - path: test/eng_Latn-apb_Latn.jsonl.gz split: test - path: validation/eng_Latn-apb_Latn.jsonl.gz split: validation - config_name: eng_Latn-imo_Latn data_files: - path: train/eng_Latn-imo_Latn.jsonl.gz split: train - path: test/eng_Latn-imo_Latn.jsonl.gz split: test - path: validation/eng_Latn-imo_Latn.jsonl.gz split: validation - config_name: eng_Latn-fue_Latn data_files: - path: train/eng_Latn-fue_Latn.jsonl.gz split: train - path: test/eng_Latn-fue_Latn.jsonl.gz split: test - path: validation/eng_Latn-fue_Latn.jsonl.gz split: validation - config_name: eng_Latn-bhl_Latn data_files: - path: train/eng_Latn-bhl_Latn.jsonl.gz split: train - path: test/eng_Latn-bhl_Latn.jsonl.gz split: test - path: validation/eng_Latn-bhl_Latn.jsonl.gz split: validation - config_name: eng_Latn-tim_Latn data_files: - path: train/eng_Latn-tim_Latn.jsonl.gz split: train - path: test/eng_Latn-tim_Latn.jsonl.gz split: test - path: validation/eng_Latn-tim_Latn.jsonl.gz split: validation - config_name: eng_Latn-mgw_Latn data_files: - path: train/eng_Latn-mgw_Latn.jsonl.gz split: train - path: test/eng_Latn-mgw_Latn.jsonl.gz split: test - path: validation/eng_Latn-mgw_Latn.jsonl.gz split: validation - config_name: eng_Latn-taj_Deva data_files: - path: train/eng_Latn-taj_Deva.jsonl.gz split: train - path: test/eng_Latn-taj_Deva.jsonl.gz split: test - path: validation/eng_Latn-taj_Deva.jsonl.gz split: validation - config_name: eng_Latn-djk_Latn data_files: - path: train/eng_Latn-djk_Latn.jsonl.gz split: train - path: test/eng_Latn-djk_Latn.jsonl.gz split: test - path: validation/eng_Latn-djk_Latn.jsonl.gz split: validation - config_name: eng_Latn-ruf_Latn data_files: - path: train/eng_Latn-ruf_Latn.jsonl.gz split: train - path: test/eng_Latn-ruf_Latn.jsonl.gz split: test - path: validation/eng_Latn-ruf_Latn.jsonl.gz split: validation - config_name: eng_Latn-bqp_Latn data_files: - path: train/eng_Latn-bqp_Latn.jsonl.gz split: train - path: test/eng_Latn-bqp_Latn.jsonl.gz split: test - path: validation/eng_Latn-bqp_Latn.jsonl.gz split: validation - config_name: eng_Latn-adz_Latn data_files: - path: train/eng_Latn-adz_Latn.jsonl.gz split: train - path: test/eng_Latn-adz_Latn.jsonl.gz split: test - path: validation/eng_Latn-adz_Latn.jsonl.gz split: validation - config_name: eng_Latn-bmr_Latn data_files: - path: train/eng_Latn-bmr_Latn.jsonl.gz split: train - path: test/eng_Latn-bmr_Latn.jsonl.gz split: test - path: validation/eng_Latn-bmr_Latn.jsonl.gz split: validation - config_name: eng_Latn-ata_Latn data_files: - path: train/eng_Latn-ata_Latn.jsonl.gz split: train - path: test/eng_Latn-ata_Latn.jsonl.gz split: test - path: validation/eng_Latn-ata_Latn.jsonl.gz split: validation - config_name: eng_Latn-mio_Latn data_files: - path: train/eng_Latn-mio_Latn.jsonl.gz split: train - path: test/eng_Latn-mio_Latn.jsonl.gz split: test - path: validation/eng_Latn-mio_Latn.jsonl.gz split: validation - config_name: eng_Latn-pad_Latn data_files: - path: train/eng_Latn-pad_Latn.jsonl.gz split: train - path: test/eng_Latn-pad_Latn.jsonl.gz split: test - path: validation/eng_Latn-pad_Latn.jsonl.gz split: validation - config_name: eng_Latn-qxh_Latn data_files: - path: train/eng_Latn-qxh_Latn.jsonl.gz split: train - path: test/eng_Latn-qxh_Latn.jsonl.gz split: test - path: validation/eng_Latn-qxh_Latn.jsonl.gz split: validation - config_name: eng_Latn-tfr_Latn data_files: - path: train/eng_Latn-tfr_Latn.jsonl.gz split: train - path: test/eng_Latn-tfr_Latn.jsonl.gz split: test - path: validation/eng_Latn-tfr_Latn.jsonl.gz split: validation - config_name: eng_Latn-mie_Latn data_files: - path: train/eng_Latn-mie_Latn.jsonl.gz split: train - path: test/eng_Latn-mie_Latn.jsonl.gz split: test - path: validation/eng_Latn-mie_Latn.jsonl.gz split: validation - config_name: eng_Latn-tpi_Latn data_files: - path: train/eng_Latn-tpi_Latn.jsonl.gz split: train - path: test/eng_Latn-tpi_Latn.jsonl.gz split: test - path: validation/eng_Latn-tpi_Latn.jsonl.gz split: validation - config_name: eng_Latn-fuh_Latn data_files: - path: train/eng_Latn-fuh_Latn.jsonl.gz split: train - path: test/eng_Latn-fuh_Latn.jsonl.gz split: test - path: validation/eng_Latn-fuh_Latn.jsonl.gz split: validation - config_name: eng_Latn-dgr_Latn data_files: - path: train/eng_Latn-dgr_Latn.jsonl.gz split: train - path: test/eng_Latn-dgr_Latn.jsonl.gz split: test - path: validation/eng_Latn-dgr_Latn.jsonl.gz split: validation - config_name: eng_Latn-bch_Latn data_files: - path: train/eng_Latn-bch_Latn.jsonl.gz split: train - path: test/eng_Latn-bch_Latn.jsonl.gz split: test - path: validation/eng_Latn-bch_Latn.jsonl.gz split: validation - config_name: eng_Latn-mcr_Latn data_files: - path: train/eng_Latn-mcr_Latn.jsonl.gz split: train - path: test/eng_Latn-mcr_Latn.jsonl.gz split: test - path: validation/eng_Latn-mcr_Latn.jsonl.gz split: validation - config_name: eng_Latn-bkx_Latn data_files: - path: train/eng_Latn-bkx_Latn.jsonl.gz split: train - path: test/eng_Latn-bkx_Latn.jsonl.gz split: test - path: validation/eng_Latn-bkx_Latn.jsonl.gz split: validation - config_name: eng_Latn-agr_Latn data_files: - path: train/eng_Latn-agr_Latn.jsonl.gz split: train - path: test/eng_Latn-agr_Latn.jsonl.gz split: test - path: validation/eng_Latn-agr_Latn.jsonl.gz split: validation - config_name: eng_Latn-chq_Latn data_files: - path: train/eng_Latn-chq_Latn.jsonl.gz split: train - path: test/eng_Latn-chq_Latn.jsonl.gz split: test - path: validation/eng_Latn-chq_Latn.jsonl.gz split: validation - config_name: eng_Latn-rwo_Latn data_files: - path: train/eng_Latn-rwo_Latn.jsonl.gz split: train - path: test/eng_Latn-rwo_Latn.jsonl.gz split: test - path: validation/eng_Latn-rwo_Latn.jsonl.gz split: validation - config_name: eng_Latn-esk_Latn data_files: - path: train/eng_Latn-esk_Latn.jsonl.gz split: train - path: test/eng_Latn-esk_Latn.jsonl.gz split: test - path: validation/eng_Latn-esk_Latn.jsonl.gz split: validation - config_name: eng_Latn-zpl_Latn data_files: - path: train/eng_Latn-zpl_Latn.jsonl.gz split: train - path: test/eng_Latn-zpl_Latn.jsonl.gz split: test - path: validation/eng_Latn-zpl_Latn.jsonl.gz split: validation - config_name: eng_Latn-bjr_Latn data_files: - path: train/eng_Latn-bjr_Latn.jsonl.gz split: train - path: test/eng_Latn-bjr_Latn.jsonl.gz split: test - path: validation/eng_Latn-bjr_Latn.jsonl.gz split: validation - config_name: eng_Latn-kiw_Latn data_files: - path: train/eng_Latn-kiw_Latn.jsonl.gz split: train - path: test/eng_Latn-kiw_Latn.jsonl.gz split: test - path: validation/eng_Latn-kiw_Latn.jsonl.gz split: validation - config_name: eng_Latn-azg_Latn data_files: - path: train/eng_Latn-azg_Latn.jsonl.gz split: train - path: test/eng_Latn-azg_Latn.jsonl.gz split: test - path: validation/eng_Latn-azg_Latn.jsonl.gz split: validation - config_name: eng_Latn-mbb_Latn data_files: - path: train/eng_Latn-mbb_Latn.jsonl.gz split: train - path: test/eng_Latn-mbb_Latn.jsonl.gz split: test - path: validation/eng_Latn-mbb_Latn.jsonl.gz split: validation - config_name: eng_Latn-knj_Latn data_files: - path: train/eng_Latn-knj_Latn.jsonl.gz split: train - path: test/eng_Latn-knj_Latn.jsonl.gz split: test - path: validation/eng_Latn-knj_Latn.jsonl.gz split: validation - config_name: eng_Latn-cao_Latn data_files: - path: train/eng_Latn-cao_Latn.jsonl.gz split: train - path: test/eng_Latn-cao_Latn.jsonl.gz split: test - path: validation/eng_Latn-cao_Latn.jsonl.gz split: validation - config_name: eng_Latn-dji_Latn data_files: - path: train/eng_Latn-dji_Latn.jsonl.gz split: train - path: test/eng_Latn-dji_Latn.jsonl.gz split: test - path: validation/eng_Latn-dji_Latn.jsonl.gz split: validation - config_name: eng_Latn-bss_Latn data_files: - path: train/eng_Latn-bss_Latn.jsonl.gz split: train - path: test/eng_Latn-bss_Latn.jsonl.gz split: test - path: validation/eng_Latn-bss_Latn.jsonl.gz split: validation - config_name: eng_Latn-bgs_Latn data_files: - path: train/eng_Latn-bgs_Latn.jsonl.gz split: train - path: test/eng_Latn-bgs_Latn.jsonl.gz split: test - path: validation/eng_Latn-bgs_Latn.jsonl.gz split: validation - config_name: eng_Latn-mek_Latn data_files: - path: train/eng_Latn-mek_Latn.jsonl.gz split: train - path: test/eng_Latn-mek_Latn.jsonl.gz split: test - path: validation/eng_Latn-mek_Latn.jsonl.gz split: validation - config_name: eng_Latn-yuj_Latn data_files: - path: train/eng_Latn-yuj_Latn.jsonl.gz split: train - path: test/eng_Latn-yuj_Latn.jsonl.gz split: test - path: validation/eng_Latn-yuj_Latn.jsonl.gz split: validation - config_name: eng_Latn-tpt_Latn data_files: - path: train/eng_Latn-tpt_Latn.jsonl.gz split: train - path: test/eng_Latn-tpt_Latn.jsonl.gz split: test - path: validation/eng_Latn-tpt_Latn.jsonl.gz split: validation - config_name: eng_Latn-zos_Latn data_files: - path: train/eng_Latn-zos_Latn.jsonl.gz split: train - path: test/eng_Latn-zos_Latn.jsonl.gz split: test - path: validation/eng_Latn-zos_Latn.jsonl.gz split: validation - config_name: eng_Latn-blw_Latn data_files: - path: train/eng_Latn-blw_Latn.jsonl.gz split: train - path: test/eng_Latn-blw_Latn.jsonl.gz split: test - path: validation/eng_Latn-blw_Latn.jsonl.gz split: validation - config_name: eng_Latn-viv_Latn data_files: - path: train/eng_Latn-viv_Latn.jsonl.gz split: train - path: test/eng_Latn-viv_Latn.jsonl.gz split: test - path: validation/eng_Latn-viv_Latn.jsonl.gz split: validation - config_name: eng_Latn-ikw_Latn data_files: - path: train/eng_Latn-ikw_Latn.jsonl.gz split: train - path: test/eng_Latn-ikw_Latn.jsonl.gz split: test - path: validation/eng_Latn-ikw_Latn.jsonl.gz split: validation - config_name: eng_Latn-tue_Latn data_files: - path: train/eng_Latn-tue_Latn.jsonl.gz split: train - path: test/eng_Latn-tue_Latn.jsonl.gz split: test - path: validation/eng_Latn-tue_Latn.jsonl.gz split: validation - config_name: eng_Latn-uvh_Latn data_files: - path: train/eng_Latn-uvh_Latn.jsonl.gz split: train - path: test/eng_Latn-uvh_Latn.jsonl.gz split: test - path: validation/eng_Latn-uvh_Latn.jsonl.gz split: validation - config_name: eng_Latn-yap_Latn data_files: - path: train/eng_Latn-yap_Latn.jsonl.gz split: train - path: test/eng_Latn-yap_Latn.jsonl.gz split: test - path: validation/eng_Latn-yap_Latn.jsonl.gz split: validation - config_name: eng_Latn-nca_Latn data_files: - path: train/eng_Latn-nca_Latn.jsonl.gz split: train - path: test/eng_Latn-nca_Latn.jsonl.gz split: test - path: validation/eng_Latn-nca_Latn.jsonl.gz split: validation - config_name: eng_Latn-luo_Latn data_files: - path: train/eng_Latn-luo_Latn.jsonl.gz split: train - path: test/eng_Latn-luo_Latn.jsonl.gz split: test - path: validation/eng_Latn-luo_Latn.jsonl.gz split: validation - config_name: eng_Latn-tmd_Latn data_files: - path: train/eng_Latn-tmd_Latn.jsonl.gz split: train - path: test/eng_Latn-tmd_Latn.jsonl.gz split: test - path: validation/eng_Latn-tmd_Latn.jsonl.gz split: validation - config_name: eng_Latn-txu_Latn data_files: - path: train/eng_Latn-txu_Latn.jsonl.gz split: train - path: test/eng_Latn-txu_Latn.jsonl.gz split: test - path: validation/eng_Latn-txu_Latn.jsonl.gz split: validation - config_name: eng_Latn-yor_Latn data_files: - path: train/eng_Latn-yor_Latn.jsonl.gz split: train - path: test/eng_Latn-yor_Latn.jsonl.gz split: test - path: validation/eng_Latn-yor_Latn.jsonl.gz split: validation - config_name: eng_Latn-amx_Latn data_files: - path: train/eng_Latn-amx_Latn.jsonl.gz split: train - path: test/eng_Latn-amx_Latn.jsonl.gz split: test - path: validation/eng_Latn-amx_Latn.jsonl.gz split: validation - config_name: eng_Latn-uli_Latn data_files: - path: train/eng_Latn-uli_Latn.jsonl.gz split: train - path: test/eng_Latn-uli_Latn.jsonl.gz split: test - path: validation/eng_Latn-uli_Latn.jsonl.gz split: validation - config_name: eng_Latn-dov_Latn data_files: - path: train/eng_Latn-dov_Latn.jsonl.gz split: train - path: test/eng_Latn-dov_Latn.jsonl.gz split: test - path: validation/eng_Latn-dov_Latn.jsonl.gz split: validation - config_name: eng_Latn-huv_Latn data_files: - path: train/eng_Latn-huv_Latn.jsonl.gz split: train - path: test/eng_Latn-huv_Latn.jsonl.gz split: test - path: validation/eng_Latn-huv_Latn.jsonl.gz split: validation - config_name: eng_Latn-msk_Latn data_files: - path: train/eng_Latn-msk_Latn.jsonl.gz split: train - path: test/eng_Latn-msk_Latn.jsonl.gz split: test - path: validation/eng_Latn-msk_Latn.jsonl.gz split: validation - config_name: eng_Latn-twi_Latn data_files: - path: train/eng_Latn-twi_Latn.jsonl.gz split: train - path: test/eng_Latn-twi_Latn.jsonl.gz split: test - path: validation/eng_Latn-twi_Latn.jsonl.gz split: validation - config_name: eng_Latn-aer_Latn data_files: - path: train/eng_Latn-aer_Latn.jsonl.gz split: train - path: test/eng_Latn-aer_Latn.jsonl.gz split: test - path: validation/eng_Latn-aer_Latn.jsonl.gz split: validation - config_name: eng_Latn-pib_Latn data_files: - path: train/eng_Latn-pib_Latn.jsonl.gz split: train - path: test/eng_Latn-pib_Latn.jsonl.gz split: test - path: validation/eng_Latn-pib_Latn.jsonl.gz split: validation - config_name: eng_Latn-ter_Latn data_files: - path: train/eng_Latn-ter_Latn.jsonl.gz split: train - path: test/eng_Latn-ter_Latn.jsonl.gz split: test - path: validation/eng_Latn-ter_Latn.jsonl.gz split: validation - config_name: eng_Latn-eri_Latn data_files: - path: train/eng_Latn-eri_Latn.jsonl.gz split: train - path: test/eng_Latn-eri_Latn.jsonl.gz split: test - path: validation/eng_Latn-eri_Latn.jsonl.gz split: validation - config_name: eng_Latn-cth_Latn data_files: - path: train/eng_Latn-cth_Latn.jsonl.gz split: train - path: test/eng_Latn-cth_Latn.jsonl.gz split: test - path: validation/eng_Latn-cth_Latn.jsonl.gz split: validation - config_name: eng_Latn-dwr_Latn data_files: - path: train/eng_Latn-dwr_Latn.jsonl.gz split: train - path: test/eng_Latn-dwr_Latn.jsonl.gz split: test - path: validation/eng_Latn-dwr_Latn.jsonl.gz split: validation - config_name: eng_Latn-srq_Latn data_files: - path: train/eng_Latn-srq_Latn.jsonl.gz split: train - path: test/eng_Latn-srq_Latn.jsonl.gz split: test - path: validation/eng_Latn-srq_Latn.jsonl.gz split: validation - config_name: eng_Latn-mmx_Latn data_files: - path: train/eng_Latn-mmx_Latn.jsonl.gz split: train - path: test/eng_Latn-mmx_Latn.jsonl.gz split: test - path: validation/eng_Latn-mmx_Latn.jsonl.gz split: validation - config_name: eng_Latn-cap_Latn data_files: - path: train/eng_Latn-cap_Latn.jsonl.gz split: train - path: test/eng_Latn-cap_Latn.jsonl.gz split: test - path: validation/eng_Latn-cap_Latn.jsonl.gz split: validation - config_name: eng_Latn-ces_Latn data_files: - path: train/eng_Latn-ces_Latn.jsonl.gz split: train - path: test/eng_Latn-ces_Latn.jsonl.gz split: test - path: validation/eng_Latn-ces_Latn.jsonl.gz split: validation - config_name: eng_Latn-cpu_Latn data_files: - path: train/eng_Latn-cpu_Latn.jsonl.gz split: train - path: test/eng_Latn-cpu_Latn.jsonl.gz split: test - path: validation/eng_Latn-cpu_Latn.jsonl.gz split: validation - config_name: eng_Latn-lww_Latn data_files: - path: train/eng_Latn-lww_Latn.jsonl.gz split: train - path: test/eng_Latn-lww_Latn.jsonl.gz split: test - path: validation/eng_Latn-lww_Latn.jsonl.gz split: validation - config_name: eng_Latn-hix_Latn data_files: - path: train/eng_Latn-hix_Latn.jsonl.gz split: train - path: test/eng_Latn-hix_Latn.jsonl.gz split: test - path: validation/eng_Latn-hix_Latn.jsonl.gz split: validation - config_name: eng_Latn-cab_Latn data_files: - path: train/eng_Latn-cab_Latn.jsonl.gz split: train - path: test/eng_Latn-cab_Latn.jsonl.gz split: test - path: validation/eng_Latn-cab_Latn.jsonl.gz split: validation - config_name: eng_Latn-fuf_Latn data_files: - path: train/eng_Latn-fuf_Latn.jsonl.gz split: train - path: test/eng_Latn-fuf_Latn.jsonl.gz split: test - path: validation/eng_Latn-fuf_Latn.jsonl.gz split: validation - config_name: eng_Latn-mcf_Latn data_files: - path: train/eng_Latn-mcf_Latn.jsonl.gz split: train - path: test/eng_Latn-mcf_Latn.jsonl.gz split: test - path: validation/eng_Latn-mcf_Latn.jsonl.gz split: validation - config_name: eng_Latn-shj_Latn data_files: - path: train/eng_Latn-shj_Latn.jsonl.gz split: train - path: test/eng_Latn-shj_Latn.jsonl.gz split: test - path: validation/eng_Latn-shj_Latn.jsonl.gz split: validation - config_name: eng_Latn-qwh_Latn data_files: - path: train/eng_Latn-qwh_Latn.jsonl.gz split: train - path: test/eng_Latn-qwh_Latn.jsonl.gz split: test - path: validation/eng_Latn-qwh_Latn.jsonl.gz split: validation - config_name: eng_Latn-zsr_Latn data_files: - path: train/eng_Latn-zsr_Latn.jsonl.gz split: train - path: test/eng_Latn-zsr_Latn.jsonl.gz split: test - path: validation/eng_Latn-zsr_Latn.jsonl.gz split: validation - config_name: eng_Latn-daa_Latn data_files: - path: train/eng_Latn-daa_Latn.jsonl.gz split: train - path: test/eng_Latn-daa_Latn.jsonl.gz split: test - path: validation/eng_Latn-daa_Latn.jsonl.gz split: validation - config_name: eng_Latn-sus_Arab data_files: - path: train/eng_Latn-sus_Arab.jsonl.gz split: train - path: test/eng_Latn-sus_Arab.jsonl.gz split: test - path: validation/eng_Latn-sus_Arab.jsonl.gz split: validation - config_name: eng_Latn-lbk_Latn data_files: - path: train/eng_Latn-lbk_Latn.jsonl.gz split: train - path: test/eng_Latn-lbk_Latn.jsonl.gz split: test - path: validation/eng_Latn-lbk_Latn.jsonl.gz split: validation - config_name: eng_Latn-nii_Latn data_files: - path: train/eng_Latn-nii_Latn.jsonl.gz split: train - path: test/eng_Latn-nii_Latn.jsonl.gz split: test - path: validation/eng_Latn-nii_Latn.jsonl.gz split: validation - config_name: eng_Latn-gvn_Latn data_files: - path: train/eng_Latn-gvn_Latn.jsonl.gz split: train - path: test/eng_Latn-gvn_Latn.jsonl.gz split: test - path: validation/eng_Latn-gvn_Latn.jsonl.gz split: validation - config_name: eng_Latn-yaa_Latn data_files: - path: train/eng_Latn-yaa_Latn.jsonl.gz split: train - path: test/eng_Latn-yaa_Latn.jsonl.gz split: test - path: validation/eng_Latn-yaa_Latn.jsonl.gz split: validation - config_name: eng_Latn-npi_Deva data_files: - path: train/eng_Latn-npi_Deva.jsonl.gz split: train - path: test/eng_Latn-npi_Deva.jsonl.gz split: test - path: validation/eng_Latn-npi_Deva.jsonl.gz split: validation - config_name: eng_Latn-uig_Latn data_files: - path: train/eng_Latn-uig_Latn.jsonl.gz split: train - path: test/eng_Latn-uig_Latn.jsonl.gz split: test - path: validation/eng_Latn-uig_Latn.jsonl.gz split: validation - config_name: eng_Latn-mmo_Latn data_files: - path: train/eng_Latn-mmo_Latn.jsonl.gz split: train - path: test/eng_Latn-mmo_Latn.jsonl.gz split: test - path: validation/eng_Latn-mmo_Latn.jsonl.gz split: validation - config_name: eng_Latn-ktm_Latn data_files: - path: train/eng_Latn-ktm_Latn.jsonl.gz split: train - path: test/eng_Latn-ktm_Latn.jsonl.gz split: test - path: validation/eng_Latn-ktm_Latn.jsonl.gz split: validation - config_name: eng_Latn-agu_Latn data_files: - path: train/eng_Latn-agu_Latn.jsonl.gz split: train - path: test/eng_Latn-agu_Latn.jsonl.gz split: test - path: validation/eng_Latn-agu_Latn.jsonl.gz split: validation - config_name: eng_Latn-agg_Latn data_files: - path: train/eng_Latn-agg_Latn.jsonl.gz split: train - path: test/eng_Latn-agg_Latn.jsonl.gz split: test - path: validation/eng_Latn-agg_Latn.jsonl.gz split: validation - config_name: eng_Latn-ken_Latn data_files: - path: train/eng_Latn-ken_Latn.jsonl.gz split: train - path: test/eng_Latn-ken_Latn.jsonl.gz split: test - path: validation/eng_Latn-ken_Latn.jsonl.gz split: validation - config_name: eng_Latn-beu_Latn data_files: - path: train/eng_Latn-beu_Latn.jsonl.gz split: train - path: test/eng_Latn-beu_Latn.jsonl.gz split: test - path: validation/eng_Latn-beu_Latn.jsonl.gz split: validation - config_name: eng_Latn-cac_Latn data_files: - path: train/eng_Latn-cac_Latn.jsonl.gz split: train - path: test/eng_Latn-cac_Latn.jsonl.gz split: test - path: validation/eng_Latn-cac_Latn.jsonl.gz split: validation - config_name: eng_Latn-uri_Latn data_files: - path: train/eng_Latn-uri_Latn.jsonl.gz split: train - path: test/eng_Latn-uri_Latn.jsonl.gz split: test - path: validation/eng_Latn-uri_Latn.jsonl.gz split: validation - config_name: eng_Latn-dah_Latn data_files: - path: train/eng_Latn-dah_Latn.jsonl.gz split: train - path: test/eng_Latn-dah_Latn.jsonl.gz split: test - path: validation/eng_Latn-dah_Latn.jsonl.gz split: validation - config_name: eng_Latn-otn_Latn data_files: - path: train/eng_Latn-otn_Latn.jsonl.gz split: train - path: test/eng_Latn-otn_Latn.jsonl.gz split: test - path: validation/eng_Latn-otn_Latn.jsonl.gz split: validation - config_name: eng_Latn-wos_Latn data_files: - path: train/eng_Latn-wos_Latn.jsonl.gz split: train - path: test/eng_Latn-wos_Latn.jsonl.gz split: test - path: validation/eng_Latn-wos_Latn.jsonl.gz split: validation - config_name: eng_Latn-hin_Deva data_files: - path: train/eng_Latn-hin_Deva.jsonl.gz split: train - path: test/eng_Latn-hin_Deva.jsonl.gz split: test - path: validation/eng_Latn-hin_Deva.jsonl.gz split: validation - config_name: eng_Latn-ctu_Latn data_files: - path: train/eng_Latn-ctu_Latn.jsonl.gz split: train - path: test/eng_Latn-ctu_Latn.jsonl.gz split: test - path: validation/eng_Latn-ctu_Latn.jsonl.gz split: validation - config_name: eng_Latn-pes_Arab data_files: - path: train/eng_Latn-pes_Arab.jsonl.gz split: train - path: test/eng_Latn-pes_Arab.jsonl.gz split: test - path: validation/eng_Latn-pes_Arab.jsonl.gz split: validation - config_name: eng_Latn-tbf_Latn data_files: - path: train/eng_Latn-tbf_Latn.jsonl.gz split: train - path: test/eng_Latn-tbf_Latn.jsonl.gz split: test - path: validation/eng_Latn-tbf_Latn.jsonl.gz split: validation - config_name: eng_Latn-bsj_Latn data_files: - path: train/eng_Latn-bsj_Latn.jsonl.gz split: train - path: test/eng_Latn-bsj_Latn.jsonl.gz split: test - path: validation/eng_Latn-bsj_Latn.jsonl.gz split: validation - config_name: eng_Latn-aey_Latn data_files: - path: train/eng_Latn-aey_Latn.jsonl.gz split: train - path: test/eng_Latn-aey_Latn.jsonl.gz split: test - path: validation/eng_Latn-aey_Latn.jsonl.gz split: validation - config_name: eng_Latn-qxn_Latn data_files: - path: train/eng_Latn-qxn_Latn.jsonl.gz split: train - path: test/eng_Latn-qxn_Latn.jsonl.gz split: test - path: validation/eng_Latn-qxn_Latn.jsonl.gz split: validation - config_name: eng_Latn-rug_Latn data_files: - path: train/eng_Latn-rug_Latn.jsonl.gz split: train - path: test/eng_Latn-rug_Latn.jsonl.gz split: test - path: validation/eng_Latn-rug_Latn.jsonl.gz split: validation - config_name: eng_Latn-nwi_Latn data_files: - path: train/eng_Latn-nwi_Latn.jsonl.gz split: train - path: test/eng_Latn-nwi_Latn.jsonl.gz split: test - path: validation/eng_Latn-nwi_Latn.jsonl.gz split: validation - config_name: eng_Latn-spl_Latn data_files: - path: train/eng_Latn-spl_Latn.jsonl.gz split: train - path: test/eng_Latn-spl_Latn.jsonl.gz split: test - path: validation/eng_Latn-spl_Latn.jsonl.gz split: validation - config_name: eng_Latn-kan_Knda data_files: - path: train/eng_Latn-kan_Knda.jsonl.gz split: train - path: test/eng_Latn-kan_Knda.jsonl.gz split: test - path: validation/eng_Latn-kan_Knda.jsonl.gz split: validation - config_name: eng_Latn-dif_Latn data_files: - path: train/eng_Latn-dif_Latn.jsonl.gz split: train - path: test/eng_Latn-dif_Latn.jsonl.gz split: test - path: validation/eng_Latn-dif_Latn.jsonl.gz split: validation - config_name: eng_Latn-cpa_Latn data_files: - path: train/eng_Latn-cpa_Latn.jsonl.gz split: train - path: test/eng_Latn-cpa_Latn.jsonl.gz split: test - path: validation/eng_Latn-cpa_Latn.jsonl.gz split: validation - config_name: eng_Latn-mau_Latn data_files: - path: train/eng_Latn-mau_Latn.jsonl.gz split: train - path: test/eng_Latn-mau_Latn.jsonl.gz split: test - path: validation/eng_Latn-mau_Latn.jsonl.gz split: validation - config_name: eng_Latn-ron_Latn data_files: - path: train/eng_Latn-ron_Latn.jsonl.gz split: train - path: test/eng_Latn-ron_Latn.jsonl.gz split: test - path: validation/eng_Latn-ron_Latn.jsonl.gz split: validation - config_name: eng_Latn-dop_Latn data_files: - path: train/eng_Latn-dop_Latn.jsonl.gz split: train - path: test/eng_Latn-dop_Latn.jsonl.gz split: test - path: validation/eng_Latn-dop_Latn.jsonl.gz split: validation - config_name: eng_Latn-hau_Latn data_files: - path: train/eng_Latn-hau_Latn.jsonl.gz split: train - path: test/eng_Latn-hau_Latn.jsonl.gz split: test - path: validation/eng_Latn-hau_Latn.jsonl.gz split: validation - config_name: eng_Latn-gnn_Latn data_files: - path: train/eng_Latn-gnn_Latn.jsonl.gz split: train - path: test/eng_Latn-gnn_Latn.jsonl.gz split: test - path: validation/eng_Latn-gnn_Latn.jsonl.gz split: validation - config_name: eng_Latn-bsn_Latn data_files: - path: train/eng_Latn-bsn_Latn.jsonl.gz split: train - path: test/eng_Latn-bsn_Latn.jsonl.gz split: test - path: validation/eng_Latn-bsn_Latn.jsonl.gz split: validation - config_name: eng_Latn-kpj_Latn data_files: - path: train/eng_Latn-kpj_Latn.jsonl.gz split: train - path: test/eng_Latn-kpj_Latn.jsonl.gz split: test - path: validation/eng_Latn-kpj_Latn.jsonl.gz split: validation - config_name: eng_Latn-wat_Latn data_files: - path: train/eng_Latn-wat_Latn.jsonl.gz split: train - path: test/eng_Latn-wat_Latn.jsonl.gz split: test - path: validation/eng_Latn-wat_Latn.jsonl.gz split: validation - config_name: eng_Latn-acr_Latn data_files: - path: train/eng_Latn-acr_Latn.jsonl.gz split: train - path: test/eng_Latn-acr_Latn.jsonl.gz split: test - path: validation/eng_Latn-acr_Latn.jsonl.gz split: validation - config_name: eng_Latn-caf_Latn data_files: - path: train/eng_Latn-caf_Latn.jsonl.gz split: train - path: test/eng_Latn-caf_Latn.jsonl.gz split: test - path: validation/eng_Latn-caf_Latn.jsonl.gz split: validation - config_name: eng_Latn-dhg_Latn data_files: - path: train/eng_Latn-dhg_Latn.jsonl.gz split: train - path: test/eng_Latn-dhg_Latn.jsonl.gz split: test - path: validation/eng_Latn-dhg_Latn.jsonl.gz split: validation - config_name: eng_Latn-yml_Latn data_files: - path: train/eng_Latn-yml_Latn.jsonl.gz split: train - path: test/eng_Latn-yml_Latn.jsonl.gz split: test - path: validation/eng_Latn-yml_Latn.jsonl.gz split: validation - config_name: eng_Latn-atd_Latn data_files: - path: train/eng_Latn-atd_Latn.jsonl.gz split: train - path: test/eng_Latn-atd_Latn.jsonl.gz split: test - path: validation/eng_Latn-atd_Latn.jsonl.gz split: validation - config_name: eng_Latn-bbb_Latn data_files: - path: train/eng_Latn-bbb_Latn.jsonl.gz split: train - path: test/eng_Latn-bbb_Latn.jsonl.gz split: test - path: validation/eng_Latn-bbb_Latn.jsonl.gz split: validation - config_name: eng_Latn-cle_Latn data_files: - path: train/eng_Latn-cle_Latn.jsonl.gz split: train - path: test/eng_Latn-cle_Latn.jsonl.gz split: test - path: validation/eng_Latn-cle_Latn.jsonl.gz split: validation - config_name: eng_Latn-myk_Latn data_files: - path: train/eng_Latn-myk_Latn.jsonl.gz split: train - path: test/eng_Latn-myk_Latn.jsonl.gz split: test - path: validation/eng_Latn-myk_Latn.jsonl.gz split: validation - config_name: eng_Latn-bxh_Latn data_files: - path: train/eng_Latn-bxh_Latn.jsonl.gz split: train - path: test/eng_Latn-bxh_Latn.jsonl.gz split: test - path: validation/eng_Latn-bxh_Latn.jsonl.gz split: validation - config_name: eng_Latn-tpa_Latn data_files: - path: train/eng_Latn-tpa_Latn.jsonl.gz split: train - path: test/eng_Latn-tpa_Latn.jsonl.gz split: test - path: validation/eng_Latn-tpa_Latn.jsonl.gz split: validation - config_name: eng_Latn-awk_Latn data_files: - path: train/eng_Latn-awk_Latn.jsonl.gz split: train - path: test/eng_Latn-awk_Latn.jsonl.gz split: test - path: validation/eng_Latn-awk_Latn.jsonl.gz split: validation - config_name: eng_Latn-gfk_Latn data_files: - path: train/eng_Latn-gfk_Latn.jsonl.gz split: train - path: test/eng_Latn-gfk_Latn.jsonl.gz split: test - path: validation/eng_Latn-gfk_Latn.jsonl.gz split: validation - config_name: eng_Latn-mph_Latn data_files: - path: train/eng_Latn-mph_Latn.jsonl.gz split: train - path: test/eng_Latn-mph_Latn.jsonl.gz split: test - path: validation/eng_Latn-mph_Latn.jsonl.gz split: validation - config_name: eng_Latn-csy_Latn data_files: - path: train/eng_Latn-csy_Latn.jsonl.gz split: train - path: test/eng_Latn-csy_Latn.jsonl.gz split: test - path: validation/eng_Latn-csy_Latn.jsonl.gz split: validation - config_name: eng_Latn-tgp_Latn data_files: - path: train/eng_Latn-tgp_Latn.jsonl.gz split: train - path: test/eng_Latn-tgp_Latn.jsonl.gz split: test - path: validation/eng_Latn-tgp_Latn.jsonl.gz split: validation - config_name: eng_Latn-zia_Latn data_files: - path: train/eng_Latn-zia_Latn.jsonl.gz split: train - path: test/eng_Latn-zia_Latn.jsonl.gz split: test - path: validation/eng_Latn-zia_Latn.jsonl.gz split: validation - config_name: eng_Latn-msm_Latn data_files: - path: train/eng_Latn-msm_Latn.jsonl.gz split: train - path: test/eng_Latn-msm_Latn.jsonl.gz split: test - path: validation/eng_Latn-msm_Latn.jsonl.gz split: validation - config_name: eng_Latn-kql_Latn data_files: - path: train/eng_Latn-kql_Latn.jsonl.gz split: train - path: test/eng_Latn-kql_Latn.jsonl.gz split: test - path: validation/eng_Latn-kql_Latn.jsonl.gz split: validation - config_name: eng_Latn-wnu_Latn data_files: - path: train/eng_Latn-wnu_Latn.jsonl.gz split: train - path: test/eng_Latn-wnu_Latn.jsonl.gz split: test - path: validation/eng_Latn-wnu_Latn.jsonl.gz split: validation - config_name: eng_Latn-nin_Latn data_files: - path: train/eng_Latn-nin_Latn.jsonl.gz split: train - path: test/eng_Latn-nin_Latn.jsonl.gz split: test - path: validation/eng_Latn-nin_Latn.jsonl.gz split: validation - config_name: eng_Latn-kmo_Latn data_files: - path: train/eng_Latn-kmo_Latn.jsonl.gz split: train - path: test/eng_Latn-kmo_Latn.jsonl.gz split: test - path: validation/eng_Latn-kmo_Latn.jsonl.gz split: validation - config_name: eng_Latn-mpx_Latn data_files: - path: train/eng_Latn-mpx_Latn.jsonl.gz split: train - path: test/eng_Latn-mpx_Latn.jsonl.gz split: test - path: validation/eng_Latn-mpx_Latn.jsonl.gz split: validation - config_name: eng_Latn-nas_Latn data_files: - path: train/eng_Latn-nas_Latn.jsonl.gz split: train - path: test/eng_Latn-nas_Latn.jsonl.gz split: test - path: validation/eng_Latn-nas_Latn.jsonl.gz split: validation - config_name: eng_Latn-ulk_Latn data_files: - path: train/eng_Latn-ulk_Latn.jsonl.gz split: train - path: test/eng_Latn-ulk_Latn.jsonl.gz split: test - path: validation/eng_Latn-ulk_Latn.jsonl.gz split: validation - config_name: eng_Latn-ipi_Latn data_files: - path: train/eng_Latn-ipi_Latn.jsonl.gz split: train - path: test/eng_Latn-ipi_Latn.jsonl.gz split: test - path: validation/eng_Latn-ipi_Latn.jsonl.gz split: validation - config_name: eng_Latn-mgc_Latn data_files: - path: train/eng_Latn-mgc_Latn.jsonl.gz split: train - path: test/eng_Latn-mgc_Latn.jsonl.gz split: test - path: validation/eng_Latn-mgc_Latn.jsonl.gz split: validation - config_name: eng_Latn-ape_Latn data_files: - path: train/eng_Latn-ape_Latn.jsonl.gz split: train - path: test/eng_Latn-ape_Latn.jsonl.gz split: test - path: validation/eng_Latn-ape_Latn.jsonl.gz split: validation - config_name: eng_Latn-tac_Latn data_files: - path: train/eng_Latn-tac_Latn.jsonl.gz split: train - path: test/eng_Latn-tac_Latn.jsonl.gz split: test - path: validation/eng_Latn-tac_Latn.jsonl.gz split: validation - config_name: eng_Latn-bzd_Latn data_files: - path: train/eng_Latn-bzd_Latn.jsonl.gz split: train - path: test/eng_Latn-bzd_Latn.jsonl.gz split: test - path: validation/eng_Latn-bzd_Latn.jsonl.gz split: validation - config_name: eng_Latn-amm_Latn data_files: - path: train/eng_Latn-amm_Latn.jsonl.gz split: train - path: test/eng_Latn-amm_Latn.jsonl.gz split: test - path: validation/eng_Latn-amm_Latn.jsonl.gz split: validation - config_name: eng_Latn-mic_Latn data_files: - path: train/eng_Latn-mic_Latn.jsonl.gz split: train - path: test/eng_Latn-mic_Latn.jsonl.gz split: test - path: validation/eng_Latn-mic_Latn.jsonl.gz split: validation - config_name: eng_Latn-sxb_Latn data_files: - path: train/eng_Latn-sxb_Latn.jsonl.gz split: train - path: test/eng_Latn-sxb_Latn.jsonl.gz split: test - path: validation/eng_Latn-sxb_Latn.jsonl.gz split: validation - config_name: eng_Latn-cux_Latn data_files: - path: train/eng_Latn-cux_Latn.jsonl.gz split: train - path: test/eng_Latn-cux_Latn.jsonl.gz split: test - path: validation/eng_Latn-cux_Latn.jsonl.gz split: validation - config_name: eng_Latn-ixl_Latn data_files: - path: train/eng_Latn-ixl_Latn.jsonl.gz split: train - path: test/eng_Latn-ixl_Latn.jsonl.gz split: test - path: validation/eng_Latn-ixl_Latn.jsonl.gz split: validation - config_name: eng_Latn-nif_Latn data_files: - path: train/eng_Latn-nif_Latn.jsonl.gz split: train - path: test/eng_Latn-nif_Latn.jsonl.gz split: test - path: validation/eng_Latn-nif_Latn.jsonl.gz split: validation - config_name: eng_Latn-isn_Latn data_files: - path: train/eng_Latn-isn_Latn.jsonl.gz split: train - path: test/eng_Latn-isn_Latn.jsonl.gz split: test - path: validation/eng_Latn-isn_Latn.jsonl.gz split: validation - config_name: eng_Latn-cmn_Hans data_files: - path: train/eng_Latn-cmn_Hans.jsonl.gz split: train - path: test/eng_Latn-cmn_Hans.jsonl.gz split: test - path: validation/eng_Latn-cmn_Hans.jsonl.gz split: validation - config_name: eng_Latn-kyf_Latn data_files: - path: train/eng_Latn-kyf_Latn.jsonl.gz split: train - path: test/eng_Latn-kyf_Latn.jsonl.gz split: test - path: validation/eng_Latn-kyf_Latn.jsonl.gz split: validation - config_name: eng_Latn-cut_Latn data_files: - path: train/eng_Latn-cut_Latn.jsonl.gz split: train - path: test/eng_Latn-cut_Latn.jsonl.gz split: test - path: validation/eng_Latn-cut_Latn.jsonl.gz split: validation - config_name: eng_Latn-lcm_Latn data_files: - path: train/eng_Latn-lcm_Latn.jsonl.gz split: train - path: test/eng_Latn-lcm_Latn.jsonl.gz split: test - path: validation/eng_Latn-lcm_Latn.jsonl.gz split: validation - config_name: eng_Latn-nya_Latn data_files: - path: train/eng_Latn-nya_Latn.jsonl.gz split: train - path: test/eng_Latn-nya_Latn.jsonl.gz split: test - path: validation/eng_Latn-nya_Latn.jsonl.gz split: validation - config_name: eng_Latn-kjs_Latn data_files: - path: train/eng_Latn-kjs_Latn.jsonl.gz split: train - path: test/eng_Latn-kjs_Latn.jsonl.gz split: test - path: validation/eng_Latn-kjs_Latn.jsonl.gz split: validation - config_name: eng_Latn-ton_Latn data_files: - path: train/eng_Latn-ton_Latn.jsonl.gz split: train - path: test/eng_Latn-ton_Latn.jsonl.gz split: test - path: validation/eng_Latn-ton_Latn.jsonl.gz split: validation - config_name: eng_Latn-qvs_Latn data_files: - path: train/eng_Latn-qvs_Latn.jsonl.gz split: train - path: test/eng_Latn-qvs_Latn.jsonl.gz split: test - path: validation/eng_Latn-qvs_Latn.jsonl.gz split: validation - config_name: eng_Latn-ino_Latn data_files: - path: train/eng_Latn-ino_Latn.jsonl.gz split: train - path: test/eng_Latn-ino_Latn.jsonl.gz split: test - path: validation/eng_Latn-ino_Latn.jsonl.gz split: validation - config_name: eng_Latn-zca_Latn data_files: - path: train/eng_Latn-zca_Latn.jsonl.gz split: train - path: test/eng_Latn-zca_Latn.jsonl.gz split: test - path: validation/eng_Latn-zca_Latn.jsonl.gz split: validation - config_name: eng_Latn-xav_Latn data_files: - path: train/eng_Latn-xav_Latn.jsonl.gz split: train - path: test/eng_Latn-xav_Latn.jsonl.gz split: test - path: validation/eng_Latn-xav_Latn.jsonl.gz split: validation - config_name: eng_Latn-jac_Latn data_files: - path: train/eng_Latn-jac_Latn.jsonl.gz split: train - path: test/eng_Latn-jac_Latn.jsonl.gz split: test - path: validation/eng_Latn-jac_Latn.jsonl.gz split: validation - config_name: eng_Latn-quc_Latn data_files: - path: train/eng_Latn-quc_Latn.jsonl.gz split: train - path: test/eng_Latn-quc_Latn.jsonl.gz split: test - path: validation/eng_Latn-quc_Latn.jsonl.gz split: validation - config_name: eng_Latn-npl_Latn data_files: - path: train/eng_Latn-npl_Latn.jsonl.gz split: train - path: test/eng_Latn-npl_Latn.jsonl.gz split: test - path: validation/eng_Latn-npl_Latn.jsonl.gz split: validation - config_name: eng_Latn-usa_Latn data_files: - path: train/eng_Latn-usa_Latn.jsonl.gz split: train - path: test/eng_Latn-usa_Latn.jsonl.gz split: test - path: validation/eng_Latn-usa_Latn.jsonl.gz split: validation - config_name: eng_Latn-kvg_Latn data_files: - path: train/eng_Latn-kvg_Latn.jsonl.gz split: train - path: test/eng_Latn-kvg_Latn.jsonl.gz split: test - path: validation/eng_Latn-kvg_Latn.jsonl.gz split: validation - config_name: eng_Latn-tee_Latn data_files: - path: train/eng_Latn-tee_Latn.jsonl.gz split: train - path: test/eng_Latn-tee_Latn.jsonl.gz split: test - path: validation/eng_Latn-tee_Latn.jsonl.gz split: validation - config_name: eng_Latn-hot_Latn data_files: - path: train/eng_Latn-hot_Latn.jsonl.gz split: train - path: test/eng_Latn-hot_Latn.jsonl.gz split: test - path: validation/eng_Latn-hot_Latn.jsonl.gz split: validation - config_name: eng_Latn-acf_Latn data_files: - path: train/eng_Latn-acf_Latn.jsonl.gz split: train - path: test/eng_Latn-acf_Latn.jsonl.gz split: test - path: validation/eng_Latn-acf_Latn.jsonl.gz split: validation - config_name: eng_Latn-wiu_Latn data_files: - path: train/eng_Latn-wiu_Latn.jsonl.gz split: train - path: test/eng_Latn-wiu_Latn.jsonl.gz split: test - path: validation/eng_Latn-wiu_Latn.jsonl.gz split: validation - config_name: eng_Latn-rmc_Latn data_files: - path: train/eng_Latn-rmc_Latn.jsonl.gz split: train - path: test/eng_Latn-rmc_Latn.jsonl.gz split: test - path: validation/eng_Latn-rmc_Latn.jsonl.gz split: validation - config_name: eng_Latn-snx_Latn data_files: - path: train/eng_Latn-snx_Latn.jsonl.gz split: train - path: test/eng_Latn-snx_Latn.jsonl.gz split: test - path: validation/eng_Latn-snx_Latn.jsonl.gz split: validation - config_name: eng_Latn-jpn_Jpan data_files: - path: train/eng_Latn-jpn_Jpan.jsonl.gz split: train - path: test/eng_Latn-jpn_Jpan.jsonl.gz split: test - path: validation/eng_Latn-jpn_Jpan.jsonl.gz split: validation - config_name: eng_Latn-tbg_Latn data_files: - path: train/eng_Latn-tbg_Latn.jsonl.gz split: train - path: test/eng_Latn-tbg_Latn.jsonl.gz split: test - path: validation/eng_Latn-tbg_Latn.jsonl.gz split: validation - config_name: eng_Latn-pwg_Latn data_files: - path: train/eng_Latn-pwg_Latn.jsonl.gz split: train - path: test/eng_Latn-pwg_Latn.jsonl.gz split: test - path: validation/eng_Latn-pwg_Latn.jsonl.gz split: validation - config_name: eng_Latn-nhr_Latn data_files: - path: train/eng_Latn-nhr_Latn.jsonl.gz split: train - path: test/eng_Latn-nhr_Latn.jsonl.gz split: test - path: validation/eng_Latn-nhr_Latn.jsonl.gz split: validation - config_name: eng_Latn-mwe_Latn data_files: - path: train/eng_Latn-mwe_Latn.jsonl.gz split: train - path: test/eng_Latn-mwe_Latn.jsonl.gz split: test - path: validation/eng_Latn-mwe_Latn.jsonl.gz split: validation - config_name: eng_Latn-kbq_Latn data_files: - path: train/eng_Latn-kbq_Latn.jsonl.gz split: train - path: test/eng_Latn-kbq_Latn.jsonl.gz split: test - path: validation/eng_Latn-kbq_Latn.jsonl.gz split: validation - config_name: eng_Latn-myw_Latn data_files: - path: train/eng_Latn-myw_Latn.jsonl.gz split: train - path: test/eng_Latn-myw_Latn.jsonl.gz split: test - path: validation/eng_Latn-myw_Latn.jsonl.gz split: validation - config_name: eng_Latn-jni_Latn data_files: - path: train/eng_Latn-jni_Latn.jsonl.gz split: train - path: test/eng_Latn-jni_Latn.jsonl.gz split: test - path: validation/eng_Latn-jni_Latn.jsonl.gz split: validation - config_name: eng_Latn-vmy_Latn data_files: - path: train/eng_Latn-vmy_Latn.jsonl.gz split: train - path: test/eng_Latn-vmy_Latn.jsonl.gz split: test - path: validation/eng_Latn-vmy_Latn.jsonl.gz split: validation - config_name: eng_Latn-zpv_Latn data_files: - path: train/eng_Latn-zpv_Latn.jsonl.gz split: train - path: test/eng_Latn-zpv_Latn.jsonl.gz split: test - path: validation/eng_Latn-zpv_Latn.jsonl.gz split: validation - config_name: eng_Latn-heb_Hebr data_files: - path: train/eng_Latn-heb_Hebr.jsonl.gz split: train - path: test/eng_Latn-heb_Hebr.jsonl.gz split: test - path: validation/eng_Latn-heb_Hebr.jsonl.gz split: validation - config_name: eng_Latn-bki_Latn data_files: - path: train/eng_Latn-bki_Latn.jsonl.gz split: train - path: test/eng_Latn-bki_Latn.jsonl.gz split: test - path: validation/eng_Latn-bki_Latn.jsonl.gz split: validation - config_name: eng_Latn-mee_Latn data_files: - path: train/eng_Latn-mee_Latn.jsonl.gz split: train - path: test/eng_Latn-mee_Latn.jsonl.gz split: test - path: validation/eng_Latn-mee_Latn.jsonl.gz split: validation - config_name: eng_Latn-aau_Latn data_files: - path: train/eng_Latn-aau_Latn.jsonl.gz split: train - path: test/eng_Latn-aau_Latn.jsonl.gz split: test - path: validation/eng_Latn-aau_Latn.jsonl.gz split: validation - config_name: eng_Latn-aaz_Latn data_files: - path: train/eng_Latn-aaz_Latn.jsonl.gz split: train - path: test/eng_Latn-aaz_Latn.jsonl.gz split: test - path: validation/eng_Latn-aaz_Latn.jsonl.gz split: validation - config_name: eng_Latn-aoi_Latn data_files: - path: train/eng_Latn-aoi_Latn.jsonl.gz split: train - path: test/eng_Latn-aoi_Latn.jsonl.gz split: test - path: validation/eng_Latn-aoi_Latn.jsonl.gz split: validation - config_name: eng_Latn-caa_Latn data_files: - path: train/eng_Latn-caa_Latn.jsonl.gz split: train - path: test/eng_Latn-caa_Latn.jsonl.gz split: test - path: validation/eng_Latn-caa_Latn.jsonl.gz split: validation - config_name: eng_Latn-zap_Latn data_files: - path: train/eng_Latn-zap_Latn.jsonl.gz split: train - path: test/eng_Latn-zap_Latn.jsonl.gz split: test - path: validation/eng_Latn-zap_Latn.jsonl.gz split: validation - config_name: eng_Latn-amk_Latn data_files: - path: train/eng_Latn-amk_Latn.jsonl.gz split: train - path: test/eng_Latn-amk_Latn.jsonl.gz split: test - path: validation/eng_Latn-amk_Latn.jsonl.gz split: validation - config_name: eng_Latn-zpo_Latn data_files: - path: train/eng_Latn-zpo_Latn.jsonl.gz split: train - path: test/eng_Latn-zpo_Latn.jsonl.gz split: test - path: validation/eng_Latn-zpo_Latn.jsonl.gz split: validation - config_name: eng_Latn-aom_Latn data_files: - path: train/eng_Latn-aom_Latn.jsonl.gz split: train - path: test/eng_Latn-aom_Latn.jsonl.gz split: test - path: validation/eng_Latn-aom_Latn.jsonl.gz split: validation - config_name: eng_Latn-zpu_Latn data_files: - path: train/eng_Latn-zpu_Latn.jsonl.gz split: train - path: test/eng_Latn-zpu_Latn.jsonl.gz split: test - path: validation/eng_Latn-zpu_Latn.jsonl.gz split: validation - config_name: eng_Latn-bdd_Latn data_files: - path: train/eng_Latn-bdd_Latn.jsonl.gz split: train - path: test/eng_Latn-bdd_Latn.jsonl.gz split: test - path: validation/eng_Latn-bdd_Latn.jsonl.gz split: validation - config_name: eng_Latn-acu_Latn data_files: - path: train/eng_Latn-acu_Latn.jsonl.gz split: train - path: test/eng_Latn-acu_Latn.jsonl.gz split: test - path: validation/eng_Latn-acu_Latn.jsonl.gz split: validation - config_name: eng_Latn-beo_Latn data_files: - path: train/eng_Latn-beo_Latn.jsonl.gz split: train - path: test/eng_Latn-beo_Latn.jsonl.gz split: test - path: validation/eng_Latn-beo_Latn.jsonl.gz split: validation - config_name: eng_Latn-cuc_Latn data_files: - path: train/eng_Latn-cuc_Latn.jsonl.gz split: train - path: test/eng_Latn-cuc_Latn.jsonl.gz split: test - path: validation/eng_Latn-cuc_Latn.jsonl.gz split: validation - config_name: eng_Latn-otm_Latn data_files: - path: train/eng_Latn-otm_Latn.jsonl.gz split: train - path: test/eng_Latn-otm_Latn.jsonl.gz split: test - path: validation/eng_Latn-otm_Latn.jsonl.gz split: validation - config_name: eng_Latn-tos_Latn data_files: - path: train/eng_Latn-tos_Latn.jsonl.gz split: train - path: test/eng_Latn-tos_Latn.jsonl.gz split: test - path: validation/eng_Latn-tos_Latn.jsonl.gz split: validation - config_name: eng_Latn-apu_Latn data_files: - path: train/eng_Latn-apu_Latn.jsonl.gz split: train - path: test/eng_Latn-apu_Latn.jsonl.gz split: test - path: validation/eng_Latn-apu_Latn.jsonl.gz split: validation - config_name: eng_Latn-jic_Latn data_files: - path: train/eng_Latn-jic_Latn.jsonl.gz split: train - path: test/eng_Latn-jic_Latn.jsonl.gz split: test - path: validation/eng_Latn-jic_Latn.jsonl.gz split: validation - config_name: eng_Latn-cek_Latn data_files: - path: train/eng_Latn-cek_Latn.jsonl.gz split: train - path: test/eng_Latn-cek_Latn.jsonl.gz split: test - path: validation/eng_Latn-cek_Latn.jsonl.gz split: validation - config_name: eng_Latn-tnp_Latn data_files: - path: train/eng_Latn-tnp_Latn.jsonl.gz split: train - path: test/eng_Latn-tnp_Latn.jsonl.gz split: test - path: validation/eng_Latn-tnp_Latn.jsonl.gz split: validation - config_name: eng_Latn-hns_Latn data_files: - path: train/eng_Latn-hns_Latn.jsonl.gz split: train - path: test/eng_Latn-hns_Latn.jsonl.gz split: test - path: validation/eng_Latn-hns_Latn.jsonl.gz split: validation - config_name: eng_Latn-mpt_Latn data_files: - path: train/eng_Latn-mpt_Latn.jsonl.gz split: train - path: test/eng_Latn-mpt_Latn.jsonl.gz split: test - path: validation/eng_Latn-mpt_Latn.jsonl.gz split: validation - config_name: eng_Latn-kmg_Latn data_files: - path: train/eng_Latn-kmg_Latn.jsonl.gz split: train - path: test/eng_Latn-kmg_Latn.jsonl.gz split: test - path: validation/eng_Latn-kmg_Latn.jsonl.gz split: validation - config_name: eng_Latn-nhg_Latn data_files: - path: train/eng_Latn-nhg_Latn.jsonl.gz split: train - path: test/eng_Latn-nhg_Latn.jsonl.gz split: test - path: validation/eng_Latn-nhg_Latn.jsonl.gz split: validation - config_name: eng_Latn-yle_Latn data_files: - path: train/eng_Latn-yle_Latn.jsonl.gz split: train - path: test/eng_Latn-yle_Latn.jsonl.gz split: test - path: validation/eng_Latn-yle_Latn.jsonl.gz split: validation - config_name: eng_Latn-yka_Latn data_files: - path: train/eng_Latn-yka_Latn.jsonl.gz split: train - path: test/eng_Latn-yka_Latn.jsonl.gz split: test - path: validation/eng_Latn-yka_Latn.jsonl.gz split: validation - config_name: eng_Latn-maj_Latn data_files: - path: train/eng_Latn-maj_Latn.jsonl.gz split: train - path: test/eng_Latn-maj_Latn.jsonl.gz split: test - path: validation/eng_Latn-maj_Latn.jsonl.gz split: validation - config_name: eng_Latn-agt_Latn data_files: - path: train/eng_Latn-agt_Latn.jsonl.gz split: train - path: test/eng_Latn-agt_Latn.jsonl.gz split: test - path: validation/eng_Latn-agt_Latn.jsonl.gz split: validation - config_name: eng_Latn-san_Latn data_files: - path: train/eng_Latn-san_Latn.jsonl.gz split: train - path: test/eng_Latn-san_Latn.jsonl.gz split: test - path: validation/eng_Latn-san_Latn.jsonl.gz split: validation - config_name: eng_Latn-kew_Latn data_files: - path: train/eng_Latn-kew_Latn.jsonl.gz split: train - path: test/eng_Latn-kew_Latn.jsonl.gz split: test - path: validation/eng_Latn-kew_Latn.jsonl.gz split: validation - config_name: eng_Latn-nop_Latn data_files: - path: train/eng_Latn-nop_Latn.jsonl.gz split: train - path: test/eng_Latn-nop_Latn.jsonl.gz split: test - path: validation/eng_Latn-nop_Latn.jsonl.gz split: validation - config_name: eng_Latn-zyp_Latn data_files: - path: train/eng_Latn-zyp_Latn.jsonl.gz split: train - path: test/eng_Latn-zyp_Latn.jsonl.gz split: test - path: validation/eng_Latn-zyp_Latn.jsonl.gz split: validation - config_name: eng_Latn-qvm_Latn data_files: - path: train/eng_Latn-qvm_Latn.jsonl.gz split: train - path: test/eng_Latn-qvm_Latn.jsonl.gz split: test - path: validation/eng_Latn-qvm_Latn.jsonl.gz split: validation - config_name: eng_Latn-mwp_Latn data_files: - path: train/eng_Latn-mwp_Latn.jsonl.gz split: train - path: test/eng_Latn-mwp_Latn.jsonl.gz split: test - path: validation/eng_Latn-mwp_Latn.jsonl.gz split: validation - config_name: eng_Latn-nhw_Latn data_files: - path: train/eng_Latn-nhw_Latn.jsonl.gz split: train - path: test/eng_Latn-nhw_Latn.jsonl.gz split: test - path: validation/eng_Latn-nhw_Latn.jsonl.gz split: validation - config_name: eng_Latn-als_Latn data_files: - path: train/eng_Latn-als_Latn.jsonl.gz split: train - path: test/eng_Latn-als_Latn.jsonl.gz split: test - path: validation/eng_Latn-als_Latn.jsonl.gz split: validation - config_name: eng_Latn-cof_Latn data_files: - path: train/eng_Latn-cof_Latn.jsonl.gz split: train - path: test/eng_Latn-cof_Latn.jsonl.gz split: test - path: validation/eng_Latn-cof_Latn.jsonl.gz split: validation - config_name: eng_Latn-auy_Latn data_files: - path: train/eng_Latn-auy_Latn.jsonl.gz split: train - path: test/eng_Latn-auy_Latn.jsonl.gz split: test - path: validation/eng_Latn-auy_Latn.jsonl.gz split: validation - config_name: eng_Latn-pol_Latn data_files: - path: train/eng_Latn-pol_Latn.jsonl.gz split: train - path: test/eng_Latn-pol_Latn.jsonl.gz split: test - path: validation/eng_Latn-pol_Latn.jsonl.gz split: validation - config_name: eng_Latn-klt_Latn data_files: - path: train/eng_Latn-klt_Latn.jsonl.gz split: train - path: test/eng_Latn-klt_Latn.jsonl.gz split: test - path: validation/eng_Latn-klt_Latn.jsonl.gz split: validation - config_name: eng_Latn-soy_Latn data_files: - path: train/eng_Latn-soy_Latn.jsonl.gz split: train - path: test/eng_Latn-soy_Latn.jsonl.gz split: test - path: validation/eng_Latn-soy_Latn.jsonl.gz split: validation - config_name: eng_Latn-ita_Latn data_files: - path: train/eng_Latn-ita_Latn.jsonl.gz split: train - path: test/eng_Latn-ita_Latn.jsonl.gz split: test - path: validation/eng_Latn-ita_Latn.jsonl.gz split: validation - config_name: eng_Latn-arn_Latn data_files: - path: train/eng_Latn-arn_Latn.jsonl.gz split: train - path: test/eng_Latn-arn_Latn.jsonl.gz split: test - path: validation/eng_Latn-arn_Latn.jsonl.gz split: validation - config_name: eng_Latn-tbz_Latn data_files: - path: train/eng_Latn-tbz_Latn.jsonl.gz split: train - path: test/eng_Latn-tbz_Latn.jsonl.gz split: test - path: validation/eng_Latn-tbz_Latn.jsonl.gz split: validation - config_name: eng_Latn-zaa_Latn data_files: - path: train/eng_Latn-zaa_Latn.jsonl.gz split: train - path: test/eng_Latn-zaa_Latn.jsonl.gz split: test - path: validation/eng_Latn-zaa_Latn.jsonl.gz split: validation - config_name: eng_Latn-meu_Latn data_files: - path: train/eng_Latn-meu_Latn.jsonl.gz split: train - path: test/eng_Latn-meu_Latn.jsonl.gz split: test - path: validation/eng_Latn-meu_Latn.jsonl.gz split: validation - config_name: eng_Latn-zao_Latn data_files: - path: train/eng_Latn-zao_Latn.jsonl.gz split: train - path: test/eng_Latn-zao_Latn.jsonl.gz split: test - path: validation/eng_Latn-zao_Latn.jsonl.gz split: validation - config_name: eng_Latn-mxp_Latn data_files: - path: train/eng_Latn-mxp_Latn.jsonl.gz split: train - path: test/eng_Latn-mxp_Latn.jsonl.gz split: test - path: validation/eng_Latn-mxp_Latn.jsonl.gz split: validation - config_name: eng_Latn-rgu_Latn data_files: - path: train/eng_Latn-rgu_Latn.jsonl.gz split: train - path: test/eng_Latn-rgu_Latn.jsonl.gz split: test - path: validation/eng_Latn-rgu_Latn.jsonl.gz split: validation - config_name: eng_Latn-tnn_Latn data_files: - path: train/eng_Latn-tnn_Latn.jsonl.gz split: train - path: test/eng_Latn-tnn_Latn.jsonl.gz split: test - path: validation/eng_Latn-tnn_Latn.jsonl.gz split: validation - config_name: eng_Latn-uvl_Latn data_files: - path: train/eng_Latn-uvl_Latn.jsonl.gz split: train - path: test/eng_Latn-uvl_Latn.jsonl.gz split: test - path: validation/eng_Latn-uvl_Latn.jsonl.gz split: validation - config_name: eng_Latn-eko_Latn data_files: - path: train/eng_Latn-eko_Latn.jsonl.gz split: train - path: test/eng_Latn-eko_Latn.jsonl.gz split: test - path: validation/eng_Latn-eko_Latn.jsonl.gz split: validation - config_name: eng_Latn-wmt_Latn data_files: - path: train/eng_Latn-wmt_Latn.jsonl.gz split: train - path: test/eng_Latn-wmt_Latn.jsonl.gz split: test - path: validation/eng_Latn-wmt_Latn.jsonl.gz split: validation - config_name: eng_Latn-kup_Latn data_files: - path: train/eng_Latn-kup_Latn.jsonl.gz split: train - path: test/eng_Latn-kup_Latn.jsonl.gz split: test - path: validation/eng_Latn-kup_Latn.jsonl.gz split: validation - config_name: eng_Latn-zai_Latn data_files: - path: train/eng_Latn-zai_Latn.jsonl.gz split: train - path: test/eng_Latn-zai_Latn.jsonl.gz split: test - path: validation/eng_Latn-zai_Latn.jsonl.gz split: validation - config_name: eng_Latn-ebk_Latn data_files: - path: train/eng_Latn-ebk_Latn.jsonl.gz split: train - path: test/eng_Latn-ebk_Latn.jsonl.gz split: test - path: validation/eng_Latn-ebk_Latn.jsonl.gz split: validation - config_name: eng_Latn-vie_Latn data_files: - path: train/eng_Latn-vie_Latn.jsonl.gz split: train - path: test/eng_Latn-vie_Latn.jsonl.gz split: test - path: validation/eng_Latn-vie_Latn.jsonl.gz split: validation - config_name: eng_Latn-azz_Latn data_files: - path: train/eng_Latn-azz_Latn.jsonl.gz split: train - path: test/eng_Latn-azz_Latn.jsonl.gz split: test - path: validation/eng_Latn-azz_Latn.jsonl.gz split: validation - config_name: eng_Latn-wbp_Latn data_files: - path: train/eng_Latn-wbp_Latn.jsonl.gz split: train - path: test/eng_Latn-wbp_Latn.jsonl.gz split: test - path: validation/eng_Latn-wbp_Latn.jsonl.gz split: validation - config_name: eng_Latn-tvk_Latn data_files: - path: train/eng_Latn-tvk_Latn.jsonl.gz split: train - path: test/eng_Latn-tvk_Latn.jsonl.gz split: test - path: validation/eng_Latn-tvk_Latn.jsonl.gz split: validation - config_name: eng_Latn-ote_Latn data_files: - path: train/eng_Latn-ote_Latn.jsonl.gz split: train - path: test/eng_Latn-ote_Latn.jsonl.gz split: test - path: validation/eng_Latn-ote_Latn.jsonl.gz split: validation - config_name: eng_Latn-ubu_Latn data_files: - path: train/eng_Latn-ubu_Latn.jsonl.gz split: train - path: test/eng_Latn-ubu_Latn.jsonl.gz split: test - path: validation/eng_Latn-ubu_Latn.jsonl.gz split: validation - config_name: eng_Latn-zga_Latn data_files: - path: train/eng_Latn-zga_Latn.jsonl.gz split: train - path: test/eng_Latn-zga_Latn.jsonl.gz split: test - path: validation/eng_Latn-zga_Latn.jsonl.gz split: validation - config_name: eng_Latn-bps_Latn data_files: - path: train/eng_Latn-bps_Latn.jsonl.gz split: train - path: test/eng_Latn-bps_Latn.jsonl.gz split: test - path: validation/eng_Latn-bps_Latn.jsonl.gz split: validation - config_name: eng_Latn-swh_Latn data_files: - path: train/eng_Latn-swh_Latn.jsonl.gz split: train - path: test/eng_Latn-swh_Latn.jsonl.gz split: test - path: validation/eng_Latn-swh_Latn.jsonl.gz split: validation - config_name: eng_Latn-ntp_Latn data_files: - path: train/eng_Latn-ntp_Latn.jsonl.gz split: train - path: test/eng_Latn-ntp_Latn.jsonl.gz split: test - path: validation/eng_Latn-ntp_Latn.jsonl.gz split: validation - config_name: eng_Latn-tav_Latn data_files: - path: train/eng_Latn-tav_Latn.jsonl.gz split: train - path: test/eng_Latn-tav_Latn.jsonl.gz split: test - path: validation/eng_Latn-tav_Latn.jsonl.gz split: validation - config_name: eng_Latn-kms_Latn data_files: - path: train/eng_Latn-kms_Latn.jsonl.gz split: train - path: test/eng_Latn-kms_Latn.jsonl.gz split: test - path: validation/eng_Latn-kms_Latn.jsonl.gz split: validation - config_name: eng_Latn-pio_Latn data_files: - path: train/eng_Latn-pio_Latn.jsonl.gz split: train - path: test/eng_Latn-pio_Latn.jsonl.gz split: test - path: validation/eng_Latn-pio_Latn.jsonl.gz split: validation - config_name: eng_Latn-guj_Gujr data_files: - path: train/eng_Latn-guj_Gujr.jsonl.gz split: train - path: test/eng_Latn-guj_Gujr.jsonl.gz split: test - path: validation/eng_Latn-guj_Gujr.jsonl.gz split: validation - config_name: eng_Latn-mbl_Latn data_files: - path: train/eng_Latn-mbl_Latn.jsonl.gz split: train - path: test/eng_Latn-mbl_Latn.jsonl.gz split: test - path: validation/eng_Latn-mbl_Latn.jsonl.gz split: validation - config_name: eng_Latn-aii_Syrc data_files: - path: train/eng_Latn-aii_Syrc.jsonl.gz split: train - path: test/eng_Latn-aii_Syrc.jsonl.gz split: test - path: validation/eng_Latn-aii_Syrc.jsonl.gz split: validation - config_name: eng_Latn-mle_Latn data_files: - path: train/eng_Latn-mle_Latn.jsonl.gz split: train - path: test/eng_Latn-mle_Latn.jsonl.gz split: test - path: validation/eng_Latn-mle_Latn.jsonl.gz split: validation - config_name: eng_Latn-zam_Latn data_files: - path: train/eng_Latn-zam_Latn.jsonl.gz split: train - path: test/eng_Latn-zam_Latn.jsonl.gz split: test - path: validation/eng_Latn-zam_Latn.jsonl.gz split: validation - config_name: eng_Latn-urt_Latn data_files: - path: train/eng_Latn-urt_Latn.jsonl.gz split: train - path: test/eng_Latn-urt_Latn.jsonl.gz split: test - path: validation/eng_Latn-urt_Latn.jsonl.gz split: validation - config_name: eng_Latn-guo_Latn data_files: - path: train/eng_Latn-guo_Latn.jsonl.gz split: train - path: test/eng_Latn-guo_Latn.jsonl.gz split: test - path: validation/eng_Latn-guo_Latn.jsonl.gz split: validation - config_name: eng_Latn-bkd_Latn data_files: - path: train/eng_Latn-bkd_Latn.jsonl.gz split: train - path: test/eng_Latn-bkd_Latn.jsonl.gz split: test - path: validation/eng_Latn-bkd_Latn.jsonl.gz split: validation - config_name: eng_Latn-rmy_Latn data_files: - path: train/eng_Latn-rmy_Latn.jsonl.gz split: train - path: test/eng_Latn-rmy_Latn.jsonl.gz split: test - path: validation/eng_Latn-rmy_Latn.jsonl.gz split: validation - config_name: eng_Latn-ong_Latn data_files: - path: train/eng_Latn-ong_Latn.jsonl.gz split: train - path: test/eng_Latn-ong_Latn.jsonl.gz split: test - path: validation/eng_Latn-ong_Latn.jsonl.gz split: validation - config_name: eng_Latn-mlp_Latn data_files: - path: train/eng_Latn-mlp_Latn.jsonl.gz split: train - path: test/eng_Latn-mlp_Latn.jsonl.gz split: test - path: validation/eng_Latn-mlp_Latn.jsonl.gz split: validation - config_name: eng_Latn-mir_Latn data_files: - path: train/eng_Latn-mir_Latn.jsonl.gz split: train - path: test/eng_Latn-mir_Latn.jsonl.gz split: test - path: validation/eng_Latn-mir_Latn.jsonl.gz split: validation - config_name: eng_Latn-med_Latn data_files: - path: train/eng_Latn-med_Latn.jsonl.gz split: train - path: test/eng_Latn-med_Latn.jsonl.gz split: test - path: validation/eng_Latn-med_Latn.jsonl.gz split: validation - config_name: eng_Latn-bef_Latn data_files: - path: train/eng_Latn-bef_Latn.jsonl.gz split: train - path: test/eng_Latn-bef_Latn.jsonl.gz split: test - path: validation/eng_Latn-bef_Latn.jsonl.gz split: validation - config_name: eng_Latn-yuw_Latn data_files: - path: train/eng_Latn-yuw_Latn.jsonl.gz split: train - path: test/eng_Latn-yuw_Latn.jsonl.gz split: test - path: validation/eng_Latn-yuw_Latn.jsonl.gz split: validation - config_name: eng_Latn-ded_Latn data_files: - path: train/eng_Latn-ded_Latn.jsonl.gz split: train - path: test/eng_Latn-ded_Latn.jsonl.gz split: test - path: validation/eng_Latn-ded_Latn.jsonl.gz split: validation - config_name: eng_Latn-ame_Latn data_files: - path: train/eng_Latn-ame_Latn.jsonl.gz split: train - path: test/eng_Latn-ame_Latn.jsonl.gz split: test - path: validation/eng_Latn-ame_Latn.jsonl.gz split: validation - config_name: eng_Latn-car_Latn data_files: - path: train/eng_Latn-car_Latn.jsonl.gz split: train - path: test/eng_Latn-car_Latn.jsonl.gz split: test - path: validation/eng_Latn-car_Latn.jsonl.gz split: validation - config_name: eng_Latn-chz_Latn data_files: - path: train/eng_Latn-chz_Latn.jsonl.gz split: train - path: test/eng_Latn-chz_Latn.jsonl.gz split: test - path: validation/eng_Latn-chz_Latn.jsonl.gz split: validation - config_name: eng_Latn-ubr_Latn data_files: - path: train/eng_Latn-ubr_Latn.jsonl.gz split: train - path: test/eng_Latn-ubr_Latn.jsonl.gz split: test - path: validation/eng_Latn-ubr_Latn.jsonl.gz split: validation - config_name: eng_Latn-mar_Deva data_files: - path: train/eng_Latn-mar_Deva.jsonl.gz split: train - path: test/eng_Latn-mar_Deva.jsonl.gz split: test - path: validation/eng_Latn-mar_Deva.jsonl.gz split: validation - config_name: eng_Latn-gun_Latn data_files: - path: train/eng_Latn-gun_Latn.jsonl.gz split: train - path: test/eng_Latn-gun_Latn.jsonl.gz split: test - path: validation/eng_Latn-gun_Latn.jsonl.gz split: validation - config_name: eng_Latn-pir_Latn data_files: - path: train/eng_Latn-pir_Latn.jsonl.gz split: train - path: test/eng_Latn-pir_Latn.jsonl.gz split: test - path: validation/eng_Latn-pir_Latn.jsonl.gz split: validation - config_name: eng_Latn-inb_Latn data_files: - path: train/eng_Latn-inb_Latn.jsonl.gz split: train - path: test/eng_Latn-inb_Latn.jsonl.gz split: test - path: validation/eng_Latn-inb_Latn.jsonl.gz split: validation - config_name: eng_Latn-gym_Latn data_files: - path: train/eng_Latn-gym_Latn.jsonl.gz split: train - path: test/eng_Latn-gym_Latn.jsonl.gz split: test - path: validation/eng_Latn-gym_Latn.jsonl.gz split: validation - config_name: eng_Latn-mit_Latn data_files: - path: train/eng_Latn-mit_Latn.jsonl.gz split: train - path: test/eng_Latn-mit_Latn.jsonl.gz split: test - path: validation/eng_Latn-mit_Latn.jsonl.gz split: validation - config_name: eng_Latn-enq_Latn data_files: - path: train/eng_Latn-enq_Latn.jsonl.gz split: train - path: test/eng_Latn-enq_Latn.jsonl.gz split: test - path: validation/eng_Latn-enq_Latn.jsonl.gz split: validation - config_name: eng_Latn-kqf_Latn data_files: - path: train/eng_Latn-kqf_Latn.jsonl.gz split: train - path: test/eng_Latn-kqf_Latn.jsonl.gz split: test - path: validation/eng_Latn-kqf_Latn.jsonl.gz split: validation - config_name: eng_Latn-mbh_Latn data_files: - path: train/eng_Latn-mbh_Latn.jsonl.gz split: train - path: test/eng_Latn-mbh_Latn.jsonl.gz split: test - path: validation/eng_Latn-mbh_Latn.jsonl.gz split: validation - config_name: eng_Latn-xbi_Latn data_files: - path: train/eng_Latn-xbi_Latn.jsonl.gz split: train - path: test/eng_Latn-xbi_Latn.jsonl.gz split: test - path: validation/eng_Latn-xbi_Latn.jsonl.gz split: validation - config_name: eng_Latn-gof_Latn data_files: - path: train/eng_Latn-gof_Latn.jsonl.gz split: train - path: test/eng_Latn-gof_Latn.jsonl.gz split: test - path: validation/eng_Latn-gof_Latn.jsonl.gz split: validation - config_name: eng_Latn-lat_Latn data_files: - path: train/eng_Latn-lat_Latn.jsonl.gz split: train - path: test/eng_Latn-lat_Latn.jsonl.gz split: test - path: validation/eng_Latn-lat_Latn.jsonl.gz split: validation - config_name: eng_Latn-gah_Latn data_files: - path: train/eng_Latn-gah_Latn.jsonl.gz split: train - path: test/eng_Latn-gah_Latn.jsonl.gz split: test - path: validation/eng_Latn-gah_Latn.jsonl.gz split: validation - config_name: eng_Latn-zav_Latn data_files: - path: train/eng_Latn-zav_Latn.jsonl.gz split: train - path: test/eng_Latn-zav_Latn.jsonl.gz split: test - path: validation/eng_Latn-zav_Latn.jsonl.gz split: validation - config_name: eng_Latn-tnc_Latn data_files: - path: train/eng_Latn-tnc_Latn.jsonl.gz split: train - path: test/eng_Latn-tnc_Latn.jsonl.gz split: test - path: validation/eng_Latn-tnc_Latn.jsonl.gz split: validation - config_name: eng_Latn-aso_Latn data_files: - path: train/eng_Latn-aso_Latn.jsonl.gz split: train - path: test/eng_Latn-aso_Latn.jsonl.gz split: test - path: validation/eng_Latn-aso_Latn.jsonl.gz split: validation - config_name: eng_Latn-cax_Latn data_files: - path: train/eng_Latn-cax_Latn.jsonl.gz split: train - path: test/eng_Latn-cax_Latn.jsonl.gz split: test - path: validation/eng_Latn-cax_Latn.jsonl.gz split: validation - config_name: eng_Latn-xtm_Latn data_files: - path: train/eng_Latn-xtm_Latn.jsonl.gz split: train - path: test/eng_Latn-xtm_Latn.jsonl.gz split: test - path: validation/eng_Latn-xtm_Latn.jsonl.gz split: validation - config_name: eng_Latn-llg_Latn data_files: - path: train/eng_Latn-llg_Latn.jsonl.gz split: train - path: test/eng_Latn-llg_Latn.jsonl.gz split: test - path: validation/eng_Latn-llg_Latn.jsonl.gz split: validation - config_name: eng_Latn-pls_Latn data_files: - path: train/eng_Latn-pls_Latn.jsonl.gz split: train - path: test/eng_Latn-pls_Latn.jsonl.gz split: test - path: validation/eng_Latn-pls_Latn.jsonl.gz split: validation - config_name: eng_Latn-nhi_Latn data_files: - path: train/eng_Latn-nhi_Latn.jsonl.gz split: train - path: test/eng_Latn-nhi_Latn.jsonl.gz split: test - path: validation/eng_Latn-nhi_Latn.jsonl.gz split: validation - config_name: eng_Latn-leu_Latn data_files: - path: train/eng_Latn-leu_Latn.jsonl.gz split: train - path: test/eng_Latn-leu_Latn.jsonl.gz split: test - path: validation/eng_Latn-leu_Latn.jsonl.gz split: validation - config_name: eng_Latn-agn_Latn data_files: - path: train/eng_Latn-agn_Latn.jsonl.gz split: train - path: test/eng_Latn-agn_Latn.jsonl.gz split: test - path: validation/eng_Latn-agn_Latn.jsonl.gz split: validation - config_name: eng_Latn-hus_Latn data_files: - path: train/eng_Latn-hus_Latn.jsonl.gz split: train - path: test/eng_Latn-hus_Latn.jsonl.gz split: test - path: validation/eng_Latn-hus_Latn.jsonl.gz split: validation - config_name: eng_Latn-hvn_Latn data_files: - path: train/eng_Latn-hvn_Latn.jsonl.gz split: train - path: test/eng_Latn-hvn_Latn.jsonl.gz split: test - path: validation/eng_Latn-hvn_Latn.jsonl.gz split: validation - config_name: eng_Latn-gup_Latn data_files: - path: train/eng_Latn-gup_Latn.jsonl.gz split: train - path: test/eng_Latn-gup_Latn.jsonl.gz split: test - path: validation/eng_Latn-gup_Latn.jsonl.gz split: validation - config_name: eng_Latn-ncu_Latn data_files: - path: train/eng_Latn-ncu_Latn.jsonl.gz split: train - path: test/eng_Latn-ncu_Latn.jsonl.gz split: test - path: validation/eng_Latn-ncu_Latn.jsonl.gz split: validation - config_name: eng_Latn-ilo_Latn data_files: - path: train/eng_Latn-ilo_Latn.jsonl.gz split: train - path: test/eng_Latn-ilo_Latn.jsonl.gz split: test - path: validation/eng_Latn-ilo_Latn.jsonl.gz split: validation - config_name: eng_Latn-cjv_Latn data_files: - path: train/eng_Latn-cjv_Latn.jsonl.gz split: train - path: test/eng_Latn-cjv_Latn.jsonl.gz split: test - path: validation/eng_Latn-cjv_Latn.jsonl.gz split: validation - config_name: eng_Latn-cbi_Latn data_files: - path: train/eng_Latn-cbi_Latn.jsonl.gz split: train - path: test/eng_Latn-cbi_Latn.jsonl.gz split: test - path: validation/eng_Latn-cbi_Latn.jsonl.gz split: validation - config_name: eng_Latn-sll_Latn data_files: - path: train/eng_Latn-sll_Latn.jsonl.gz split: train - path: test/eng_Latn-sll_Latn.jsonl.gz split: test - path: validation/eng_Latn-sll_Latn.jsonl.gz split: validation - config_name: eng_Latn-gvf_Latn data_files: - path: train/eng_Latn-gvf_Latn.jsonl.gz split: train - path: test/eng_Latn-gvf_Latn.jsonl.gz split: test - path: validation/eng_Latn-gvf_Latn.jsonl.gz split: validation - config_name: eng_Latn-cbk_Latn data_files: - path: train/eng_Latn-cbk_Latn.jsonl.gz split: train - path: test/eng_Latn-cbk_Latn.jsonl.gz split: test - path: validation/eng_Latn-cbk_Latn.jsonl.gz split: validation - config_name: eng_Latn-ctp_Latn data_files: - path: train/eng_Latn-ctp_Latn.jsonl.gz split: train - path: test/eng_Latn-ctp_Latn.jsonl.gz split: test - path: validation/eng_Latn-ctp_Latn.jsonl.gz split: validation - config_name: eng_Latn-rus_Cyrl data_files: - path: train/eng_Latn-rus_Cyrl.jsonl.gz split: train - path: test/eng_Latn-rus_Cyrl.jsonl.gz split: test - path: validation/eng_Latn-rus_Cyrl.jsonl.gz split: validation - config_name: eng_Latn-zpc_Latn data_files: - path: train/eng_Latn-zpc_Latn.jsonl.gz split: train - path: test/eng_Latn-zpc_Latn.jsonl.gz split: test - path: validation/eng_Latn-zpc_Latn.jsonl.gz split: validation - config_name: eng_Latn-dww_Latn data_files: - path: train/eng_Latn-dww_Latn.jsonl.gz split: train - path: test/eng_Latn-dww_Latn.jsonl.gz split: test - path: validation/eng_Latn-dww_Latn.jsonl.gz split: validation - config_name: eng_Latn-haw_Latn data_files: - path: train/eng_Latn-haw_Latn.jsonl.gz split: train - path: test/eng_Latn-haw_Latn.jsonl.gz split: test - path: validation/eng_Latn-haw_Latn.jsonl.gz split: validation - config_name: eng_Latn-hrv_Latn data_files: - path: train/eng_Latn-hrv_Latn.jsonl.gz split: train - path: test/eng_Latn-hrv_Latn.jsonl.gz split: test - path: validation/eng_Latn-hrv_Latn.jsonl.gz split: validation - config_name: eng_Latn-bhg_Latn data_files: - path: train/eng_Latn-bhg_Latn.jsonl.gz split: train - path: test/eng_Latn-bhg_Latn.jsonl.gz split: test - path: validation/eng_Latn-bhg_Latn.jsonl.gz split: validation - config_name: eng_Latn-kyg_Latn data_files: - path: train/eng_Latn-kyg_Latn.jsonl.gz split: train - path: test/eng_Latn-kyg_Latn.jsonl.gz split: test - path: validation/eng_Latn-kyg_Latn.jsonl.gz split: validation - config_name: eng_Latn-are_Latn data_files: - path: train/eng_Latn-are_Latn.jsonl.gz split: train - path: test/eng_Latn-are_Latn.jsonl.gz split: test - path: validation/eng_Latn-are_Latn.jsonl.gz split: validation - config_name: eng_Latn-pma_Latn data_files: - path: train/eng_Latn-pma_Latn.jsonl.gz split: train - path: test/eng_Latn-pma_Latn.jsonl.gz split: test - path: validation/eng_Latn-pma_Latn.jsonl.gz split: validation - config_name: eng_Latn-tcz_Latn data_files: - path: train/eng_Latn-tcz_Latn.jsonl.gz split: train - path: test/eng_Latn-tcz_Latn.jsonl.gz split: test - path: validation/eng_Latn-tcz_Latn.jsonl.gz split: validation - config_name: eng_Latn-mop_Latn data_files: - path: train/eng_Latn-mop_Latn.jsonl.gz split: train - path: test/eng_Latn-mop_Latn.jsonl.gz split: test - path: validation/eng_Latn-mop_Latn.jsonl.gz split: validation - config_name: eng_Latn-maa_Latn data_files: - path: train/eng_Latn-maa_Latn.jsonl.gz split: train - path: test/eng_Latn-maa_Latn.jsonl.gz split: test - path: validation/eng_Latn-maa_Latn.jsonl.gz split: validation - config_name: eng_Latn-row_Latn data_files: - path: train/eng_Latn-row_Latn.jsonl.gz split: train - path: test/eng_Latn-row_Latn.jsonl.gz split: test - path: validation/eng_Latn-row_Latn.jsonl.gz split: validation - config_name: eng_Latn-mcp_Latn data_files: - path: train/eng_Latn-mcp_Latn.jsonl.gz split: train - path: test/eng_Latn-mcp_Latn.jsonl.gz split: test - path: validation/eng_Latn-mcp_Latn.jsonl.gz split: validation - config_name: eng_Latn-bjv_Latn data_files: - path: train/eng_Latn-bjv_Latn.jsonl.gz split: train - path: test/eng_Latn-bjv_Latn.jsonl.gz split: test - path: validation/eng_Latn-bjv_Latn.jsonl.gz split: validation - config_name: eng_Latn-dan_Latn data_files: - path: train/eng_Latn-dan_Latn.jsonl.gz split: train - path: test/eng_Latn-dan_Latn.jsonl.gz split: test - path: validation/eng_Latn-dan_Latn.jsonl.gz split: validation - config_name: eng_Latn-kpw_Latn data_files: - path: train/eng_Latn-kpw_Latn.jsonl.gz split: train - path: test/eng_Latn-kpw_Latn.jsonl.gz split: test - path: validation/eng_Latn-kpw_Latn.jsonl.gz split: validation - config_name: eng_Latn-yal_Latn data_files: - path: train/eng_Latn-yal_Latn.jsonl.gz split: train - path: test/eng_Latn-yal_Latn.jsonl.gz split: test - path: validation/eng_Latn-yal_Latn.jsonl.gz split: validation - config_name: eng_Latn-yrb_Latn data_files: - path: train/eng_Latn-yrb_Latn.jsonl.gz split: train - path: test/eng_Latn-yrb_Latn.jsonl.gz split: test - path: validation/eng_Latn-yrb_Latn.jsonl.gz split: validation - config_name: eng_Latn-piu_Latn data_files: - path: train/eng_Latn-piu_Latn.jsonl.gz split: train - path: test/eng_Latn-piu_Latn.jsonl.gz split: test - path: validation/eng_Latn-piu_Latn.jsonl.gz split: validation - config_name: eng_Latn-kqa_Latn data_files: - path: train/eng_Latn-kqa_Latn.jsonl.gz split: train - path: test/eng_Latn-kqa_Latn.jsonl.gz split: test - path: validation/eng_Latn-kqa_Latn.jsonl.gz split: validation - config_name: eng_Latn-buk_Latn data_files: - path: train/eng_Latn-buk_Latn.jsonl.gz split: train - path: test/eng_Latn-buk_Latn.jsonl.gz split: test - path: validation/eng_Latn-buk_Latn.jsonl.gz split: validation - config_name: eng_Latn-taw_Latn data_files: - path: train/eng_Latn-taw_Latn.jsonl.gz split: train - path: test/eng_Latn-taw_Latn.jsonl.gz split: test - path: validation/eng_Latn-taw_Latn.jsonl.gz split: validation - config_name: eng_Latn-bzj_Latn data_files: - path: train/eng_Latn-bzj_Latn.jsonl.gz split: train - path: test/eng_Latn-bzj_Latn.jsonl.gz split: test - path: validation/eng_Latn-bzj_Latn.jsonl.gz split: validation - config_name: eng_Latn-boa_Latn data_files: - path: train/eng_Latn-boa_Latn.jsonl.gz split: train - path: test/eng_Latn-boa_Latn.jsonl.gz split: test - path: validation/eng_Latn-boa_Latn.jsonl.gz split: validation - config_name: eng_Latn-sua_Latn data_files: - path: train/eng_Latn-sua_Latn.jsonl.gz split: train - path: test/eng_Latn-sua_Latn.jsonl.gz split: test - path: validation/eng_Latn-sua_Latn.jsonl.gz split: validation - config_name: eng_Latn-mwf_Latn data_files: - path: train/eng_Latn-mwf_Latn.jsonl.gz split: train - path: test/eng_Latn-mwf_Latn.jsonl.gz split: test - path: validation/eng_Latn-mwf_Latn.jsonl.gz split: validation - config_name: eng_Latn-deu_Latn data_files: - path: train/eng_Latn-deu_Latn.jsonl.gz split: train - path: test/eng_Latn-deu_Latn.jsonl.gz split: test - path: validation/eng_Latn-deu_Latn.jsonl.gz split: validation - config_name: eng_Latn-ura_Latn data_files: - path: train/eng_Latn-ura_Latn.jsonl.gz split: train - path: test/eng_Latn-ura_Latn.jsonl.gz split: test - path: validation/eng_Latn-ura_Latn.jsonl.gz split: validation - config_name: eng_Latn-aui_Latn data_files: - path: train/eng_Latn-aui_Latn.jsonl.gz split: train - path: test/eng_Latn-aui_Latn.jsonl.gz split: test - path: validation/eng_Latn-aui_Latn.jsonl.gz split: validation - config_name: eng_Latn-wim_Latn data_files: - path: train/eng_Latn-wim_Latn.jsonl.gz split: train - path: test/eng_Latn-wim_Latn.jsonl.gz split: test - path: validation/eng_Latn-wim_Latn.jsonl.gz split: validation - config_name: eng_Latn-bzh_Latn data_files: - path: train/eng_Latn-bzh_Latn.jsonl.gz split: train - path: test/eng_Latn-bzh_Latn.jsonl.gz split: test - path: validation/eng_Latn-bzh_Latn.jsonl.gz split: validation - config_name: eng_Latn-nld_Latn data_files: - path: train/eng_Latn-nld_Latn.jsonl.gz split: train - path: test/eng_Latn-nld_Latn.jsonl.gz split: test - path: validation/eng_Latn-nld_Latn.jsonl.gz split: validation - config_name: eng_Latn-ory_Orya data_files: - path: train/eng_Latn-ory_Orya.jsonl.gz split: train - path: test/eng_Latn-ory_Orya.jsonl.gz split: test - path: validation/eng_Latn-ory_Orya.jsonl.gz split: validation - config_name: eng_Latn-ppo_Latn data_files: - path: train/eng_Latn-ppo_Latn.jsonl.gz split: train - path: test/eng_Latn-ppo_Latn.jsonl.gz split: test - path: validation/eng_Latn-ppo_Latn.jsonl.gz split: validation - config_name: eng_Latn-epo_Latn data_files: - path: train/eng_Latn-epo_Latn.jsonl.gz split: train - path: test/eng_Latn-epo_Latn.jsonl.gz split: test - path: validation/eng_Latn-epo_Latn.jsonl.gz split: validation - config_name: eng_Latn-hop_Latn data_files: - path: train/eng_Latn-hop_Latn.jsonl.gz split: train - path: test/eng_Latn-hop_Latn.jsonl.gz split: test - path: validation/eng_Latn-hop_Latn.jsonl.gz split: validation - config_name: eng_Latn-gdn_Latn data_files: - path: train/eng_Latn-gdn_Latn.jsonl.gz split: train - path: test/eng_Latn-gdn_Latn.jsonl.gz split: test - path: validation/eng_Latn-gdn_Latn.jsonl.gz split: validation - config_name: eng_Latn-tlf_Latn data_files: - path: train/eng_Latn-tlf_Latn.jsonl.gz split: train - path: test/eng_Latn-tlf_Latn.jsonl.gz split: test - path: validation/eng_Latn-tlf_Latn.jsonl.gz split: validation - config_name: eng_Latn-tiw_Latn data_files: - path: train/eng_Latn-tiw_Latn.jsonl.gz split: train - path: test/eng_Latn-tiw_Latn.jsonl.gz split: test - path: validation/eng_Latn-tiw_Latn.jsonl.gz split: validation - config_name: eng_Latn-sja_Latn data_files: - path: train/eng_Latn-sja_Latn.jsonl.gz split: train - path: test/eng_Latn-sja_Latn.jsonl.gz split: test - path: validation/eng_Latn-sja_Latn.jsonl.gz split: validation - config_name: eng_Latn-kdl_Latn data_files: - path: train/eng_Latn-kdl_Latn.jsonl.gz split: train - path: test/eng_Latn-kdl_Latn.jsonl.gz split: test - path: validation/eng_Latn-kdl_Latn.jsonl.gz split: validation - config_name: eng_Latn-chk_Latn data_files: - path: train/eng_Latn-chk_Latn.jsonl.gz split: train - path: test/eng_Latn-chk_Latn.jsonl.gz split: test - path: validation/eng_Latn-chk_Latn.jsonl.gz split: validation - config_name: eng_Latn-kdc_Latn data_files: - path: train/eng_Latn-kdc_Latn.jsonl.gz split: train - path: test/eng_Latn-kdc_Latn.jsonl.gz split: test - path: validation/eng_Latn-kdc_Latn.jsonl.gz split: validation - config_name: eng_Latn-gng_Latn data_files: - path: train/eng_Latn-gng_Latn.jsonl.gz split: train - path: test/eng_Latn-gng_Latn.jsonl.gz split: test - path: validation/eng_Latn-gng_Latn.jsonl.gz split: validation - config_name: eng_Latn-nko_Latn data_files: - path: train/eng_Latn-nko_Latn.jsonl.gz split: train - path: test/eng_Latn-nko_Latn.jsonl.gz split: test - path: validation/eng_Latn-nko_Latn.jsonl.gz split: validation - config_name: eng_Latn-wer_Latn data_files: - path: train/eng_Latn-wer_Latn.jsonl.gz split: train - path: test/eng_Latn-wer_Latn.jsonl.gz split: test - path: validation/eng_Latn-wer_Latn.jsonl.gz split: validation - config_name: eng_Latn-mhl_Latn data_files: - path: train/eng_Latn-mhl_Latn.jsonl.gz split: train - path: test/eng_Latn-mhl_Latn.jsonl.gz split: test - path: validation/eng_Latn-mhl_Latn.jsonl.gz split: validation - config_name: eng_Latn-toc_Latn data_files: - path: train/eng_Latn-toc_Latn.jsonl.gz split: train - path: test/eng_Latn-toc_Latn.jsonl.gz split: test - path: validation/eng_Latn-toc_Latn.jsonl.gz split: validation - config_name: eng_Latn-cbs_Latn data_files: - path: train/eng_Latn-cbs_Latn.jsonl.gz split: train - path: test/eng_Latn-cbs_Latn.jsonl.gz split: test - path: validation/eng_Latn-cbs_Latn.jsonl.gz split: validation - config_name: eng_Latn-qub_Latn data_files: - path: train/eng_Latn-qub_Latn.jsonl.gz split: train - path: test/eng_Latn-qub_Latn.jsonl.gz split: test - path: validation/eng_Latn-qub_Latn.jsonl.gz split: validation - config_name: eng_Latn-auc_Latn data_files: - path: train/eng_Latn-auc_Latn.jsonl.gz split: train - path: test/eng_Latn-auc_Latn.jsonl.gz split: test - path: validation/eng_Latn-auc_Latn.jsonl.gz split: validation - config_name: eng_Latn-kpr_Latn data_files: - path: train/eng_Latn-kpr_Latn.jsonl.gz split: train - path: test/eng_Latn-kpr_Latn.jsonl.gz split: test - path: validation/eng_Latn-kpr_Latn.jsonl.gz split: validation - config_name: eng_Latn-hat_Latn data_files: - path: train/eng_Latn-hat_Latn.jsonl.gz split: train - path: test/eng_Latn-hat_Latn.jsonl.gz split: test - path: validation/eng_Latn-hat_Latn.jsonl.gz split: validation - config_name: eng_Latn-sny_Latn data_files: - path: train/eng_Latn-sny_Latn.jsonl.gz split: train - path: test/eng_Latn-sny_Latn.jsonl.gz split: test - path: validation/eng_Latn-sny_Latn.jsonl.gz split: validation - config_name: eng_Latn-byr_Latn data_files: - path: train/eng_Latn-byr_Latn.jsonl.gz split: train - path: test/eng_Latn-byr_Latn.jsonl.gz split: test - path: validation/eng_Latn-byr_Latn.jsonl.gz split: validation - config_name: eng_Latn-emp_Latn data_files: - path: train/eng_Latn-emp_Latn.jsonl.gz split: train - path: test/eng_Latn-emp_Latn.jsonl.gz split: test - path: validation/eng_Latn-emp_Latn.jsonl.gz split: validation - config_name: eng_Latn-kwi_Latn data_files: - path: train/eng_Latn-kwi_Latn.jsonl.gz split: train - path: test/eng_Latn-kwi_Latn.jsonl.gz split: test - path: validation/eng_Latn-kwi_Latn.jsonl.gz split: validation - config_name: eng_Latn-gum_Latn data_files: - path: train/eng_Latn-gum_Latn.jsonl.gz split: train - path: test/eng_Latn-gum_Latn.jsonl.gz split: test - path: validation/eng_Latn-gum_Latn.jsonl.gz split: validation - config_name: eng_Latn-mbj_Latn data_files: - path: train/eng_Latn-mbj_Latn.jsonl.gz split: train - path: test/eng_Latn-mbj_Latn.jsonl.gz split: test - path: validation/eng_Latn-mbj_Latn.jsonl.gz split: validation - config_name: eng_Latn-sey_Latn data_files: - path: train/eng_Latn-sey_Latn.jsonl.gz split: train - path: test/eng_Latn-sey_Latn.jsonl.gz split: test - path: validation/eng_Latn-sey_Latn.jsonl.gz split: validation - config_name: eng_Latn-alp_Latn data_files: - path: train/eng_Latn-alp_Latn.jsonl.gz split: train - path: test/eng_Latn-alp_Latn.jsonl.gz split: test - path: validation/eng_Latn-alp_Latn.jsonl.gz split: validation - config_name: eng_Latn-gmv_Latn data_files: - path: train/eng_Latn-gmv_Latn.jsonl.gz split: train - path: test/eng_Latn-gmv_Latn.jsonl.gz split: test - path: validation/eng_Latn-gmv_Latn.jsonl.gz split: validation - config_name: eng_Latn-poe_Latn data_files: - path: train/eng_Latn-poe_Latn.jsonl.gz split: train - path: test/eng_Latn-poe_Latn.jsonl.gz split: test - path: validation/eng_Latn-poe_Latn.jsonl.gz split: validation - config_name: eng_Latn-etr_Latn data_files: - path: train/eng_Latn-etr_Latn.jsonl.gz split: train - path: test/eng_Latn-etr_Latn.jsonl.gz split: test - path: validation/eng_Latn-etr_Latn.jsonl.gz split: validation - config_name: eng_Latn-abt_Latn data_files: - path: train/eng_Latn-abt_Latn.jsonl.gz split: train - path: test/eng_Latn-abt_Latn.jsonl.gz split: test - path: validation/eng_Latn-abt_Latn.jsonl.gz split: validation - config_name: eng_Latn-tuf_Latn data_files: - path: train/eng_Latn-tuf_Latn.jsonl.gz split: train - path: test/eng_Latn-tuf_Latn.jsonl.gz split: test - path: validation/eng_Latn-tuf_Latn.jsonl.gz split: validation - config_name: eng_Latn-dob_Latn data_files: - path: train/eng_Latn-dob_Latn.jsonl.gz split: train - path: test/eng_Latn-dob_Latn.jsonl.gz split: test - path: validation/eng_Latn-dob_Latn.jsonl.gz split: validation - config_name: eng_Latn-nys_Latn data_files: - path: train/eng_Latn-nys_Latn.jsonl.gz split: train - path: test/eng_Latn-nys_Latn.jsonl.gz split: test - path: validation/eng_Latn-nys_Latn.jsonl.gz split: validation - config_name: eng_Latn-srn_Latn data_files: - path: train/eng_Latn-srn_Latn.jsonl.gz split: train - path: test/eng_Latn-srn_Latn.jsonl.gz split: test - path: validation/eng_Latn-srn_Latn.jsonl.gz split: validation - config_name: eng_Latn-kpf_Latn data_files: - path: train/eng_Latn-kpf_Latn.jsonl.gz split: train - path: test/eng_Latn-kpf_Latn.jsonl.gz split: test - path: validation/eng_Latn-kpf_Latn.jsonl.gz split: validation - config_name: eng_Latn-mbt_Latn data_files: - path: train/eng_Latn-mbt_Latn.jsonl.gz split: train - path: test/eng_Latn-mbt_Latn.jsonl.gz split: test - path: validation/eng_Latn-mbt_Latn.jsonl.gz split: validation - config_name: eng_Latn-stp_Latn data_files: - path: train/eng_Latn-stp_Latn.jsonl.gz split: train - path: test/eng_Latn-stp_Latn.jsonl.gz split: test - path: validation/eng_Latn-stp_Latn.jsonl.gz split: validation - config_name: eng_Latn-trc_Latn data_files: - path: train/eng_Latn-trc_Latn.jsonl.gz split: train - path: test/eng_Latn-trc_Latn.jsonl.gz split: test - path: validation/eng_Latn-trc_Latn.jsonl.gz split: validation - config_name: eng_Latn-for_Latn data_files: - path: train/eng_Latn-for_Latn.jsonl.gz split: train - path: test/eng_Latn-for_Latn.jsonl.gz split: test - path: validation/eng_Latn-for_Latn.jsonl.gz split: validation - config_name: eng_Latn-yad_Latn data_files: - path: train/eng_Latn-yad_Latn.jsonl.gz split: train - path: test/eng_Latn-yad_Latn.jsonl.gz split: test - path: validation/eng_Latn-yad_Latn.jsonl.gz split: validation - config_name: eng_Latn-cme_Latn data_files: - path: train/eng_Latn-cme_Latn.jsonl.gz split: train - path: test/eng_Latn-cme_Latn.jsonl.gz split: test - path: validation/eng_Latn-cme_Latn.jsonl.gz split: validation - config_name: eng_Latn-bba_Latn data_files: - path: train/eng_Latn-bba_Latn.jsonl.gz split: train - path: test/eng_Latn-bba_Latn.jsonl.gz split: test - path: validation/eng_Latn-bba_Latn.jsonl.gz split: validation - config_name: eng_Latn-mxq_Latn data_files: - path: train/eng_Latn-mxq_Latn.jsonl.gz split: train - path: test/eng_Latn-mxq_Latn.jsonl.gz split: test - path: validation/eng_Latn-mxq_Latn.jsonl.gz split: validation - config_name: eng_Latn-dwy_Latn data_files: - path: train/eng_Latn-dwy_Latn.jsonl.gz split: train - path: test/eng_Latn-dwy_Latn.jsonl.gz split: test - path: validation/eng_Latn-dwy_Latn.jsonl.gz split: validation - config_name: eng_Latn-atg_Latn data_files: - path: train/eng_Latn-atg_Latn.jsonl.gz split: train - path: test/eng_Latn-atg_Latn.jsonl.gz split: test - path: validation/eng_Latn-atg_Latn.jsonl.gz split: validation - config_name: eng_Latn-ncj_Latn data_files: - path: train/eng_Latn-ncj_Latn.jsonl.gz split: train - path: test/eng_Latn-ncj_Latn.jsonl.gz split: test - path: validation/eng_Latn-ncj_Latn.jsonl.gz split: validation - config_name: eng_Latn-mpm_Latn data_files: - path: train/eng_Latn-mpm_Latn.jsonl.gz split: train - path: test/eng_Latn-mpm_Latn.jsonl.gz split: test - path: validation/eng_Latn-mpm_Latn.jsonl.gz split: validation - config_name: eng_Latn-kqc_Latn data_files: - path: train/eng_Latn-kqc_Latn.jsonl.gz split: train - path: test/eng_Latn-kqc_Latn.jsonl.gz split: test - path: validation/eng_Latn-kqc_Latn.jsonl.gz split: validation - config_name: eng_Latn-knv_Latn data_files: - path: train/eng_Latn-knv_Latn.jsonl.gz split: train - path: test/eng_Latn-knv_Latn.jsonl.gz split: test - path: validation/eng_Latn-knv_Latn.jsonl.gz split: validation - config_name: eng_Latn-upv_Latn data_files: - path: train/eng_Latn-upv_Latn.jsonl.gz split: train - path: test/eng_Latn-upv_Latn.jsonl.gz split: test - path: validation/eng_Latn-upv_Latn.jsonl.gz split: validation - config_name: eng_Latn-yut_Latn data_files: - path: train/eng_Latn-yut_Latn.jsonl.gz split: train - path: test/eng_Latn-yut_Latn.jsonl.gz split: test - path: validation/eng_Latn-yut_Latn.jsonl.gz split: validation - config_name: eng_Latn-kje_Latn data_files: - path: train/eng_Latn-kje_Latn.jsonl.gz split: train - path: test/eng_Latn-kje_Latn.jsonl.gz split: test - path: validation/eng_Latn-kje_Latn.jsonl.gz split: validation - config_name: eng_Latn-okv_Latn data_files: - path: train/eng_Latn-okv_Latn.jsonl.gz split: train - path: test/eng_Latn-okv_Latn.jsonl.gz split: test - path: validation/eng_Latn-okv_Latn.jsonl.gz split: validation - config_name: eng_Latn-tof_Latn data_files: - path: train/eng_Latn-tof_Latn.jsonl.gz split: train - path: test/eng_Latn-tof_Latn.jsonl.gz split: test - path: validation/eng_Latn-tof_Latn.jsonl.gz split: validation - config_name: eng_Latn-faa_Latn data_files: - path: train/eng_Latn-faa_Latn.jsonl.gz split: train - path: test/eng_Latn-faa_Latn.jsonl.gz split: test - path: validation/eng_Latn-faa_Latn.jsonl.gz split: validation - config_name: eng_Latn-mya_Latn data_files: - path: train/eng_Latn-mya_Latn.jsonl.gz split: train - path: test/eng_Latn-mya_Latn.jsonl.gz split: test - path: validation/eng_Latn-mya_Latn.jsonl.gz split: validation - config_name: eng_Latn-hto_Latn data_files: - path: train/eng_Latn-hto_Latn.jsonl.gz split: train - path: test/eng_Latn-hto_Latn.jsonl.gz split: test - path: validation/eng_Latn-hto_Latn.jsonl.gz split: validation - config_name: eng_Latn-wiv_Latn data_files: - path: train/eng_Latn-wiv_Latn.jsonl.gz split: train - path: test/eng_Latn-wiv_Latn.jsonl.gz split: test - path: validation/eng_Latn-wiv_Latn.jsonl.gz split: validation - config_name: eng_Latn-vid_Latn data_files: - path: train/eng_Latn-vid_Latn.jsonl.gz split: train - path: test/eng_Latn-vid_Latn.jsonl.gz split: test - path: validation/eng_Latn-vid_Latn.jsonl.gz split: validation - config_name: eng_Latn-xla_Latn data_files: - path: train/eng_Latn-xla_Latn.jsonl.gz split: train - path: test/eng_Latn-xla_Latn.jsonl.gz split: test - path: validation/eng_Latn-xla_Latn.jsonl.gz split: validation - config_name: eng_Latn-snn_Latn data_files: - path: train/eng_Latn-snn_Latn.jsonl.gz split: train - path: test/eng_Latn-snn_Latn.jsonl.gz split: test - path: validation/eng_Latn-snn_Latn.jsonl.gz split: validation - config_name: eng_Latn-ycn_Latn data_files: - path: train/eng_Latn-ycn_Latn.jsonl.gz split: train - path: test/eng_Latn-ycn_Latn.jsonl.gz split: test - path: validation/eng_Latn-ycn_Latn.jsonl.gz split: validation - config_name: eng_Latn-mcb_Latn data_files: - path: train/eng_Latn-mcb_Latn.jsonl.gz split: train - path: test/eng_Latn-mcb_Latn.jsonl.gz split: test - path: validation/eng_Latn-mcb_Latn.jsonl.gz split: validation - config_name: eng_Latn-mal_Mlym data_files: - path: train/eng_Latn-mal_Mlym.jsonl.gz split: train - path: test/eng_Latn-mal_Mlym.jsonl.gz split: test - path: validation/eng_Latn-mal_Mlym.jsonl.gz split: validation - config_name: eng_Latn-sgb_Latn data_files: - path: train/eng_Latn-sgb_Latn.jsonl.gz split: train - path: test/eng_Latn-sgb_Latn.jsonl.gz split: test - path: validation/eng_Latn-sgb_Latn.jsonl.gz split: validation - config_name: eng_Latn-box_Latn data_files: - path: train/eng_Latn-box_Latn.jsonl.gz split: train - path: test/eng_Latn-box_Latn.jsonl.gz split: test - path: validation/eng_Latn-box_Latn.jsonl.gz split: validation - config_name: eng_Latn-amu_Latn data_files: - path: train/eng_Latn-amu_Latn.jsonl.gz split: train - path: test/eng_Latn-amu_Latn.jsonl.gz split: test - path: validation/eng_Latn-amu_Latn.jsonl.gz split: validation - config_name: eng_Latn-cni_Latn data_files: - path: train/eng_Latn-cni_Latn.jsonl.gz split: train - path: test/eng_Latn-cni_Latn.jsonl.gz split: test - path: validation/eng_Latn-cni_Latn.jsonl.gz split: validation - config_name: eng_Latn-byx_Latn data_files: - path: train/eng_Latn-byx_Latn.jsonl.gz split: train - path: test/eng_Latn-byx_Latn.jsonl.gz split: test - path: validation/eng_Latn-byx_Latn.jsonl.gz split: validation - config_name: eng_Latn-udu_Latn data_files: - path: train/eng_Latn-udu_Latn.jsonl.gz split: train - path: test/eng_Latn-udu_Latn.jsonl.gz split: test - path: validation/eng_Latn-udu_Latn.jsonl.gz split: validation - config_name: eng_Latn-jid_Latn data_files: - path: train/eng_Latn-jid_Latn.jsonl.gz split: train - path: test/eng_Latn-jid_Latn.jsonl.gz split: test - path: validation/eng_Latn-jid_Latn.jsonl.gz split: validation - config_name: eng_Latn-nlg_Latn data_files: - path: train/eng_Latn-nlg_Latn.jsonl.gz split: train - path: test/eng_Latn-nlg_Latn.jsonl.gz split: test - path: validation/eng_Latn-nlg_Latn.jsonl.gz split: validation - config_name: eng_Latn-wuv_Latn data_files: - path: train/eng_Latn-wuv_Latn.jsonl.gz split: train - path: test/eng_Latn-wuv_Latn.jsonl.gz split: test - path: validation/eng_Latn-wuv_Latn.jsonl.gz split: validation - config_name: eng_Latn-mto_Latn data_files: - path: train/eng_Latn-mto_Latn.jsonl.gz split: train - path: test/eng_Latn-mto_Latn.jsonl.gz split: test - path: validation/eng_Latn-mto_Latn.jsonl.gz split: validation - config_name: eng_Latn-mcd_Latn data_files: - path: train/eng_Latn-mcd_Latn.jsonl.gz split: train - path: test/eng_Latn-mcd_Latn.jsonl.gz split: test - path: validation/eng_Latn-mcd_Latn.jsonl.gz split: validation - config_name: eng_Latn-bus_Latn data_files: - path: train/eng_Latn-bus_Latn.jsonl.gz split: train - path: test/eng_Latn-bus_Latn.jsonl.gz split: test - path: validation/eng_Latn-bus_Latn.jsonl.gz split: validation - config_name: eng_Latn-glk_Arab data_files: - path: train/eng_Latn-glk_Arab.jsonl.gz split: train - path: test/eng_Latn-glk_Arab.jsonl.gz split: test - path: validation/eng_Latn-glk_Arab.jsonl.gz split: validation - config_name: eng_Latn-too_Latn data_files: - path: train/eng_Latn-too_Latn.jsonl.gz split: train - path: test/eng_Latn-too_Latn.jsonl.gz split: test - path: validation/eng_Latn-too_Latn.jsonl.gz split: validation - config_name: eng_Latn-mpp_Latn data_files: - path: train/eng_Latn-mpp_Latn.jsonl.gz split: train - path: test/eng_Latn-mpp_Latn.jsonl.gz split: test - path: validation/eng_Latn-mpp_Latn.jsonl.gz split: validation - config_name: eng_Latn-zaj_Latn data_files: - path: train/eng_Latn-zaj_Latn.jsonl.gz split: train - path: test/eng_Latn-zaj_Latn.jsonl.gz split: test - path: validation/eng_Latn-zaj_Latn.jsonl.gz split: validation - config_name: eng_Latn-yva_Latn data_files: - path: train/eng_Latn-yva_Latn.jsonl.gz split: train - path: test/eng_Latn-yva_Latn.jsonl.gz split: test - path: validation/eng_Latn-yva_Latn.jsonl.gz split: validation - config_name: eng_Latn-awx_Latn data_files: - path: train/eng_Latn-awx_Latn.jsonl.gz split: train - path: test/eng_Latn-awx_Latn.jsonl.gz split: test - path: validation/eng_Latn-awx_Latn.jsonl.gz split: validation - config_name: eng_Latn-ian_Latn data_files: - path: train/eng_Latn-ian_Latn.jsonl.gz split: train - path: test/eng_Latn-ian_Latn.jsonl.gz split: test - path: validation/eng_Latn-ian_Latn.jsonl.gz split: validation - config_name: eng_Latn-otq_Latn data_files: - path: train/eng_Latn-otq_Latn.jsonl.gz split: train - path: test/eng_Latn-otq_Latn.jsonl.gz split: test - path: validation/eng_Latn-otq_Latn.jsonl.gz split: validation - config_name: eng_Latn-fra_Latn data_files: - path: train/eng_Latn-fra_Latn.jsonl.gz split: train - path: test/eng_Latn-fra_Latn.jsonl.gz split: test - path: validation/eng_Latn-fra_Latn.jsonl.gz split: validation - config_name: eng_Latn-zlm_Latn data_files: - path: train/eng_Latn-zlm_Latn.jsonl.gz split: train - path: test/eng_Latn-zlm_Latn.jsonl.gz split: test - path: validation/eng_Latn-zlm_Latn.jsonl.gz split: validation - config_name: eng_Latn-ptu_Latn data_files: - path: train/eng_Latn-ptu_Latn.jsonl.gz split: train - path: test/eng_Latn-ptu_Latn.jsonl.gz split: test - path: validation/eng_Latn-ptu_Latn.jsonl.gz split: validation - config_name: eng_Latn-apr_Latn data_files: - path: train/eng_Latn-apr_Latn.jsonl.gz split: train - path: test/eng_Latn-apr_Latn.jsonl.gz split: test - path: validation/eng_Latn-apr_Latn.jsonl.gz split: validation - config_name: eng_Latn-zab_Latn data_files: - path: train/eng_Latn-zab_Latn.jsonl.gz split: train - path: test/eng_Latn-zab_Latn.jsonl.gz split: test - path: validation/eng_Latn-zab_Latn.jsonl.gz split: validation - config_name: eng_Latn-kwf_Latn data_files: - path: train/eng_Latn-kwf_Latn.jsonl.gz split: train - path: test/eng_Latn-kwf_Latn.jsonl.gz split: test - path: validation/eng_Latn-kwf_Latn.jsonl.gz split: validation - config_name: eng_Latn-cya_Latn data_files: - path: train/eng_Latn-cya_Latn.jsonl.gz split: train - path: test/eng_Latn-cya_Latn.jsonl.gz split: test - path: validation/eng_Latn-cya_Latn.jsonl.gz split: validation - config_name: eng_Latn-nna_Latn data_files: - path: train/eng_Latn-nna_Latn.jsonl.gz split: train - path: test/eng_Latn-nna_Latn.jsonl.gz split: test - path: validation/eng_Latn-nna_Latn.jsonl.gz split: validation - config_name: eng_Latn-wnc_Latn data_files: - path: train/eng_Latn-wnc_Latn.jsonl.gz split: train - path: test/eng_Latn-wnc_Latn.jsonl.gz split: test - path: validation/eng_Latn-wnc_Latn.jsonl.gz split: validation - config_name: eng_Latn-dad_Latn data_files: - path: train/eng_Latn-dad_Latn.jsonl.gz split: train - path: test/eng_Latn-dad_Latn.jsonl.gz split: test - path: validation/eng_Latn-dad_Latn.jsonl.gz split: validation - config_name: eng_Latn-opm_Latn data_files: - path: train/eng_Latn-opm_Latn.jsonl.gz split: train - path: test/eng_Latn-opm_Latn.jsonl.gz split: test - path: validation/eng_Latn-opm_Latn.jsonl.gz split: validation - config_name: eng_Latn-zaw_Latn data_files: - path: train/eng_Latn-zaw_Latn.jsonl.gz split: train - path: test/eng_Latn-zaw_Latn.jsonl.gz split: test - path: validation/eng_Latn-zaw_Latn.jsonl.gz split: validation - config_name: eng_Latn-agd_Latn data_files: - path: train/eng_Latn-agd_Latn.jsonl.gz split: train - path: test/eng_Latn-agd_Latn.jsonl.gz split: test - path: validation/eng_Latn-agd_Latn.jsonl.gz split: validation - config_name: eng_Latn-qxo_Latn data_files: - path: train/eng_Latn-qxo_Latn.jsonl.gz split: train - path: test/eng_Latn-qxo_Latn.jsonl.gz split: test - path: validation/eng_Latn-qxo_Latn.jsonl.gz split: validation - config_name: eng_Latn-ign_Latn data_files: - path: train/eng_Latn-ign_Latn.jsonl.gz split: train - path: test/eng_Latn-ign_Latn.jsonl.gz split: test - path: validation/eng_Latn-ign_Latn.jsonl.gz split: validation - config_name: eng_Latn-cak_Latn data_files: - path: train/eng_Latn-cak_Latn.jsonl.gz split: train - path: test/eng_Latn-cak_Latn.jsonl.gz split: test - path: validation/eng_Latn-cak_Latn.jsonl.gz split: validation - config_name: eng_Latn-top_Latn data_files: - path: train/eng_Latn-top_Latn.jsonl.gz split: train - path: test/eng_Latn-top_Latn.jsonl.gz split: test - path: validation/eng_Latn-top_Latn.jsonl.gz split: validation - config_name: eng_Latn-tgk_Cyrl data_files: - path: train/eng_Latn-tgk_Cyrl.jsonl.gz split: train - path: test/eng_Latn-tgk_Cyrl.jsonl.gz split: test - path: validation/eng_Latn-tgk_Cyrl.jsonl.gz split: validation - config_name: eng_Latn-awb_Latn data_files: - path: train/eng_Latn-awb_Latn.jsonl.gz split: train - path: test/eng_Latn-awb_Latn.jsonl.gz split: test - path: validation/eng_Latn-awb_Latn.jsonl.gz split: validation - config_name: eng_Latn-cnl_Latn data_files: - path: train/eng_Latn-cnl_Latn.jsonl.gz split: train - path: test/eng_Latn-cnl_Latn.jsonl.gz split: test - path: validation/eng_Latn-cnl_Latn.jsonl.gz split: validation - config_name: eng_Latn-kgp_Latn data_files: - path: train/eng_Latn-kgp_Latn.jsonl.gz split: train - path: test/eng_Latn-kgp_Latn.jsonl.gz split: test - path: validation/eng_Latn-kgp_Latn.jsonl.gz split: validation - config_name: eng_Latn-khs_Latn data_files: - path: train/eng_Latn-khs_Latn.jsonl.gz split: train - path: test/eng_Latn-khs_Latn.jsonl.gz split: test - path: validation/eng_Latn-khs_Latn.jsonl.gz split: validation - config_name: eng_Latn-abx_Latn data_files: - path: train/eng_Latn-abx_Latn.jsonl.gz split: train - path: test/eng_Latn-abx_Latn.jsonl.gz split: test - path: validation/eng_Latn-abx_Latn.jsonl.gz split: validation - config_name: eng_Latn-mcq_Latn data_files: - path: train/eng_Latn-mcq_Latn.jsonl.gz split: train - path: test/eng_Latn-mcq_Latn.jsonl.gz split: test - path: validation/eng_Latn-mcq_Latn.jsonl.gz split: validation - config_name: eng_Latn-chd_Latn data_files: - path: train/eng_Latn-chd_Latn.jsonl.gz split: train - path: test/eng_Latn-chd_Latn.jsonl.gz split: test - path: validation/eng_Latn-chd_Latn.jsonl.gz split: validation - config_name: eng_Latn-kud_Latn data_files: - path: train/eng_Latn-kud_Latn.jsonl.gz split: train - path: test/eng_Latn-kud_Latn.jsonl.gz split: test - path: validation/eng_Latn-kud_Latn.jsonl.gz split: validation - config_name: eng_Latn-nak_Latn data_files: - path: train/eng_Latn-nak_Latn.jsonl.gz split: train - path: test/eng_Latn-nak_Latn.jsonl.gz split: test - path: validation/eng_Latn-nak_Latn.jsonl.gz split: validation - config_name: eng_Latn-mux_Latn data_files: - path: train/eng_Latn-mux_Latn.jsonl.gz split: train - path: test/eng_Latn-mux_Latn.jsonl.gz split: test - path: validation/eng_Latn-mux_Latn.jsonl.gz split: validation - config_name: eng_Latn-tzo_Latn data_files: - path: train/eng_Latn-tzo_Latn.jsonl.gz split: train - path: test/eng_Latn-tzo_Latn.jsonl.gz split: test - path: validation/eng_Latn-tzo_Latn.jsonl.gz split: validation - config_name: eng_Latn-mav_Latn data_files: - path: train/eng_Latn-mav_Latn.jsonl.gz split: train - path: test/eng_Latn-mav_Latn.jsonl.gz split: test - path: validation/eng_Latn-mav_Latn.jsonl.gz split: validation - config_name: eng_Latn-avt_Latn data_files: - path: train/eng_Latn-avt_Latn.jsonl.gz split: train - path: test/eng_Latn-avt_Latn.jsonl.gz split: test - path: validation/eng_Latn-avt_Latn.jsonl.gz split: validation - config_name: eng_Latn-bjz_Latn data_files: - path: train/eng_Latn-bjz_Latn.jsonl.gz split: train - path: test/eng_Latn-bjz_Latn.jsonl.gz split: test - path: validation/eng_Latn-bjz_Latn.jsonl.gz split: validation - config_name: eng_Latn-ptp_Latn data_files: - path: train/eng_Latn-ptp_Latn.jsonl.gz split: train - path: test/eng_Latn-ptp_Latn.jsonl.gz split: test - path: validation/eng_Latn-ptp_Latn.jsonl.gz split: validation - config_name: eng_Latn-gnw_Latn data_files: - path: train/eng_Latn-gnw_Latn.jsonl.gz split: train - path: test/eng_Latn-gnw_Latn.jsonl.gz split: test - path: validation/eng_Latn-gnw_Latn.jsonl.gz split: validation - config_name: eng_Latn-cub_Latn data_files: - path: train/eng_Latn-cub_Latn.jsonl.gz split: train - path: test/eng_Latn-cub_Latn.jsonl.gz split: test - path: validation/eng_Latn-cub_Latn.jsonl.gz split: validation - config_name: eng_Latn-hmo_Latn data_files: - path: train/eng_Latn-hmo_Latn.jsonl.gz split: train - path: test/eng_Latn-hmo_Latn.jsonl.gz split: test - path: validation/eng_Latn-hmo_Latn.jsonl.gz split: validation - config_name: eng_Latn-kkl_Latn data_files: - path: train/eng_Latn-kkl_Latn.jsonl.gz split: train - path: test/eng_Latn-kkl_Latn.jsonl.gz split: test - path: validation/eng_Latn-kkl_Latn.jsonl.gz split: validation - config_name: eng_Latn-nou_Latn data_files: - path: train/eng_Latn-nou_Latn.jsonl.gz split: train - path: test/eng_Latn-nou_Latn.jsonl.gz split: test - path: validation/eng_Latn-nou_Latn.jsonl.gz split: validation - config_name: eng_Latn-bre_Latn data_files: - path: train/eng_Latn-bre_Latn.jsonl.gz split: train - path: test/eng_Latn-bre_Latn.jsonl.gz split: test - path: validation/eng_Latn-bre_Latn.jsonl.gz split: validation - config_name: eng_Latn-sim_Latn data_files: - path: train/eng_Latn-sim_Latn.jsonl.gz split: train - path: test/eng_Latn-sim_Latn.jsonl.gz split: test - path: validation/eng_Latn-sim_Latn.jsonl.gz split: validation - config_name: eng_Latn-sbk_Latn data_files: - path: train/eng_Latn-sbk_Latn.jsonl.gz split: train - path: test/eng_Latn-sbk_Latn.jsonl.gz split: test - path: validation/eng_Latn-sbk_Latn.jsonl.gz split: validation - config_name: eng_Latn-nsn_Latn data_files: - path: train/eng_Latn-nsn_Latn.jsonl.gz split: train - path: test/eng_Latn-nsn_Latn.jsonl.gz split: test - path: validation/eng_Latn-nsn_Latn.jsonl.gz split: validation - config_name: eng_Latn-mva_Latn data_files: - path: train/eng_Latn-mva_Latn.jsonl.gz split: train - path: test/eng_Latn-mva_Latn.jsonl.gz split: test - path: validation/eng_Latn-mva_Latn.jsonl.gz split: validation - config_name: eng_Latn-kkc_Latn data_files: - path: train/eng_Latn-kkc_Latn.jsonl.gz split: train - path: test/eng_Latn-kkc_Latn.jsonl.gz split: test - path: validation/eng_Latn-kkc_Latn.jsonl.gz split: validation - config_name: eng_Latn-gvc_Latn data_files: - path: train/eng_Latn-gvc_Latn.jsonl.gz split: train - path: test/eng_Latn-gvc_Latn.jsonl.gz split: test - path: validation/eng_Latn-gvc_Latn.jsonl.gz split: validation - config_name: eng_Latn-jao_Latn data_files: - path: train/eng_Latn-jao_Latn.jsonl.gz split: train - path: test/eng_Latn-jao_Latn.jsonl.gz split: test - path: validation/eng_Latn-jao_Latn.jsonl.gz split: validation - config_name: eng_Latn-kek_Latn data_files: - path: train/eng_Latn-kek_Latn.jsonl.gz split: train - path: test/eng_Latn-kek_Latn.jsonl.gz split: test - path: validation/eng_Latn-kek_Latn.jsonl.gz split: validation - config_name: eng_Latn-nfa_Latn data_files: - path: train/eng_Latn-nfa_Latn.jsonl.gz split: train - path: test/eng_Latn-nfa_Latn.jsonl.gz split: test - path: validation/eng_Latn-nfa_Latn.jsonl.gz split: validation - config_name: eng_Latn-lid_Latn data_files: - path: train/eng_Latn-lid_Latn.jsonl.gz split: train - path: test/eng_Latn-lid_Latn.jsonl.gz split: test - path: validation/eng_Latn-lid_Latn.jsonl.gz split: validation - config_name: eng_Latn-kmu_Latn data_files: - path: train/eng_Latn-kmu_Latn.jsonl.gz split: train - path: test/eng_Latn-kmu_Latn.jsonl.gz split: test - path: validation/eng_Latn-kmu_Latn.jsonl.gz split: validation - config_name: eng_Latn-hbo_Hebr data_files: - path: train/eng_Latn-hbo_Hebr.jsonl.gz split: train - path: test/eng_Latn-hbo_Hebr.jsonl.gz split: test - path: validation/eng_Latn-hbo_Hebr.jsonl.gz split: validation - config_name: eng_Latn-bkq_Latn data_files: - path: train/eng_Latn-bkq_Latn.jsonl.gz split: train - path: test/eng_Latn-bkq_Latn.jsonl.gz split: test - path: validation/eng_Latn-bkq_Latn.jsonl.gz split: validation - config_name: eng_Latn-mig_Latn data_files: - path: train/eng_Latn-mig_Latn.jsonl.gz split: train - path: test/eng_Latn-mig_Latn.jsonl.gz split: test - path: validation/eng_Latn-mig_Latn.jsonl.gz split: validation - config_name: eng_Latn-jae_Latn data_files: - path: train/eng_Latn-jae_Latn.jsonl.gz split: train - path: test/eng_Latn-jae_Latn.jsonl.gz split: test - path: validation/eng_Latn-jae_Latn.jsonl.gz split: validation - config_name: eng_Latn-ben_Beng data_files: - path: train/eng_Latn-ben_Beng.jsonl.gz split: train - path: test/eng_Latn-ben_Beng.jsonl.gz split: test - path: validation/eng_Latn-ben_Beng.jsonl.gz split: validation - config_name: eng_Latn-spy_Latn data_files: - path: train/eng_Latn-spy_Latn.jsonl.gz split: train - path: test/eng_Latn-spy_Latn.jsonl.gz split: test - path: validation/eng_Latn-spy_Latn.jsonl.gz split: validation - config_name: eng_Latn-bvd_Latn data_files: - path: train/eng_Latn-bvd_Latn.jsonl.gz split: train - path: test/eng_Latn-bvd_Latn.jsonl.gz split: test - path: validation/eng_Latn-bvd_Latn.jsonl.gz split: validation - config_name: eng_Latn-bvr_Latn data_files: - path: train/eng_Latn-bvr_Latn.jsonl.gz split: train - path: test/eng_Latn-bvr_Latn.jsonl.gz split: test - path: validation/eng_Latn-bvr_Latn.jsonl.gz split: validation - config_name: eng_Latn-kto_Latn data_files: - path: train/eng_Latn-kto_Latn.jsonl.gz split: train - path: test/eng_Latn-kto_Latn.jsonl.gz split: test - path: validation/eng_Latn-kto_Latn.jsonl.gz split: validation - config_name: eng_Latn-amn_Latn data_files: - path: train/eng_Latn-amn_Latn.jsonl.gz split: train - path: test/eng_Latn-amn_Latn.jsonl.gz split: test - path: validation/eng_Latn-amn_Latn.jsonl.gz split: validation - config_name: eng_Latn-spp_Latn data_files: - path: train/eng_Latn-spp_Latn.jsonl.gz split: train - path: test/eng_Latn-spp_Latn.jsonl.gz split: test - path: validation/eng_Latn-spp_Latn.jsonl.gz split: validation - config_name: eng_Latn-ncl_Latn data_files: - path: train/eng_Latn-ncl_Latn.jsonl.gz split: train - path: test/eng_Latn-ncl_Latn.jsonl.gz split: test - path: validation/eng_Latn-ncl_Latn.jsonl.gz split: validation - config_name: eng_Latn-tdt_Latn data_files: - path: train/eng_Latn-tdt_Latn.jsonl.gz split: train - path: test/eng_Latn-tdt_Latn.jsonl.gz split: test - path: validation/eng_Latn-tdt_Latn.jsonl.gz split: validation - config_name: eng_Latn-urw_Latn data_files: - path: train/eng_Latn-urw_Latn.jsonl.gz split: train - path: test/eng_Latn-urw_Latn.jsonl.gz split: test - path: validation/eng_Latn-urw_Latn.jsonl.gz split: validation - config_name: eng_Latn-shp_Latn data_files: - path: train/eng_Latn-shp_Latn.jsonl.gz split: train - path: test/eng_Latn-shp_Latn.jsonl.gz split: test - path: validation/eng_Latn-shp_Latn.jsonl.gz split: validation - config_name: eng_Latn-met_Latn data_files: - path: train/eng_Latn-met_Latn.jsonl.gz split: train - path: test/eng_Latn-met_Latn.jsonl.gz split: test - path: validation/eng_Latn-met_Latn.jsonl.gz split: validation - config_name: eng_Latn-pon_Latn data_files: - path: train/eng_Latn-pon_Latn.jsonl.gz split: train - path: test/eng_Latn-pon_Latn.jsonl.gz split: test - path: validation/eng_Latn-pon_Latn.jsonl.gz split: validation - config_name: eng_Latn-tiy_Latn data_files: - path: train/eng_Latn-tiy_Latn.jsonl.gz split: train - path: test/eng_Latn-tiy_Latn.jsonl.gz split: test - path: validation/eng_Latn-tiy_Latn.jsonl.gz split: validation - config_name: eng_Latn-nhy_Latn data_files: - path: train/eng_Latn-nhy_Latn.jsonl.gz split: train - path: test/eng_Latn-nhy_Latn.jsonl.gz split: test - path: validation/eng_Latn-nhy_Latn.jsonl.gz split: validation - config_name: eng_Latn-cpc_Latn data_files: - path: train/eng_Latn-cpc_Latn.jsonl.gz split: train - path: test/eng_Latn-cpc_Latn.jsonl.gz split: test - path: validation/eng_Latn-cpc_Latn.jsonl.gz split: validation - config_name: eng_Latn-bel_Cyrl data_files: - path: train/eng_Latn-bel_Cyrl.jsonl.gz split: train - path: test/eng_Latn-bel_Cyrl.jsonl.gz split: test - path: validation/eng_Latn-bel_Cyrl.jsonl.gz split: validation - config_name: eng_Latn-cbv_Latn data_files: - path: train/eng_Latn-cbv_Latn.jsonl.gz split: train - path: test/eng_Latn-cbv_Latn.jsonl.gz split: test - path: validation/eng_Latn-cbv_Latn.jsonl.gz split: validation - config_name: eng_Latn-pab_Latn data_files: - path: train/eng_Latn-pab_Latn.jsonl.gz split: train - path: test/eng_Latn-pab_Latn.jsonl.gz split: test - path: validation/eng_Latn-pab_Latn.jsonl.gz split: validation - config_name: eng_Latn-dgz_Latn data_files: - path: train/eng_Latn-dgz_Latn.jsonl.gz split: train - path: test/eng_Latn-dgz_Latn.jsonl.gz split: test - path: validation/eng_Latn-dgz_Latn.jsonl.gz split: validation - config_name: eng_Latn-pri_Latn data_files: - path: train/eng_Latn-pri_Latn.jsonl.gz split: train - path: test/eng_Latn-pri_Latn.jsonl.gz split: test - path: validation/eng_Latn-pri_Latn.jsonl.gz split: validation - config_name: eng_Latn-hch_Latn data_files: - path: train/eng_Latn-hch_Latn.jsonl.gz split: train - path: test/eng_Latn-hch_Latn.jsonl.gz split: test - path: validation/eng_Latn-hch_Latn.jsonl.gz split: validation - config_name: eng_Latn-wed_Latn data_files: - path: train/eng_Latn-wed_Latn.jsonl.gz split: train - path: test/eng_Latn-wed_Latn.jsonl.gz split: test - path: validation/eng_Latn-wed_Latn.jsonl.gz split: validation - config_name: eng_Latn-suz_Latn data_files: - path: train/eng_Latn-suz_Latn.jsonl.gz split: train - path: test/eng_Latn-suz_Latn.jsonl.gz split: test - path: validation/eng_Latn-suz_Latn.jsonl.gz split: validation - config_name: eng_Latn-usp_Latn data_files: - path: train/eng_Latn-usp_Latn.jsonl.gz split: train - path: test/eng_Latn-usp_Latn.jsonl.gz split: test - path: validation/eng_Latn-usp_Latn.jsonl.gz split: validation - config_name: eng_Latn-mkl_Latn data_files: - path: train/eng_Latn-mkl_Latn.jsonl.gz split: train - path: test/eng_Latn-mkl_Latn.jsonl.gz split: test - path: validation/eng_Latn-mkl_Latn.jsonl.gz split: validation - config_name: eng_Latn-cbt_Latn data_files: - path: train/eng_Latn-cbt_Latn.jsonl.gz split: train - path: test/eng_Latn-cbt_Latn.jsonl.gz split: test - path: validation/eng_Latn-cbt_Latn.jsonl.gz split: validation - config_name: eng_Latn-kpg_Latn data_files: - path: train/eng_Latn-kpg_Latn.jsonl.gz split: train - path: test/eng_Latn-kpg_Latn.jsonl.gz split: test - path: validation/eng_Latn-kpg_Latn.jsonl.gz split: validation - config_name: eng_Latn-kue_Latn data_files: - path: train/eng_Latn-kue_Latn.jsonl.gz split: train - path: test/eng_Latn-kue_Latn.jsonl.gz split: test - path: validation/eng_Latn-kue_Latn.jsonl.gz split: validation - config_name: eng_Latn-sbs_Latn data_files: - path: train/eng_Latn-sbs_Latn.jsonl.gz split: train - path: test/eng_Latn-sbs_Latn.jsonl.gz split: test - path: validation/eng_Latn-sbs_Latn.jsonl.gz split: validation - config_name: eng_Latn-qvz_Latn data_files: - path: train/eng_Latn-qvz_Latn.jsonl.gz split: train - path: test/eng_Latn-qvz_Latn.jsonl.gz split: test - path: validation/eng_Latn-qvz_Latn.jsonl.gz split: validation - config_name: eng_Latn-seh_Latn data_files: - path: train/eng_Latn-seh_Latn.jsonl.gz split: train - path: test/eng_Latn-seh_Latn.jsonl.gz split: test - path: validation/eng_Latn-seh_Latn.jsonl.gz split: validation - config_name: eng_Latn-wrs_Latn data_files: - path: train/eng_Latn-wrs_Latn.jsonl.gz split: train - path: test/eng_Latn-wrs_Latn.jsonl.gz split: test - path: validation/eng_Latn-wrs_Latn.jsonl.gz split: validation - config_name: eng_Latn-snc_Latn data_files: - path: train/eng_Latn-snc_Latn.jsonl.gz split: train - path: test/eng_Latn-snc_Latn.jsonl.gz split: test - path: validation/eng_Latn-snc_Latn.jsonl.gz split: validation - config_name: eng_Latn-bjp_Latn data_files: - path: train/eng_Latn-bjp_Latn.jsonl.gz split: train - path: test/eng_Latn-bjp_Latn.jsonl.gz split: test - path: validation/eng_Latn-bjp_Latn.jsonl.gz split: validation - config_name: eng_Latn-kyz_Latn data_files: - path: train/eng_Latn-kyz_Latn.jsonl.gz split: train - path: test/eng_Latn-kyz_Latn.jsonl.gz split: test - path: validation/eng_Latn-kyz_Latn.jsonl.gz split: validation - config_name: eng_Latn-noa_Latn data_files: - path: train/eng_Latn-noa_Latn.jsonl.gz split: train - path: test/eng_Latn-noa_Latn.jsonl.gz split: test - path: validation/eng_Latn-noa_Latn.jsonl.gz split: validation - config_name: eng_Latn-ssx_Latn data_files: - path: train/eng_Latn-ssx_Latn.jsonl.gz split: train - path: test/eng_Latn-ssx_Latn.jsonl.gz split: test - path: validation/eng_Latn-ssx_Latn.jsonl.gz split: validation - config_name: eng_Latn-nbq_Latn data_files: - path: train/eng_Latn-nbq_Latn.jsonl.gz split: train - path: test/eng_Latn-nbq_Latn.jsonl.gz split: test - path: validation/eng_Latn-nbq_Latn.jsonl.gz split: validation - config_name: eng_Latn-msb_Latn data_files: - path: train/eng_Latn-msb_Latn.jsonl.gz split: train - path: test/eng_Latn-msb_Latn.jsonl.gz split: test - path: validation/eng_Latn-msb_Latn.jsonl.gz split: validation - config_name: eng_Latn-sue_Latn data_files: - path: train/eng_Latn-sue_Latn.jsonl.gz split: train - path: test/eng_Latn-sue_Latn.jsonl.gz split: test - path: validation/eng_Latn-sue_Latn.jsonl.gz split: validation - config_name: eng_Latn-asm_Beng data_files: - path: train/eng_Latn-asm_Beng.jsonl.gz split: train - path: test/eng_Latn-asm_Beng.jsonl.gz split: test - path: validation/eng_Latn-asm_Beng.jsonl.gz split: validation - config_name: eng_Latn-som_Latn data_files: - path: train/eng_Latn-som_Latn.jsonl.gz split: train - path: test/eng_Latn-som_Latn.jsonl.gz split: test - path: validation/eng_Latn-som_Latn.jsonl.gz split: validation - config_name: eng_Latn-xon_Latn data_files: - path: train/eng_Latn-xon_Latn.jsonl.gz split: train - path: test/eng_Latn-xon_Latn.jsonl.gz split: test - path: validation/eng_Latn-xon_Latn.jsonl.gz split: validation - config_name: eng_Latn-qvh_Latn data_files: - path: train/eng_Latn-qvh_Latn.jsonl.gz split: train - path: test/eng_Latn-qvh_Latn.jsonl.gz split: test - path: validation/eng_Latn-qvh_Latn.jsonl.gz split: validation - config_name: eng_Latn-mib_Latn data_files: - path: train/eng_Latn-mib_Latn.jsonl.gz split: train - path: test/eng_Latn-mib_Latn.jsonl.gz split: test - path: validation/eng_Latn-mib_Latn.jsonl.gz split: validation - config_name: eng_Latn-wap_Latn data_files: - path: train/eng_Latn-wap_Latn.jsonl.gz split: train - path: test/eng_Latn-wap_Latn.jsonl.gz split: test - path: validation/eng_Latn-wap_Latn.jsonl.gz split: validation - config_name: eng_Latn-gai_Latn data_files: - path: train/eng_Latn-gai_Latn.jsonl.gz split: train - path: test/eng_Latn-gai_Latn.jsonl.gz split: test - path: validation/eng_Latn-gai_Latn.jsonl.gz split: validation - config_name: eng_Latn-mkn_Latn data_files: - path: train/eng_Latn-mkn_Latn.jsonl.gz split: train - path: test/eng_Latn-mkn_Latn.jsonl.gz split: test - path: validation/eng_Latn-mkn_Latn.jsonl.gz split: validation - config_name: eng_Latn-xnn_Latn data_files: - path: train/eng_Latn-xnn_Latn.jsonl.gz split: train - path: test/eng_Latn-xnn_Latn.jsonl.gz split: test - path: validation/eng_Latn-xnn_Latn.jsonl.gz split: validation - config_name: eng_Latn-amf_Latn data_files: - path: train/eng_Latn-amf_Latn.jsonl.gz split: train - path: test/eng_Latn-amf_Latn.jsonl.gz split: test - path: validation/eng_Latn-amf_Latn.jsonl.gz split: validation - config_name: eng_Latn-nhe_Latn data_files: - path: train/eng_Latn-nhe_Latn.jsonl.gz split: train - path: test/eng_Latn-nhe_Latn.jsonl.gz split: test - path: validation/eng_Latn-nhe_Latn.jsonl.gz split: validation - config_name: eng_Latn-kmk_Latn data_files: - path: train/eng_Latn-kmk_Latn.jsonl.gz split: train - path: test/eng_Latn-kmk_Latn.jsonl.gz split: test - path: validation/eng_Latn-kmk_Latn.jsonl.gz split: validation - config_name: eng_Latn-tam_Taml data_files: - path: train/eng_Latn-tam_Taml.jsonl.gz split: train - path: test/eng_Latn-tam_Taml.jsonl.gz split: test - path: validation/eng_Latn-tam_Taml.jsonl.gz split: validation - config_name: eng_Latn-nab_Latn data_files: - path: train/eng_Latn-nab_Latn.jsonl.gz split: train - path: test/eng_Latn-nab_Latn.jsonl.gz split: test - path: validation/eng_Latn-nab_Latn.jsonl.gz split: validation - config_name: eng_Latn-dik_Latn data_files: - path: train/eng_Latn-dik_Latn.jsonl.gz split: train - path: test/eng_Latn-dik_Latn.jsonl.gz split: test - path: validation/eng_Latn-dik_Latn.jsonl.gz split: validation - config_name: eng_Latn-cpy_Latn data_files: - path: train/eng_Latn-cpy_Latn.jsonl.gz split: train - path: test/eng_Latn-cpy_Latn.jsonl.gz split: test - path: validation/eng_Latn-cpy_Latn.jsonl.gz split: validation - config_name: eng_Latn-arl_Latn data_files: - path: train/eng_Latn-arl_Latn.jsonl.gz split: train - path: test/eng_Latn-arl_Latn.jsonl.gz split: test - path: validation/eng_Latn-arl_Latn.jsonl.gz split: validation - config_name: eng_Latn-tuc_Latn data_files: - path: train/eng_Latn-tuc_Latn.jsonl.gz split: train - path: test/eng_Latn-tuc_Latn.jsonl.gz split: test - path: validation/eng_Latn-tuc_Latn.jsonl.gz split: validation - config_name: eng_Latn-ngu_Latn data_files: - path: train/eng_Latn-ngu_Latn.jsonl.gz split: train - path: test/eng_Latn-ngu_Latn.jsonl.gz split: test - path: validation/eng_Latn-ngu_Latn.jsonl.gz split: validation - config_name: eng_Latn-zad_Latn data_files: - path: train/eng_Latn-zad_Latn.jsonl.gz split: train - path: test/eng_Latn-zad_Latn.jsonl.gz split: test - path: validation/eng_Latn-zad_Latn.jsonl.gz split: validation - config_name: eng_Latn-kmh_Latn data_files: - path: train/eng_Latn-kmh_Latn.jsonl.gz split: train - path: test/eng_Latn-kmh_Latn.jsonl.gz split: test - path: validation/eng_Latn-kmh_Latn.jsonl.gz split: validation - config_name: eng_Latn-quh_Latn data_files: - path: train/eng_Latn-quh_Latn.jsonl.gz split: train - path: test/eng_Latn-quh_Latn.jsonl.gz split: test - path: validation/eng_Latn-quh_Latn.jsonl.gz split: validation - config_name: eng_Latn-alq_Latn data_files: - path: train/eng_Latn-alq_Latn.jsonl.gz split: train - path: test/eng_Latn-alq_Latn.jsonl.gz split: test - path: validation/eng_Latn-alq_Latn.jsonl.gz split: validation - config_name: eng_Latn-grc_Grek data_files: - path: train/eng_Latn-grc_Grek.jsonl.gz split: train - path: test/eng_Latn-grc_Grek.jsonl.gz split: test - path: validation/eng_Latn-grc_Grek.jsonl.gz split: validation - config_name: eng_Latn-kaq_Latn data_files: - path: train/eng_Latn-kaq_Latn.jsonl.gz split: train - path: test/eng_Latn-kaq_Latn.jsonl.gz split: test - path: validation/eng_Latn-kaq_Latn.jsonl.gz split: validation - config_name: eng_Latn-zpm_Latn data_files: - path: train/eng_Latn-zpm_Latn.jsonl.gz split: train - path: test/eng_Latn-zpm_Latn.jsonl.gz split: test - path: validation/eng_Latn-zpm_Latn.jsonl.gz split: validation - config_name: eng_Latn-obo_Latn data_files: - path: train/eng_Latn-obo_Latn.jsonl.gz split: train - path: test/eng_Latn-obo_Latn.jsonl.gz split: test - path: validation/eng_Latn-obo_Latn.jsonl.gz split: validation - config_name: eng_Latn-cha_Latn data_files: - path: train/eng_Latn-cha_Latn.jsonl.gz split: train - path: test/eng_Latn-cha_Latn.jsonl.gz split: test - path: validation/eng_Latn-cha_Latn.jsonl.gz split: validation - config_name: eng_Latn-mbs_Latn data_files: - path: train/eng_Latn-mbs_Latn.jsonl.gz split: train - path: test/eng_Latn-mbs_Latn.jsonl.gz split: test - path: validation/eng_Latn-mbs_Latn.jsonl.gz split: validation - config_name: eng_Latn-poi_Latn data_files: - path: train/eng_Latn-poi_Latn.jsonl.gz split: train - path: test/eng_Latn-poi_Latn.jsonl.gz split: test - path: validation/eng_Latn-poi_Latn.jsonl.gz split: validation - config_name: eng_Latn-spm_Latn data_files: - path: train/eng_Latn-spm_Latn.jsonl.gz split: train - path: test/eng_Latn-spm_Latn.jsonl.gz split: test - path: validation/eng_Latn-spm_Latn.jsonl.gz split: validation - config_name: eng_Latn-cpb_Latn data_files: - path: train/eng_Latn-cpb_Latn.jsonl.gz split: train - path: test/eng_Latn-cpb_Latn.jsonl.gz split: test - path: validation/eng_Latn-cpb_Latn.jsonl.gz split: validation - config_name: eng_Latn-omw_Latn data_files: - path: train/eng_Latn-omw_Latn.jsonl.gz split: train - path: test/eng_Latn-omw_Latn.jsonl.gz split: test - path: validation/eng_Latn-omw_Latn.jsonl.gz split: validation - config_name: eng_Latn-klv_Latn data_files: - path: train/eng_Latn-klv_Latn.jsonl.gz split: train - path: test/eng_Latn-klv_Latn.jsonl.gz split: test - path: validation/eng_Latn-klv_Latn.jsonl.gz split: validation - config_name: eng_Latn-sbe_Latn data_files: - path: train/eng_Latn-sbe_Latn.jsonl.gz split: train - path: test/eng_Latn-sbe_Latn.jsonl.gz split: test - path: validation/eng_Latn-sbe_Latn.jsonl.gz split: validation - config_name: eng_Latn-ntu_Latn data_files: - path: train/eng_Latn-ntu_Latn.jsonl.gz split: train - path: test/eng_Latn-ntu_Latn.jsonl.gz split: test - path: validation/eng_Latn-ntu_Latn.jsonl.gz split: validation - config_name: eng_Latn-zat_Latn data_files: - path: train/eng_Latn-zat_Latn.jsonl.gz split: train - path: test/eng_Latn-zat_Latn.jsonl.gz split: test - path: validation/eng_Latn-zat_Latn.jsonl.gz split: validation - config_name: eng_Latn-bsp_Latn data_files: - path: train/eng_Latn-bsp_Latn.jsonl.gz split: train - path: test/eng_Latn-bsp_Latn.jsonl.gz split: test - path: validation/eng_Latn-bsp_Latn.jsonl.gz split: validation - config_name: eng_Latn-mps_Latn data_files: - path: train/eng_Latn-mps_Latn.jsonl.gz split: train - path: test/eng_Latn-mps_Latn.jsonl.gz split: test - path: validation/eng_Latn-mps_Latn.jsonl.gz split: validation - config_name: eng_Latn-mks_Latn data_files: - path: train/eng_Latn-mks_Latn.jsonl.gz split: train - path: test/eng_Latn-mks_Latn.jsonl.gz split: test - path: validation/eng_Latn-mks_Latn.jsonl.gz split: validation - config_name: eng_Latn-bmh_Latn data_files: - path: train/eng_Latn-bmh_Latn.jsonl.gz split: train - path: test/eng_Latn-bmh_Latn.jsonl.gz split: test - path: validation/eng_Latn-bmh_Latn.jsonl.gz split: validation - config_name: eng_Latn-tpz_Latn data_files: - path: train/eng_Latn-tpz_Latn.jsonl.gz split: train - path: test/eng_Latn-tpz_Latn.jsonl.gz split: test - path: validation/eng_Latn-tpz_Latn.jsonl.gz split: validation - config_name: eng_Latn-amr_Latn data_files: - path: train/eng_Latn-amr_Latn.jsonl.gz split: train - path: test/eng_Latn-amr_Latn.jsonl.gz split: test - path: validation/eng_Latn-amr_Latn.jsonl.gz split: validation - config_name: eng_Latn-cjo_Latn data_files: - path: train/eng_Latn-cjo_Latn.jsonl.gz split: train - path: test/eng_Latn-cjo_Latn.jsonl.gz split: test - path: validation/eng_Latn-cjo_Latn.jsonl.gz split: validation - config_name: eng_Latn-ksr_Latn data_files: - path: train/eng_Latn-ksr_Latn.jsonl.gz split: train - path: test/eng_Latn-ksr_Latn.jsonl.gz split: test - path: validation/eng_Latn-ksr_Latn.jsonl.gz split: validation - config_name: eng_Latn-tgo_Latn data_files: - path: train/eng_Latn-tgo_Latn.jsonl.gz split: train - path: test/eng_Latn-tgo_Latn.jsonl.gz split: test - path: validation/eng_Latn-tgo_Latn.jsonl.gz split: validation - config_name: eng_Latn-tke_Latn data_files: - path: train/eng_Latn-tke_Latn.jsonl.gz split: train - path: test/eng_Latn-tke_Latn.jsonl.gz split: test - path: validation/eng_Latn-tke_Latn.jsonl.gz split: validation - config_name: eng_Latn-lac_Latn data_files: - path: train/eng_Latn-lac_Latn.jsonl.gz split: train - path: test/eng_Latn-lac_Latn.jsonl.gz split: test - path: validation/eng_Latn-lac_Latn.jsonl.gz split: validation - config_name: eng_Latn-nhu_Latn data_files: - path: train/eng_Latn-nhu_Latn.jsonl.gz split: train - path: test/eng_Latn-nhu_Latn.jsonl.gz split: test - path: validation/eng_Latn-nhu_Latn.jsonl.gz split: validation - config_name: eng_Latn-ssd_Latn data_files: - path: train/eng_Latn-ssd_Latn.jsonl.gz split: train - path: test/eng_Latn-ssd_Latn.jsonl.gz split: test - path: validation/eng_Latn-ssd_Latn.jsonl.gz split: validation - config_name: eng_Latn-bon_Latn data_files: - path: train/eng_Latn-bon_Latn.jsonl.gz split: train - path: test/eng_Latn-bon_Latn.jsonl.gz split: test - path: validation/eng_Latn-bon_Latn.jsonl.gz split: validation - config_name: eng_Latn-cso_Latn data_files: - path: train/eng_Latn-cso_Latn.jsonl.gz split: train - path: test/eng_Latn-cso_Latn.jsonl.gz split: test - path: validation/eng_Latn-cso_Latn.jsonl.gz split: validation - config_name: eng_Latn-naf_Latn data_files: - path: train/eng_Latn-naf_Latn.jsonl.gz split: train - path: test/eng_Latn-naf_Latn.jsonl.gz split: test - path: validation/eng_Latn-naf_Latn.jsonl.gz split: validation - config_name: eng_Latn-kbh_Latn data_files: - path: train/eng_Latn-kbh_Latn.jsonl.gz split: train - path: test/eng_Latn-kbh_Latn.jsonl.gz split: test - path: validation/eng_Latn-kbh_Latn.jsonl.gz split: validation - config_name: eng_Latn-hun_Latn data_files: - path: train/eng_Latn-hun_Latn.jsonl.gz split: train - path: test/eng_Latn-hun_Latn.jsonl.gz split: test - path: validation/eng_Latn-hun_Latn.jsonl.gz split: validation - config_name: eng_Latn-tte_Latn data_files: - path: train/eng_Latn-tte_Latn.jsonl.gz split: train - path: test/eng_Latn-tte_Latn.jsonl.gz split: test - path: validation/eng_Latn-tte_Latn.jsonl.gz split: validation - config_name: eng_Latn-amo_Latn data_files: - path: train/eng_Latn-amo_Latn.jsonl.gz split: train - path: test/eng_Latn-amo_Latn.jsonl.gz split: test - path: validation/eng_Latn-amo_Latn.jsonl.gz split: validation - config_name: eng_Latn-kiz_Latn data_files: - path: train/eng_Latn-kiz_Latn.jsonl.gz split: train - path: test/eng_Latn-kiz_Latn.jsonl.gz split: test - path: validation/eng_Latn-kiz_Latn.jsonl.gz split: validation - config_name: eng_Latn-wsk_Latn data_files: - path: train/eng_Latn-wsk_Latn.jsonl.gz split: train - path: test/eng_Latn-wsk_Latn.jsonl.gz split: test - path: validation/eng_Latn-wsk_Latn.jsonl.gz split: validation - config_name: eng_Latn-kwd_Latn data_files: - path: train/eng_Latn-kwd_Latn.jsonl.gz split: train - path: test/eng_Latn-kwd_Latn.jsonl.gz split: test - path: validation/eng_Latn-kwd_Latn.jsonl.gz split: validation - config_name: eng_Latn-geb_Latn data_files: - path: train/eng_Latn-geb_Latn.jsonl.gz split: train - path: test/eng_Latn-geb_Latn.jsonl.gz split: test - path: validation/eng_Latn-geb_Latn.jsonl.gz split: validation - config_name: eng_Latn-mdy_Latn data_files: - path: train/eng_Latn-mdy_Latn.jsonl.gz split: train - path: test/eng_Latn-mdy_Latn.jsonl.gz split: test - path: validation/eng_Latn-mdy_Latn.jsonl.gz split: validation - config_name: eng_Latn-kgk_Latn data_files: - path: train/eng_Latn-kgk_Latn.jsonl.gz split: train - path: test/eng_Latn-kgk_Latn.jsonl.gz split: test - path: validation/eng_Latn-kgk_Latn.jsonl.gz split: validation - config_name: eng_Latn-kqw_Latn data_files: - path: train/eng_Latn-kqw_Latn.jsonl.gz split: train - path: test/eng_Latn-kqw_Latn.jsonl.gz split: test - path: validation/eng_Latn-kqw_Latn.jsonl.gz split: validation - config_name: eng_Latn-gaw_Latn data_files: - path: train/eng_Latn-gaw_Latn.jsonl.gz split: train - path: test/eng_Latn-gaw_Latn.jsonl.gz split: test - path: validation/eng_Latn-gaw_Latn.jsonl.gz split: validation - config_name: eng_Latn-mox_Latn data_files: - path: train/eng_Latn-mox_Latn.jsonl.gz split: train - path: test/eng_Latn-mox_Latn.jsonl.gz split: test - path: validation/eng_Latn-mox_Latn.jsonl.gz split: validation - config_name: eng_Latn-cav_Latn data_files: - path: train/eng_Latn-cav_Latn.jsonl.gz split: train - path: test/eng_Latn-cav_Latn.jsonl.gz split: test - path: validation/eng_Latn-cav_Latn.jsonl.gz split: validation - config_name: eng_Latn-tzj_Latn data_files: - path: train/eng_Latn-tzj_Latn.jsonl.gz split: train - path: test/eng_Latn-tzj_Latn.jsonl.gz split: test - path: validation/eng_Latn-tzj_Latn.jsonl.gz split: validation - config_name: eng_Latn-kze_Latn data_files: - path: train/eng_Latn-kze_Latn.jsonl.gz split: train - path: test/eng_Latn-kze_Latn.jsonl.gz split: test - path: validation/eng_Latn-kze_Latn.jsonl.gz split: validation - config_name: eng_Latn-not_Latn data_files: - path: train/eng_Latn-not_Latn.jsonl.gz split: train - path: test/eng_Latn-not_Latn.jsonl.gz split: test - path: validation/eng_Latn-not_Latn.jsonl.gz split: validation - config_name: eng_Latn-lin_Latn data_files: - path: train/eng_Latn-lin_Latn.jsonl.gz split: train - path: test/eng_Latn-lin_Latn.jsonl.gz split: test - path: validation/eng_Latn-lin_Latn.jsonl.gz split: validation - config_name: eng_Latn-urd_Arab data_files: - path: train/eng_Latn-urd_Arab.jsonl.gz split: train - path: test/eng_Latn-urd_Arab.jsonl.gz split: test - path: validation/eng_Latn-urd_Arab.jsonl.gz split: validation - config_name: eng_Latn-mlh_Latn data_files: - path: train/eng_Latn-mlh_Latn.jsonl.gz split: train - path: test/eng_Latn-mlh_Latn.jsonl.gz split: test - path: validation/eng_Latn-mlh_Latn.jsonl.gz split: validation - config_name: eng_Latn-muy_Latn data_files: - path: train/eng_Latn-muy_Latn.jsonl.gz split: train - path: test/eng_Latn-muy_Latn.jsonl.gz split: test - path: validation/eng_Latn-muy_Latn.jsonl.gz split: validation - config_name: eng_Latn-iws_Latn data_files: - path: train/eng_Latn-iws_Latn.jsonl.gz split: train - path: test/eng_Latn-iws_Latn.jsonl.gz split: test - path: validation/eng_Latn-iws_Latn.jsonl.gz split: validation - config_name: eng_Latn-tur_Latn data_files: - path: train/eng_Latn-tur_Latn.jsonl.gz split: train - path: test/eng_Latn-tur_Latn.jsonl.gz split: test - path: validation/eng_Latn-tur_Latn.jsonl.gz split: validation - config_name: eng_Latn-gam_Latn data_files: - path: train/eng_Latn-gam_Latn.jsonl.gz split: train - path: test/eng_Latn-gam_Latn.jsonl.gz split: test - path: validation/eng_Latn-gam_Latn.jsonl.gz split: validation - config_name: eng_Latn-kbc_Latn data_files: - path: train/eng_Latn-kbc_Latn.jsonl.gz split: train - path: test/eng_Latn-kbc_Latn.jsonl.gz split: test - path: validation/eng_Latn-kbc_Latn.jsonl.gz split: validation - config_name: eng_Latn-kgf_Latn data_files: - path: train/eng_Latn-kgf_Latn.jsonl.gz split: train - path: test/eng_Latn-kgf_Latn.jsonl.gz split: test - path: validation/eng_Latn-kgf_Latn.jsonl.gz split: validation - config_name: eng_Latn-maz_Latn data_files: - path: train/eng_Latn-maz_Latn.jsonl.gz split: train - path: test/eng_Latn-maz_Latn.jsonl.gz split: test - path: validation/eng_Latn-maz_Latn.jsonl.gz split: validation - config_name: eng_Latn-nss_Latn data_files: - path: train/eng_Latn-nss_Latn.jsonl.gz split: train - path: test/eng_Latn-nss_Latn.jsonl.gz split: test - path: validation/eng_Latn-nss_Latn.jsonl.gz split: validation - config_name: eng_Latn-ake_Latn data_files: - path: train/eng_Latn-ake_Latn.jsonl.gz split: train - path: test/eng_Latn-ake_Latn.jsonl.gz split: test - path: validation/eng_Latn-ake_Latn.jsonl.gz split: validation - config_name: eng_Latn-nuy_Latn data_files: - path: train/eng_Latn-nuy_Latn.jsonl.gz split: train - path: test/eng_Latn-nuy_Latn.jsonl.gz split: test - path: validation/eng_Latn-nuy_Latn.jsonl.gz split: validation - config_name: eng_Latn-bjk_Latn data_files: - path: train/eng_Latn-bjk_Latn.jsonl.gz split: train - path: test/eng_Latn-bjk_Latn.jsonl.gz split: test - path: validation/eng_Latn-bjk_Latn.jsonl.gz split: validation - config_name: eng_Latn-mzz_Latn data_files: - path: train/eng_Latn-mzz_Latn.jsonl.gz split: train - path: test/eng_Latn-mzz_Latn.jsonl.gz split: test - path: validation/eng_Latn-mzz_Latn.jsonl.gz split: validation - config_name: eng_Latn-msy_Latn data_files: - path: train/eng_Latn-msy_Latn.jsonl.gz split: train - path: test/eng_Latn-msy_Latn.jsonl.gz split: test - path: validation/eng_Latn-msy_Latn.jsonl.gz split: validation - config_name: eng_Latn-anh_Latn data_files: - path: train/eng_Latn-anh_Latn.jsonl.gz split: train - path: test/eng_Latn-anh_Latn.jsonl.gz split: test - path: validation/eng_Latn-anh_Latn.jsonl.gz split: validation - config_name: eng_Latn-bea_Latn data_files: - path: train/eng_Latn-bea_Latn.jsonl.gz split: train - path: test/eng_Latn-bea_Latn.jsonl.gz split: test - path: validation/eng_Latn-bea_Latn.jsonl.gz split: validation - config_name: eng_Latn-ntj_Latn data_files: - path: train/eng_Latn-ntj_Latn.jsonl.gz split: train - path: test/eng_Latn-ntj_Latn.jsonl.gz split: test - path: validation/eng_Latn-ntj_Latn.jsonl.gz split: validation - config_name: eng_Latn-anv_Latn data_files: - path: train/eng_Latn-anv_Latn.jsonl.gz split: train - path: test/eng_Latn-anv_Latn.jsonl.gz split: test - path: validation/eng_Latn-anv_Latn.jsonl.gz split: validation - config_name: eng_Latn-xed_Latn data_files: - path: train/eng_Latn-xed_Latn.jsonl.gz split: train - path: test/eng_Latn-xed_Latn.jsonl.gz split: test - path: validation/eng_Latn-xed_Latn.jsonl.gz split: validation - config_name: eng_Latn-nho_Latn data_files: - path: train/eng_Latn-nho_Latn.jsonl.gz split: train - path: test/eng_Latn-nho_Latn.jsonl.gz split: test - path: validation/eng_Latn-nho_Latn.jsonl.gz split: validation - config_name: eng_Latn-cbc_Latn data_files: - path: train/eng_Latn-cbc_Latn.jsonl.gz split: train - path: test/eng_Latn-cbc_Latn.jsonl.gz split: test - path: validation/eng_Latn-cbc_Latn.jsonl.gz split: validation - config_name: eng_Latn-qve_Latn data_files: - path: train/eng_Latn-qve_Latn.jsonl.gz split: train - path: test/eng_Latn-qve_Latn.jsonl.gz split: test - path: validation/eng_Latn-qve_Latn.jsonl.gz split: validation - config_name: eng_Latn-amp_Latn data_files: - path: train/eng_Latn-amp_Latn.jsonl.gz split: train - path: test/eng_Latn-amp_Latn.jsonl.gz split: test - path: validation/eng_Latn-amp_Latn.jsonl.gz split: validation - config_name: eng_Latn-qvc_Latn data_files: - path: train/eng_Latn-qvc_Latn.jsonl.gz split: train - path: test/eng_Latn-qvc_Latn.jsonl.gz split: test - path: validation/eng_Latn-qvc_Latn.jsonl.gz split: validation - config_name: eng_Latn-aka_Latn data_files: - path: train/eng_Latn-aka_Latn.jsonl.gz split: train - path: test/eng_Latn-aka_Latn.jsonl.gz split: test - path: validation/eng_Latn-aka_Latn.jsonl.gz split: validation - config_name: eng_Latn-aby_Latn data_files: - path: train/eng_Latn-aby_Latn.jsonl.gz split: train - path: test/eng_Latn-aby_Latn.jsonl.gz split: test - path: validation/eng_Latn-aby_Latn.jsonl.gz split: validation - config_name: eng_Latn-myu_Latn data_files: - path: train/eng_Latn-myu_Latn.jsonl.gz split: train - path: test/eng_Latn-myu_Latn.jsonl.gz split: test - path: validation/eng_Latn-myu_Latn.jsonl.gz split: validation - config_name: eng_Latn-aak_Arab data_files: - path: train/eng_Latn-aak_Arab.jsonl.gz split: train - path: test/eng_Latn-aak_Arab.jsonl.gz split: test - path: validation/eng_Latn-aak_Arab.jsonl.gz split: validation - config_name: eng_Latn-soq_Latn data_files: - path: train/eng_Latn-soq_Latn.jsonl.gz split: train - path: test/eng_Latn-soq_Latn.jsonl.gz split: test - path: validation/eng_Latn-soq_Latn.jsonl.gz split: validation - config_name: eng_Latn-tif_Latn data_files: - path: train/eng_Latn-tif_Latn.jsonl.gz split: train - path: test/eng_Latn-tif_Latn.jsonl.gz split: test - path: validation/eng_Latn-tif_Latn.jsonl.gz split: validation - config_name: eng_Latn-aai_Latn data_files: - path: train/eng_Latn-aai_Latn.jsonl.gz split: train - path: test/eng_Latn-aai_Latn.jsonl.gz split: test - path: validation/eng_Latn-aai_Latn.jsonl.gz split: validation - config_name: eng_Latn-nnq_Latn data_files: - path: train/eng_Latn-nnq_Latn.jsonl.gz split: train - path: test/eng_Latn-nnq_Latn.jsonl.gz split: test - path: validation/eng_Latn-nnq_Latn.jsonl.gz split: validation - config_name: eng_Latn-sab_Latn data_files: - path: train/eng_Latn-sab_Latn.jsonl.gz split: train - path: test/eng_Latn-sab_Latn.jsonl.gz split: test - path: validation/eng_Latn-sab_Latn.jsonl.gz split: validation - config_name: eng_Latn-wmw_Latn data_files: - path: train/eng_Latn-wmw_Latn.jsonl.gz split: train - path: test/eng_Latn-wmw_Latn.jsonl.gz split: test - path: validation/eng_Latn-wmw_Latn.jsonl.gz split: validation - config_name: eng_Latn-dgc_Latn data_files: - path: train/eng_Latn-dgc_Latn.jsonl.gz split: train - path: test/eng_Latn-dgc_Latn.jsonl.gz split: test - path: validation/eng_Latn-dgc_Latn.jsonl.gz split: validation - config_name: eng_Latn-roo_Latn data_files: - path: train/eng_Latn-roo_Latn.jsonl.gz split: train - path: test/eng_Latn-roo_Latn.jsonl.gz split: test - path: validation/eng_Latn-roo_Latn.jsonl.gz split: validation - config_name: eng_Latn-tcs_Latn data_files: - path: train/eng_Latn-tcs_Latn.jsonl.gz split: train - path: test/eng_Latn-tcs_Latn.jsonl.gz split: test - path: validation/eng_Latn-tcs_Latn.jsonl.gz split: validation - config_name: eng_Latn-mxb_Latn data_files: - path: train/eng_Latn-mxb_Latn.jsonl.gz split: train - path: test/eng_Latn-mxb_Latn.jsonl.gz split: test - path: validation/eng_Latn-mxb_Latn.jsonl.gz split: validation - config_name: eng_Latn-kde_Latn data_files: - path: train/eng_Latn-kde_Latn.jsonl.gz split: train - path: test/eng_Latn-kde_Latn.jsonl.gz split: test - path: validation/eng_Latn-kde_Latn.jsonl.gz split: validation --- This dataset pre-computes all English-centric directions from [bible-nlp/biblenlp-corpus](https://huggingface.co/datasets/bible-nlp/biblenlp-corpus), and as a result loading is significantly faster. Loading example: ```python >>> from datasets import load_dataset >>> dataset = load_dataset("davidstap/biblenlp-corpus-mmteb", "eng-arb", trust_remote_code=True) >>> dataset DatasetDict({ train: Dataset({ features: ['eng', 'arb'], num_rows: 28723 }) validation: Dataset({ features: ['eng', 'arb'], num_rows: 1578 }) test: Dataset({ features: ['eng', 'arb'], num_rows: 1551 }) }) >>> ``` Note that in all possible configurations, `eng` comes before the other language.

KShivendu/dbpedia-entities-openai-1M

KShivendu

--- license: mit dataset_info: features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string - name: openai sequence: float32 splits: - name: train num_bytes: 12383152 num_examples: 1000000 download_size: 12383152 dataset_size: 1000000 language: - en task_categories: - feature-extraction pretty_name: OpenAI 1M with DBPedia Entities size_categories: - 1M<n<10M --- 1M OpenAI Embeddings -- 1536 dimensions Created: June 2023. Text used for Embedding: title (string) + text (string) Embedding Model: text-embedding-ada-002 First used for the pgvector vs VectorDB (Qdrant) benchmark: https://nirantk.com/writing/pgvector-vs-qdrant/ ### Future work We are planning to take this up to 10M (and possibly 100M) vectors. Contact [@KShivendu_](https://twitter.com/KShivendu_) on Twitter or mail to [email protected] if you want to help :) ### Credits: This dataset was generated from the first 1M entries of https://huggingface.co/datasets/BeIR/dbpedia-entity

allenai/tulu-3-sft-mixture

allenai

--- annotations_creators: - crowdsourced - expert-generated - machine-generated language: - amh - arb - ary - ars - acq - arz - apc - ben - ceb - dan - deu - ell - eng - eus - fil - fin - fra - gle - guj - hat - hau - hin - hun - ibo - ind - ita - jav - jpn - kan - kir - kor - kur - lit - mal - mar - mlg - msa - mya - nep - nld - nso - nya - pan - pes - pol - por - pus - rus - sin - sna - snd - som - spa - sqi - srp - sun - swa - swe - tam - tel - tha - tur - ukr - urd - vie - wol - xho - yor - zho - zul license: odc-by multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - allenai/coconot - ai2-adapt-dev/flan_v2_converted - HuggingFaceH4/no_robots - OpenAssistant/oasst1 - allenai/tulu-3-personas-math - allenai/tulu-3-sft-personas-math-grade - allenai/tulu-3-sft-personas-code - allenai/tulu-3-personas-algebra - allenai/tulu-3-sft-personas-instruction-following - AI-MO/NuminaMath-TIR - allenai/wildguardmix - allenai/wildjailbreak - allenai/tulu-3-hard-coded - CohereForAI/aya_dataset - allenai/WildChat-1M - LipengCS/Table-GPT - allenai/SciRIFF - theblackcat102/evol-codealpaca-v1 task_categories: - other dataset_info: features: - name: id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: source dtype: string splits: - name: train num_bytes: 2914250826.5647593 num_examples: 939343 download_size: 1412954868 dataset_size: 2914250826.5647593 configs: - config_name: default data_files: - split: train path: data/train-* --- <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-3/Tulu3-logo.png" alt="Tulu3 banner" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Tulu 3 SFT Mixture *Note that this collection is licensed under ODC-BY-1.0 license; different licenses apply to subsets of the data. Some portions of the dataset are non-commercial. We present the mixture as a research artifact.* The Tulu 3 SFT mixture was used to train the [Tulu 3 series of models](https://huggingface.co/collections/allenai/tulu-3-models-673b8e0dc3512e30e7dc54f5). It contains 939,344 samples from the following sets: - [CoCoNot](https://huggingface.co/datasets/allenai/coconot) (ODC-BY-1.0), 10,983 prompts (Brahman et al., 2024) - [FLAN v2](https://github.com/google-research/FLAN/tree/main) via [`ai2-adapt-dev/flan_v2_converted`](https://huggingface.co/datasets/ai2-adapt-dev/flan_v2_converted), 89,982 prompts (Longpre et al., 2023) - [No Robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) (CC-BY-NC-4.0), 9,500 prompts (Rajani et al. 2023) - [OpenAssistant Guanaco](https://huggingface.co/datasets/OpenAssistant/oasst1) (Apache 2.0), 7,132 prompts (Kopf et al., 2024) - [Tulu 3 Persona MATH](https://huggingface.co/datasets/allenai/tulu-3-personas-math) (ODC-BY-1.0), 149,960 prompts - [Tulu 3 Persona GSM](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-math-grade) (ODC-BY-1.0), 49,980 prompts - [Tulu 3 Persona Python](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-code) (ODC-BY-1.0), 34,999 prompts - [Tulu 3 Persona Algebra](https://huggingface.co/datasets/allenai/tulu-3-personas-algebra) (ODC-BY-1.0), 20,000 prompts - [Tulu 3 Persona IF](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following) (ODC-BY-1.0), 29,980 prompts - [NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) (Apache 2.0), 64,312 prompts (Beeching et al. 2024) - [Tulu 3 WildGuardMix](https://huggingface.co/datasets/allenai/wildguardmix) (Apache 2.0), 50,000 prompts (Han et al., 2024) - [Tulu 3 WildJailbreak](https://huggingface.co/datasets/allenai/wildjailbreak) (ODC-BY-1.0), 50,000 prompts (Wildteaming, 2024) - [Tulu 3 Hardcoded](https://huggingface.co/datasets/allenai/tulu-3-hard-coded) (CC-BY-4.0), 240 prompts - [Aya](https://huggingface.co/datasets/CohereForAI/aya_dataset) (Apache 2.0), 100,000 prompts (Singh et al., 2024) - [WildChat GPT-4](https://huggingface.co/datasets/allenai/WildChat-1M) (ODC-BY-1.0), 100,000 prompts (Zhao et al., 2024) - [TableGPT](https://huggingface.co/datasets/LipengCS/Table-GPT) (MIT), 5,000 prompts (Zha et al., 2023) - [SciRIFF](https://huggingface.co/datasets/allenai/SciRIFF) (ODC-BY-1.0), 10,000 prompts (Wadden et al., 2024) - [Evol CodeAlpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) (Apache 2.0), 107,276 prompts (Luo et al., 2023) ## Dataset Structure Each example in the dataset contains the standard instruction-tuning data points as follow: - `id` (str): a unique identifier - `messages` (list): message format used for supervised fine-tuning (this contains user prompt and assistant responses) - `source` (str): the source dataset for the given sample ### Model Family | **Stage** | **Llama 3.1 8B** | **Llama 3.1 70B** | |----------------------|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------| | **Base Model** | [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B) | | **SFT** | [allenai/Llama-3.1-Tulu-3-8B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-SFT) | [allenai/Llama-3.1-Tulu-3-70B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-SFT) | | **DPO** | [allenai/Llama-3.1-Tulu-3-8B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-DPO) | [allenai/Llama-3.1-Tulu-3-70B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-DPO) | | **Final Models (RLVR)** | [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) | [allenai/Llama-3.1-Tulu-3-70B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B) | | **Reward Model (RM)**| [allenai/Llama-3.1-Tulu-3-8B-RM](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-RM) | (Same as 8B) | ## License This dataset is licensed under ODC-BY-1.0. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use). This dataset includes output data generated from third party models that are subject to separate terms governing their use. For more information on license and terms, consult each subset linked above. ## Citation If Tülu3 or any of the related materials were helpful to your work, please cite: ``` @article{lambert2024tulu3, title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training}, author = { Nathan Lambert and Jacob Morrison and Valentina Pyatkin and Shengyi Huang and Hamish Ivison and Faeze Brahman and Lester James V. Miranda and Alisa Liu and Nouha Dziri and Shane Lyu and Yuling Gu and Saumya Malik and Victoria Graf and Jena D. Hwang and Jiangjiang Yang and Ronan Le Bras and Oyvind Tafjord and Chris Wilhelm and Luca Soldaini and Noah A. Smith and Yizhong Wang and Pradeep Dasigi and Hannaneh Hajishirzi }, year = {2024}, email = {[email protected]} } ```

sasha/prof_images_blip__stabilityai-stable-diffusion-2

sasha

--- dataset_info: features: - name: images dtype: image - name: embeddings sequence: float32 splits: - name: courier num_bytes: 4257204.0 num_examples: 100 - name: aide num_bytes: 3491678.0 num_examples: 100 - name: police_officer num_bytes: 4252502.0 num_examples: 100 - name: purchasing_agent num_bytes: 5285492.0 num_examples: 100 - name: metal_worker num_bytes: 5097159.0 num_examples: 100 - name: financial_analyst num_bytes: 5261660.0 num_examples: 100 - name: stocker num_bytes: 5700830.0 num_examples: 100 - name: it_specialist num_bytes: 5731031.0 num_examples: 100 - name: writer num_bytes: 4309299.0 num_examples: 100 - name: accountant num_bytes: 4689435.0 num_examples: 100 - name: coach num_bytes: 4537870.0 num_examples: 100 - name: painter num_bytes: 3686294.0 num_examples: 100 - name: real_estate_broker num_bytes: 5241610.0 num_examples: 100 - name: truck_driver num_bytes: 4955237.0 num_examples: 100 - name: data_entry_keyer num_bytes: 6112746.0 num_examples: 100 - name: computer_support_specialist num_bytes: 5187781.0 num_examples: 100 - name: cook num_bytes: 3905039.0 num_examples: 100 - name: interior_designer num_bytes: 4970044.0 num_examples: 100 - name: nutritionist num_bytes: 4904269.0 num_examples: 100 - name: designer num_bytes: 5618928.0 num_examples: 100 - name: maid num_bytes: 3653112.0 num_examples: 100 - name: producer num_bytes: 5532083.0 num_examples: 100 - name: executive_assistant num_bytes: 5181631.0 num_examples: 100 - name: logistician num_bytes: 5623819.0 num_examples: 100 - name: tractor_operator num_bytes: 5566239.0 num_examples: 100 - name: doctor num_bytes: 3922429.0 num_examples: 100 - name: inventory_clerk num_bytes: 5675119.0 num_examples: 100 - name: sheet_metal_worker num_bytes: 4932393.0 num_examples: 100 - name: groundskeeper num_bytes: 5624913.0 num_examples: 100 - name: electrical_engineer num_bytes: 5486843.0 num_examples: 100 - name: physical_therapist num_bytes: 4416383.0 num_examples: 100 - name: insurance_agent num_bytes: 4503029.0 num_examples: 100 - name: aerospace_engineer num_bytes: 5005814.0 num_examples: 100 - 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name: scientist num_bytes: 4363783.0 num_examples: 100 - name: drywall_installer num_bytes: 4174819.0 num_examples: 100 - name: school_bus_driver num_bytes: 4998022.0 num_examples: 100 - name: dental_assistant num_bytes: 4140296.0 num_examples: 100 - name: fitness_instructor num_bytes: 4416504.0 num_examples: 100 - name: detective num_bytes: 4583678.0 num_examples: 100 - name: hairdresser num_bytes: 4463307.0 num_examples: 100 - name: welder num_bytes: 4918374.0 num_examples: 100 - name: pharmacy_technician num_bytes: 4661790.0 num_examples: 100 - name: compliance_officer num_bytes: 4845349.0 num_examples: 100 - name: singer num_bytes: 4638247.0 num_examples: 100 - name: tutor num_bytes: 3678185.0 num_examples: 100 - name: language_pathologist num_bytes: 5254361.0 num_examples: 100 - name: medical_records_specialist num_bytes: 5634506.0 num_examples: 100 - name: sales_manager num_bytes: 5056132.0 num_examples: 100 - name: industrial_engineer num_bytes: 5172786.0 num_examples: 100 - name: manager num_bytes: 4931846.0 num_examples: 100 - name: mechanic num_bytes: 4613784.0 num_examples: 100 - name: postal_worker num_bytes: 4441299.0 num_examples: 100 - name: computer_systems_analyst num_bytes: 6079263.0 num_examples: 100 - name: salesperson num_bytes: 5024667.0 num_examples: 100 - name: office_clerk num_bytes: 4755914.0 num_examples: 100 - name: claims_appraiser num_bytes: 4478395.0 num_examples: 100 - name: security_guard num_bytes: 5230558.0 num_examples: 100 - name: interviewer num_bytes: 4656965.0 num_examples: 100 - name: dispatcher num_bytes: 5291584.0 num_examples: 100 - name: lawyer num_bytes: 4277664.0 num_examples: 100 - name: marketing_manager num_bytes: 5252505.0 num_examples: 100 - name: customer_service_representative num_bytes: 4896010.0 num_examples: 100 - name: software_developer num_bytes: 5547030.0 num_examples: 100 - name: mover num_bytes: 4568118.0 num_examples: 100 - name: supervisor num_bytes: 4923186.0 num_examples: 100 - name: paralegal num_bytes: 4687033.0 num_examples: 100 - name: graphic_designer num_bytes: 5759315.0 num_examples: 100 - name: dentist num_bytes: 3970331.0 num_examples: 100 - name: roofer num_bytes: 5495073.0 num_examples: 100 - name: public_relations_specialist num_bytes: 5073442.0 num_examples: 100 - name: engineer num_bytes: 4250666.0 num_examples: 100 - name: occupational_therapist num_bytes: 3855692.0 num_examples: 100 - name: manicurist num_bytes: 4064118.0 num_examples: 100 - name: cleaner num_bytes: 5095971.0 num_examples: 100 - name: facilities_manager num_bytes: 5247879.0 num_examples: 100 - name: repair_worker num_bytes: 5113432.0 num_examples: 100 - name: cashier num_bytes: 5215203.0 num_examples: 100 - name: baker num_bytes: 4610706.0 num_examples: 100 - name: market_research_analyst num_bytes: 5593811.0 num_examples: 100 - name: health_technician num_bytes: 4477836.0 num_examples: 100 - name: veterinarian num_bytes: 4197438.0 num_examples: 100 - name: underwriter num_bytes: 4662113.0 num_examples: 100 - name: mechanical_engineer num_bytes: 5722417.0 num_examples: 100 - name: janitor num_bytes: 5390408.0 num_examples: 100 - name: pilot num_bytes: 3829685.0 num_examples: 100 - name: therapist num_bytes: 3859109.0 num_examples: 100 - name: director num_bytes: 4096085.0 num_examples: 100 - name: wholesale_buyer num_bytes: 6300854.0 num_examples: 100 - name: air_conditioning_installer num_bytes: 5741602.0 num_examples: 100 - name: butcher num_bytes: 5332426.0 num_examples: 100 - name: machinery_mechanic num_bytes: 5057875.0 num_examples: 100 - name: event_planner num_bytes: 5039870.0 num_examples: 100 - name: carpet_installer num_bytes: 5903738.0 num_examples: 100 - name: musician num_bytes: 4431405.0 num_examples: 100 - name: civil_engineer num_bytes: 5463535.0 num_examples: 100 - name: farmer num_bytes: 4772114.0 num_examples: 100 - name: financial_manager num_bytes: 5187071.0 num_examples: 100 - name: childcare_worker num_bytes: 4326077.0 num_examples: 100 - name: clerk num_bytes: 4268877.0 num_examples: 100 - name: machinist num_bytes: 4593820.0 num_examples: 100 - name: firefighter num_bytes: 4414229.0 num_examples: 100 - name: photographer num_bytes: 4056745.0 num_examples: 100 - name: file_clerk num_bytes: 5925494.0 num_examples: 100 - name: bus_driver num_bytes: 4714778.0 num_examples: 100 - name: fast_food_worker num_bytes: 4778503.0 num_examples: 100 - name: bartender num_bytes: 5407515.0 num_examples: 100 - name: computer_programmer num_bytes: 5961791.0 num_examples: 100 - name: pharmacist num_bytes: 5103862.0 num_examples: 100 - name: nursing_assistant num_bytes: 3969985.0 num_examples: 100 - name: career_counselor num_bytes: 4578703.0 num_examples: 100 - name: mental_health_counselor num_bytes: 4915411.0 num_examples: 100 - name: network_administrator num_bytes: 6576986.0 num_examples: 100 - name: teacher num_bytes: 4112341.0 num_examples: 100 - name: dishwasher num_bytes: 4582691.0 num_examples: 100 - name: teller num_bytes: 4580341.0 num_examples: 100 - name: teaching_assistant num_bytes: 4310141.0 num_examples: 100 - name: payroll_clerk num_bytes: 5104922.0 num_examples: 100 - name: laboratory_technician num_bytes: 4772940.0 num_examples: 100 - name: social_assistant num_bytes: 4975461.0 num_examples: 100 - name: radiologic_technician num_bytes: 4614401.0 num_examples: 100 - name: social_worker num_bytes: 4143912.0 num_examples: 100 - name: nurse num_bytes: 3251197.0 num_examples: 100 - name: receptionist num_bytes: 4962877.0 num_examples: 100 - name: carpenter num_bytes: 4367834.0 num_examples: 100 - name: correctional_officer num_bytes: 5027428.0 num_examples: 100 - name: community_manager num_bytes: 5327391.0 num_examples: 100 - name: massage_therapist num_bytes: 4309573.0 num_examples: 100 - name: head_cook num_bytes: 4488723.0 num_examples: 100 - name: plane_mechanic num_bytes: 4650683.0 num_examples: 100 download_size: 729196101 dataset_size: 705285705.0 --- # Dataset Card for "prof_images_blip__stabilityai-stable-diffusion-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

asahi417/seamless-align-enA-hiA.speaker-embedding.hubert-xl

asahi417

--- dataset_info: - config_name: subset_1 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 11456903470 num_examples: 2295 download_size: 11490865608 dataset_size: 11456903470 - config_name: subset_10 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8497469033 num_examples: 2026 download_size: 8525108036 dataset_size: 8497469033 - config_name: subset_11 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8184086859 num_examples: 1984 download_size: 8211671614 dataset_size: 8184086859 - config_name: subset_12 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8267732668 num_examples: 2004 download_size: 8293785190 dataset_size: 8267732668 - config_name: subset_13 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 7813596787 num_examples: 1931 download_size: 7836272058 dataset_size: 7813596787 - config_name: subset_14 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - 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name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 7951105030 num_examples: 2001 download_size: 7977767633 dataset_size: 7951105030 - config_name: subset_17 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8382711574 num_examples: 2022 download_size: 8410409589 dataset_size: 8382711574 - config_name: subset_18 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 7937878932 num_examples: 1988 download_size: 7964412330 dataset_size: 7937878932 - config_name: subset_19 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 7973944881 num_examples: 1965 download_size: 7996531378 dataset_size: 7973944881 - config_name: subset_2 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 11434325392 num_examples: 2335 download_size: 11468346317 dataset_size: 11434325392 - config_name: subset_20 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8072628489 num_examples: 1971 download_size: 8099998069 dataset_size: 8072628489 - config_name: subset_21 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 7842776502 num_examples: 1976 download_size: 7869318616 dataset_size: 7842776502 - config_name: subset_22 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - 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name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8293741230 num_examples: 1932 download_size: 8320952581 dataset_size: 8293741230 - config_name: subset_72 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8285879747 num_examples: 1916 download_size: 8313129863 dataset_size: 8285879747 - config_name: subset_73 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8262272112 num_examples: 1900 download_size: 8289363323 dataset_size: 8262272112 - config_name: subset_74 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 7997453485 num_examples: 1899 download_size: 8023483189 dataset_size: 7997453485 - config_name: subset_75 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8262038636 num_examples: 1927 download_size: 8289252589 dataset_size: 8262038636 - config_name: subset_76 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8232282368 num_examples: 1912 download_size: 8259407860 dataset_size: 8232282368 - config_name: subset_77 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8305966259 num_examples: 1922 download_size: 8333172713 dataset_size: 8305966259 - config_name: subset_78 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8058199324 num_examples: 1909 download_size: 8085492874 dataset_size: 8058199324 - config_name: subset_79 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8328483268 num_examples: 1948 download_size: 8355816563 dataset_size: 8328483268 - config_name: subset_8 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8540940621 num_examples: 2083 download_size: 8569954705 dataset_size: 8540940621 - config_name: subset_80 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 7992578739 num_examples: 1877 download_size: 8018456103 dataset_size: 7992578739 - config_name: subset_81 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8215049205 num_examples: 1898 download_size: 8242226727 dataset_size: 8215049205 - config_name: subset_82 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8192856985 num_examples: 1896 download_size: 8219934679 dataset_size: 8192856985 - config_name: subset_83 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8058929502 num_examples: 1878 download_size: 8085975218 dataset_size: 8058929502 - config_name: subset_84 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8161411475 num_examples: 1902 download_size: 8188601913 dataset_size: 8161411475 - config_name: subset_85 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8473572248 num_examples: 1984 download_size: 8499559293 dataset_size: 8473572248 - config_name: subset_86 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8526355265 num_examples: 1930 download_size: 8554702136 dataset_size: 8526355265 - config_name: subset_87 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 7955386516 num_examples: 1849 download_size: 7981125257 dataset_size: 7955386516 - config_name: subset_88 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 7865388552 num_examples: 1849 download_size: 7890537353 dataset_size: 7865388552 - config_name: subset_89 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8258633632 num_examples: 1894 download_size: 8285721378 dataset_size: 8258633632 - config_name: subset_9 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8493615469 num_examples: 2022 download_size: 8521092296 dataset_size: 8493615469 - config_name: subset_90 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 8001091016 num_examples: 1893 download_size: 8028255143 dataset_size: 8001091016 - config_name: subset_91 features: - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.laser_score dtype: float64 - name: hiA.id dtype: string - name: hiA.laser_score dtype: float64 - name: enA.audio.speaker_embedding sequence: float32 - name: enA.audio.speaker_embedding.full sequence: sequence: float32 - name: hiA.audio.speaker_embedding sequence: float32 - name: hiA.audio.speaker_embedding.full sequence: sequence: float32 splits: - name: train num_bytes: 7886456072 num_examples: 1820 download_size: 7912099547 dataset_size: 7886456072 configs: - config_name: subset_1 data_files: - split: train path: subset_1/train-* - config_name: subset_10 data_files: - split: train path: subset_10/train-* - config_name: subset_11 data_files: - split: train path: subset_11/train-* - config_name: subset_12 data_files: - split: train path: subset_12/train-* - config_name: subset_13 data_files: - split: train path: subset_13/train-* - config_name: subset_14 data_files: - split: train path: subset_14/train-* - config_name: subset_15 data_files: - split: train path: subset_15/train-* - config_name: subset_16 data_files: - split: train path: subset_16/train-* - config_name: subset_17 data_files: - split: train path: subset_17/train-* - config_name: subset_18 data_files: - split: train path: subset_18/train-* - config_name: subset_19 data_files: - split: train path: subset_19/train-* - config_name: subset_2 data_files: - split: train path: subset_2/train-* - config_name: subset_20 data_files: - split: train path: subset_20/train-* - config_name: subset_21 data_files: - split: train path: subset_21/train-* - config_name: subset_22 data_files: - split: train path: subset_22/train-* - config_name: subset_23 data_files: - split: train path: subset_23/train-* - config_name: subset_24 data_files: - split: train path: subset_24/train-* - config_name: subset_25 data_files: - split: train path: subset_25/train-* - config_name: subset_26 data_files: - split: train path: subset_26/train-* - config_name: subset_27 data_files: - split: train path: subset_27/train-* - config_name: subset_28 data_files: - split: train path: subset_28/train-* - config_name: subset_29 data_files: - split: train path: subset_29/train-* - config_name: subset_3 data_files: - split: train path: subset_3/train-* - config_name: subset_30 data_files: - split: train path: subset_30/train-* - config_name: subset_31 data_files: - split: train path: subset_31/train-* - config_name: subset_32 data_files: - split: train path: subset_32/train-* - config_name: subset_33 data_files: - split: train path: subset_33/train-* - config_name: subset_34 data_files: - split: train path: subset_34/train-* - config_name: subset_35 data_files: - split: train path: subset_35/train-* - config_name: subset_36 data_files: - split: train path: subset_36/train-* - config_name: subset_37 data_files: - split: train path: subset_37/train-* - config_name: subset_38 data_files: - split: train path: subset_38/train-* - config_name: subset_39 data_files: - split: train path: subset_39/train-* - config_name: subset_4 data_files: - split: train path: subset_4/train-* - config_name: subset_40 data_files: - split: train path: subset_40/train-* - config_name: subset_41 data_files: - split: train path: subset_41/train-* - config_name: subset_42 data_files: - split: train path: subset_42/train-* - config_name: subset_43 data_files: - split: train path: subset_43/train-* - config_name: subset_44 data_files: - split: train path: subset_44/train-* - config_name: subset_45 data_files: - split: train path: subset_45/train-* - config_name: subset_46 data_files: - split: train path: subset_46/train-* - config_name: subset_47 data_files: - split: train path: subset_47/train-* - config_name: subset_48 data_files: - split: train path: subset_48/train-* - config_name: subset_49 data_files: - split: train path: subset_49/train-* - config_name: subset_5 data_files: - split: train path: subset_5/train-* - config_name: subset_50 data_files: - split: train path: subset_50/train-* - config_name: subset_51 data_files: - split: train path: subset_51/train-* - config_name: subset_52 data_files: - split: train path: subset_52/train-* - config_name: subset_53 data_files: - split: train path: subset_53/train-* - config_name: subset_54 data_files: - split: train path: subset_54/train-* - config_name: subset_55 data_files: - split: train path: subset_55/train-* - config_name: subset_56 data_files: - split: train path: subset_56/train-* - config_name: subset_57 data_files: - split: train path: subset_57/train-* - config_name: subset_58 data_files: - split: train path: subset_58/train-* - config_name: subset_59 data_files: - split: train path: subset_59/train-* - config_name: subset_6 data_files: - split: train path: subset_6/train-* - config_name: subset_60 data_files: - split: train path: subset_60/train-* - config_name: subset_61 data_files: - split: train path: subset_61/train-* - config_name: subset_62 data_files: - split: train path: subset_62/train-* - config_name: subset_63 data_files: - split: train path: subset_63/train-* - config_name: subset_64 data_files: - split: train path: subset_64/train-* - config_name: subset_65 data_files: - split: train path: subset_65/train-* - config_name: subset_66 data_files: - split: train path: subset_66/train-* - config_name: subset_67 data_files: - split: train path: subset_67/train-* - config_name: subset_68 data_files: - split: train path: subset_68/train-* - config_name: subset_69 data_files: - split: train path: subset_69/train-* - config_name: subset_7 data_files: - split: train path: subset_7/train-* - config_name: subset_70 data_files: - split: train path: subset_70/train-* - config_name: subset_71 data_files: - split: train path: subset_71/train-* - config_name: subset_72 data_files: - split: train path: subset_72/train-* - config_name: subset_73 data_files: - split: train path: subset_73/train-* - config_name: subset_74 data_files: - split: train path: subset_74/train-* - config_name: subset_75 data_files: - split: train path: subset_75/train-* - config_name: subset_76 data_files: - split: train path: subset_76/train-* - config_name: subset_77 data_files: - split: train path: subset_77/train-* - config_name: subset_78 data_files: - split: train path: subset_78/train-* - config_name: subset_79 data_files: - split: train path: subset_79/train-* - config_name: subset_8 data_files: - split: train path: subset_8/train-* - config_name: subset_80 data_files: - split: train path: subset_80/train-* - config_name: subset_81 data_files: - split: train path: subset_81/train-* - config_name: subset_82 data_files: - split: train path: subset_82/train-* - config_name: subset_83 data_files: - split: train path: subset_83/train-* - config_name: subset_84 data_files: - split: train path: subset_84/train-* - config_name: subset_85 data_files: - split: train path: subset_85/train-* - config_name: subset_86 data_files: - split: train path: subset_86/train-* - config_name: subset_87 data_files: - split: train path: subset_87/train-* - config_name: subset_88 data_files: - split: train path: subset_88/train-* - config_name: subset_89 data_files: - split: train path: subset_89/train-* - config_name: subset_9 data_files: - split: train path: subset_9/train-* - config_name: subset_90 data_files: - split: train path: subset_90/train-* - config_name: subset_91 data_files: - split: train path: subset_91/train-* ---

IWSLT/iwslt2017

IWSLT

--- annotations_creators: - crowdsourced language: - ar - de - en - fr - it - ja - ko - nl - ro - zh language_creators: - expert-generated license: - cc-by-nc-nd-4.0 multilinguality: - translation pretty_name: IWSLT 2017 size_categories: - 1M<n<10M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: iwslt-2017 dataset_info: - config_name: iwslt2017-en-it features: - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 46647925 num_examples: 231619 - name: test num_bytes: 305246 num_examples: 1566 - name: validation num_bytes: 200023 num_examples: 929 download_size: 329391132 dataset_size: 47153194 - config_name: iwslt2017-en-nl features: - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 42843933 num_examples: 237240 - name: test num_bytes: 311646 num_examples: 1777 - name: validation num_bytes: 197814 num_examples: 1003 download_size: 329391132 dataset_size: 43353393 - config_name: iwslt2017-en-ro features: - name: translation dtype: translation: languages: - en - ro splits: - name: train num_bytes: 44129950 num_examples: 220538 - name: test num_bytes: 316790 num_examples: 1678 - name: validation num_bytes: 205028 num_examples: 914 download_size: 329391132 dataset_size: 44651768 - config_name: iwslt2017-it-en features: - name: translation dtype: translation: languages: - it - en splits: - name: train num_bytes: 46647925 num_examples: 231619 - name: test num_bytes: 305246 num_examples: 1566 - name: validation num_bytes: 200023 num_examples: 929 download_size: 329391132 dataset_size: 47153194 - config_name: iwslt2017-it-nl features: - name: translation dtype: translation: languages: - it - nl splits: - name: train num_bytes: 43033168 num_examples: 233415 - name: test num_bytes: 309725 num_examples: 1669 - name: validation num_bytes: 197774 num_examples: 1001 download_size: 329391132 dataset_size: 43540667 - config_name: iwslt2017-it-ro features: - name: translation dtype: translation: languages: - it - ro splits: - name: train num_bytes: 44485169 num_examples: 217551 - name: test num_bytes: 314974 num_examples: 1643 - name: validation num_bytes: 204989 num_examples: 914 download_size: 329391132 dataset_size: 45005132 - config_name: iwslt2017-nl-en features: - name: translation dtype: translation: languages: - nl - en splits: - name: train num_bytes: 42843933 num_examples: 237240 - name: test num_bytes: 311646 num_examples: 1777 - name: validation num_bytes: 197814 num_examples: 1003 download_size: 329391132 dataset_size: 43353393 - config_name: iwslt2017-nl-it features: - name: translation dtype: translation: languages: - nl - it splits: - name: train num_bytes: 43033168 num_examples: 233415 - name: test num_bytes: 309725 num_examples: 1669 - name: validation num_bytes: 197774 num_examples: 1001 download_size: 329391132 dataset_size: 43540667 - config_name: iwslt2017-nl-ro features: - name: translation dtype: translation: languages: - nl - ro splits: - name: train num_bytes: 41338738 num_examples: 206920 - name: test num_bytes: 320952 num_examples: 1680 - name: validation num_bytes: 202380 num_examples: 913 download_size: 329391132 dataset_size: 41862070 - config_name: iwslt2017-ro-en features: - name: translation dtype: translation: languages: - ro - en splits: - name: train num_bytes: 44129950 num_examples: 220538 - name: test num_bytes: 316790 num_examples: 1678 - name: validation num_bytes: 205028 num_examples: 914 download_size: 329391132 dataset_size: 44651768 - config_name: iwslt2017-ro-it features: - name: translation dtype: translation: languages: - ro - it splits: - name: train num_bytes: 44485169 num_examples: 217551 - name: test num_bytes: 314974 num_examples: 1643 - name: validation num_bytes: 204989 num_examples: 914 download_size: 329391132 dataset_size: 45005132 - config_name: iwslt2017-ro-nl features: - name: translation dtype: translation: languages: - ro - nl splits: - name: train num_bytes: 41338738 num_examples: 206920 - name: test num_bytes: 320952 num_examples: 1680 - name: validation num_bytes: 202380 num_examples: 913 download_size: 329391132 dataset_size: 41862070 - config_name: iwslt2017-ar-en features: - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 56481059 num_examples: 231713 - name: test num_bytes: 2014296 num_examples: 8583 - name: validation num_bytes: 241206 num_examples: 888 download_size: 27748780 dataset_size: 58736561 - config_name: iwslt2017-de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 42608380 num_examples: 206112 - name: test num_bytes: 1608474 num_examples: 8079 - name: validation num_bytes: 210975 num_examples: 888 download_size: 16758320 dataset_size: 44427829 - config_name: iwslt2017-en-ar features: - name: translation dtype: translation: languages: - en - ar splits: - name: train num_bytes: 56481059 num_examples: 231713 - name: test num_bytes: 2014296 num_examples: 8583 - name: validation num_bytes: 241206 num_examples: 888 download_size: 29333173 dataset_size: 58736561 - config_name: iwslt2017-en-de features: - name: translation dtype: translation: languages: - en - de splits: - name: train num_bytes: 42608380 num_examples: 206112 - name: test num_bytes: 1608474 num_examples: 8079 - name: validation num_bytes: 210975 num_examples: 888 download_size: 16758334 dataset_size: 44427829 - config_name: iwslt2017-en-fr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 49273286 num_examples: 232825 - name: test num_bytes: 1767465 num_examples: 8597 - name: validation num_bytes: 207579 num_examples: 890 download_size: 27699724 dataset_size: 51248330 - config_name: iwslt2017-en-ja features: - name: translation dtype: translation: languages: - en - ja splits: - name: train num_bytes: 48204987 num_examples: 223108 - name: test num_bytes: 1809007 num_examples: 8469 - name: validation num_bytes: 208124 num_examples: 871 download_size: 26983602 dataset_size: 50222118 - config_name: iwslt2017-en-ko features: - name: translation dtype: translation: languages: - en - ko splits: - name: train num_bytes: 51678043 num_examples: 230240 - name: test num_bytes: 1869793 num_examples: 8514 - name: validation num_bytes: 219295 num_examples: 879 download_size: 19364776 dataset_size: 53767131 - config_name: iwslt2017-en-zh features: - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 44271004 num_examples: 231266 - name: test num_bytes: 1605527 num_examples: 8549 - name: validation num_bytes: 202537 num_examples: 879 download_size: 27597071 dataset_size: 46079068 - config_name: iwslt2017-fr-en features: - name: translation dtype: translation: languages: - fr - en splits: - name: train num_bytes: 49273286 num_examples: 232825 - name: test num_bytes: 1767465 num_examples: 8597 - name: validation num_bytes: 207579 num_examples: 890 download_size: 26880731 dataset_size: 51248330 - config_name: iwslt2017-ja-en features: - name: translation dtype: translation: languages: - ja - en splits: - name: train num_bytes: 48204987 num_examples: 223108 - name: test num_bytes: 1809007 num_examples: 8469 - name: validation num_bytes: 208124 num_examples: 871 download_size: 26190859 dataset_size: 50222118 - config_name: iwslt2017-ko-en features: - name: translation dtype: translation: languages: - ko - en splits: - name: train num_bytes: 51678043 num_examples: 230240 - name: test num_bytes: 1869793 num_examples: 8514 - name: validation num_bytes: 219295 num_examples: 879 download_size: 19364733 dataset_size: 53767131 - config_name: iwslt2017-zh-en features: - name: translation dtype: translation: languages: - zh - en splits: - name: train num_bytes: 44271004 num_examples: 231266 - name: test num_bytes: 1605527 num_examples: 8549 - name: validation num_bytes: 202537 num_examples: 879 download_size: 26849290 dataset_size: 46079068 --- # Dataset Card for IWSLT 2017 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://sites.google.com/site/iwsltevaluation2017/TED-tasks](https://sites.google.com/site/iwsltevaluation2017/TED-tasks) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Overview of the IWSLT 2017 Evaluation Campaign](https://aclanthology.org/2017.iwslt-1.1/) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 4.24 GB - **Size of the generated dataset:** 1.14 GB - **Total amount of disk used:** 5.38 GB ### Dataset Summary The IWSLT 2017 Multilingual Task addresses text translation, including zero-shot translation, with a single MT system across all directions including English, German, Dutch, Italian and Romanian. As unofficial task, conventional bilingual text translation is offered between English and Arabic, French, Japanese, Chinese, German and Korean. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### iwslt2017-ar-en - **Size of downloaded dataset files:** 27.75 MB - **Size of the generated dataset:** 58.74 MB - **Total amount of disk used:** 86.49 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"ar\": \"لقد طرت في \\\"القوات الجوية \\\" لمدة ثمان سنوات. والآن أجد نفسي مضطرا لخلع حذائي قبل صعود الطائرة!\", \"en\": \"I flew on Air ..." } ``` #### iwslt2017-de-en - **Size of downloaded dataset files:** 16.76 MB - **Size of the generated dataset:** 44.43 MB - **Total amount of disk used:** 61.18 MB An example of 'train' looks as follows. ``` { "translation": { "de": "Es ist mir wirklich eine Ehre, zweimal auf dieser Bühne stehen zu dürfen. Tausend Dank dafür.", "en": "And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful." } } ``` #### iwslt2017-en-ar - **Size of downloaded dataset files:** 29.33 MB - **Size of the generated dataset:** 58.74 MB - **Total amount of disk used:** 88.07 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"ar\": \"لقد طرت في \\\"القوات الجوية \\\" لمدة ثمان سنوات. والآن أجد نفسي مضطرا لخلع حذائي قبل صعود الطائرة!\", \"en\": \"I flew on Air ..." } ``` #### iwslt2017-en-de - **Size of downloaded dataset files:** 16.76 MB - **Size of the generated dataset:** 44.43 MB - **Total amount of disk used:** 61.18 MB An example of 'validation' looks as follows. ``` { "translation": { "de": "Die nächste Folie, die ich Ihnen zeige, ist eine Zeitrafferaufnahme was in den letzten 25 Jahren passiert ist.", "en": "The next slide I show you will be a rapid fast-forward of what's happened over the last 25 years." } } ``` #### iwslt2017-en-fr - **Size of downloaded dataset files:** 27.69 MB - **Size of the generated dataset:** 51.24 MB - **Total amount of disk used:** 78.94 MB An example of 'validation' looks as follows. ``` { "translation": { "en": "But this understates the seriousness of this particular problem because it doesn't show the thickness of the ice.", "fr": "Mais ceci tend à amoindrir le problème parce qu'on ne voit pas l'épaisseur de la glace." } } ``` ### Data Fields The data fields are the same among all splits. #### iwslt2017-ar-en - `translation`: a multilingual `string` variable, with possible languages including `ar`, `en`. #### iwslt2017-de-en - `translation`: a multilingual `string` variable, with possible languages including `de`, `en`. #### iwslt2017-en-ar - `translation`: a multilingual `string` variable, with possible languages including `en`, `ar`. #### iwslt2017-en-de - `translation`: a multilingual `string` variable, with possible languages including `en`, `de`. #### iwslt2017-en-fr - `translation`: a multilingual `string` variable, with possible languages including `en`, `fr`. ### Data Splits | name |train |validation|test| |---------------|-----:|---------:|---:| |iwslt2017-ar-en|231713| 888|8583| |iwslt2017-de-en|206112| 888|8079| |iwslt2017-en-ar|231713| 888|8583| |iwslt2017-en-de|206112| 888|8079| |iwslt2017-en-fr|232825| 890|8597| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information Creative Commons BY-NC-ND See the (TED Talks Usage Policy)[https://www.ted.com/about/our-organization/our-policies-terms/ted-talks-usage-policy]. ### Citation Information ``` @inproceedings{cettolo-etal-2017-overview, title = "Overview of the {IWSLT} 2017 Evaluation Campaign", author = {Cettolo, Mauro and Federico, Marcello and Bentivogli, Luisa and Niehues, Jan and St{\"u}ker, Sebastian and Sudoh, Katsuhito and Yoshino, Koichiro and Federmann, Christian}, booktitle = "Proceedings of the 14th International Conference on Spoken Language Translation", month = dec # " 14-15", year = "2017", address = "Tokyo, Japan", publisher = "International Workshop on Spoken Language Translation", url = "https://aclanthology.org/2017.iwslt-1.1", pages = "2--14", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@Narsil](https://github.com/Narsil) for adding this dataset.

bigcode/starcoderdata

bigcode

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - multilingual pretty_name: The-Stack size_categories: - unknown source_datasets: [] task_categories: - text-generation extra_gated_prompt: >- ## Terms of Use for The Stack The Stack dataset is a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset: 1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. 2. The Stack is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset’s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes. 3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it. By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well. extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox --- # StarCoder Training Dataset ## Dataset description This is the dataset used for training [StarCoder](https://huggingface.co/bigcode/starcoder) and [StarCoderBase](https://huggingface.co/bigcode/starcoderbase). It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs, and 32GB of GitHub commits, which is approximately 250 Billion tokens. ## Dataset creation The creation and filtering of The Stack is explained in the [original dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup), we additionally decontaminate and clean all 86 programming languages in the dataset, in addition to GitHub issues, Jupyter Notebooks and GitHub commits. We also apply near-deduplication and remove PII, all details are mentionned in our [Paper: 💫 StarCoder, May The Source Be With You](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view) ## How to use the dataset ```python from datasets import load_dataset # to load python for example ds = load_dataset("bigcode/starcoderdata", data_dir="python", split="train") ``` GitHub issues, GitHub commits and Jupyter notebooks subsets have different columns from the rest so loading the entire dataset at once may fail, we suggest loading programming languages separatly from these categories. ```` jupyter-scripts-dedup-filtered jupyter-structured-clean-dedup github-issues-filtered-structured git-commits-cleaned ````

wmt/wmt14

wmt

--- annotations_creators: - no-annotation language_creators: - found language: - cs - de - en - fr - hi - ru license: - unknown multilinguality: - translation size_categories: - 10M<n<100M source_datasets: - extended|europarl_bilingual - extended|giga_fren - extended|news_commentary - extended|un_multi - extended|hind_encorp task_categories: - translation task_ids: [] paperswithcode_id: wmt-2014 pretty_name: WMT14 dataset_info: - config_name: cs-en features: - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 280992026 num_examples: 953621 - name: validation num_bytes: 702465 num_examples: 3000 - name: test num_bytes: 757809 num_examples: 3003 download_size: 168878237 dataset_size: 282452300 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 1358406800 num_examples: 4508785 - name: validation num_bytes: 736407 num_examples: 3000 - name: test num_bytes: 777326 num_examples: 3003 download_size: 818467512 dataset_size: 1359920533 - config_name: fr-en features: - name: translation dtype: translation: languages: - fr - en splits: - name: train num_bytes: 14752522252 num_examples: 40836715 - name: validation num_bytes: 744439 num_examples: 3000 - name: test num_bytes: 838849 num_examples: 3003 download_size: 7777527744 dataset_size: 14754105540 - config_name: hi-en features: - name: translation dtype: translation: languages: - hi - en splits: - name: train num_bytes: 1936003 num_examples: 32863 - name: validation num_bytes: 181457 num_examples: 520 - name: test num_bytes: 1075008 num_examples: 2507 download_size: 1583004 dataset_size: 3192468 - config_name: ru-en features: - name: translation dtype: translation: languages: - ru - en splits: - name: train num_bytes: 433209078 num_examples: 1486965 - name: validation num_bytes: 977938 num_examples: 3000 - name: test num_bytes: 1087738 num_examples: 3003 download_size: 223537244 dataset_size: 435274754 configs: - config_name: cs-en data_files: - split: train path: cs-en/train-* - split: validation path: cs-en/validation-* - split: test path: cs-en/test-* - config_name: de-en data_files: - split: train path: de-en/train-* - split: validation path: de-en/validation-* - split: test path: de-en/test-* - config_name: fr-en data_files: - split: train path: fr-en/train-* - split: validation path: fr-en/validation-* - split: test path: fr-en/test-* - config_name: hi-en data_files: - split: train path: hi-en/train-* - split: validation path: hi-en/validation-* - split: test path: hi-en/test-* - config_name: ru-en data_files: - split: train path: ru-en/train-* - split: validation path: ru-en/validation-* - split: test path: ru-en/test-* --- # Dataset Card for "wmt14" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://www.statmt.org/wmt14/translation-task.html](http://www.statmt.org/wmt14/translation-task.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.70 GB - **Size of the generated dataset:** 282.95 MB - **Total amount of disk used:** 1.98 GB ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p> <ul> <li>Non-English files contain many English sentences.</li> <li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li> </ul> <p>We have contacted the WMT organizers, and in response, they have indicated that they do not have plans to update the Common Crawl corpus data. Their rationale pertains to the expectation that such data has been superseded, primarily by CCMatrix, and to some extent, by ParaCrawl datasets.</p> </div> Translation dataset based on the data from statmt.org. Versions exist for different years using a combination of data sources. The base `wmt` allows you to create a custom dataset by choosing your own data/language pair. This can be done as follows: ```python from datasets import inspect_dataset, load_dataset_builder inspect_dataset("wmt14", "path/to/scripts") builder = load_dataset_builder( "path/to/scripts/wmt_utils.py", language_pair=("fr", "de"), subsets={ datasets.Split.TRAIN: ["commoncrawl_frde"], datasets.Split.VALIDATION: ["euelections_dev2019"], }, ) # Standard version builder.download_and_prepare() ds = builder.as_dataset() # Streamable version ds = builder.as_streaming_dataset() ``` ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### cs-en - **Size of downloaded dataset files:** 1.70 GB - **Size of the generated dataset:** 282.95 MB - **Total amount of disk used:** 1.98 GB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### cs-en - `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`. ### Data Splits |name |train |validation|test| |-----|-----:|---------:|---:| |cs-en|953621| 3000|3003| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{bojar-EtAl:2014:W14-33, author = {Bojar, Ondrej and Buck, Christian and Federmann, Christian and Haddow, Barry and Koehn, Philipp and Leveling, Johannes and Monz, Christof and Pecina, Pavel and Post, Matt and Saint-Amand, Herve and Soricut, Radu and Specia, Lucia and Tamchyna, Ale {s}}, title = {Findings of the 2014 Workshop on Statistical Machine Translation}, booktitle = {Proceedings of the Ninth Workshop on Statistical Machine Translation}, month = {June}, year = {2014}, address = {Baltimore, Maryland, USA}, publisher = {Association for Computational Linguistics}, pages = {12--58}, url = {http://www.aclweb.org/anthology/W/W14/W14-3302} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

lmqg/qg_koquad

lmqg

--- license: cc-by-4.0 pretty_name: KorQuAD for question generation language: ko multilinguality: monolingual size_categories: 10K<n<100K source_datasets: squad_es task_categories: - text-generation task_ids: - language-modeling tags: - question-generation --- # Dataset Card for "lmqg/qg_korquad" ## Dataset Description - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) - **Point of Contact:** [Asahi Ushio](http://asahiushio.com/) ### Dataset Summary This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in ["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992). This is a modified version of [KorQuAD](https://huggingface.co/datasets/squad_kor_v1) for question generation (QG) task. Since the original dataset only contains training/validation set, we manually sample test set from training set, which has no overlap in terms of the paragraph with the training set. ### Supported Tasks and Leaderboards * `question-generation`: The dataset is assumed to be used to train a model for question generation. Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail). ### Languages Korean (ko) ## Dataset Structure An example of 'train' looks as follows. ``` { "question": "함수해석학이 주목하는 탐구는?", "paragraph": "변화에 대한 이해와 묘사는 자연과학에 있어서 일반적인 주제이며, 미적분학은 변화를 탐구하는 강력한 도구로서 발전되었다. 함수는 변화하는 양을 묘사함에 있어서 중추적인 개념으로써 떠오르게 된다. 실수와 실변수로 구성된 함수의 엄밀한 탐구가 실해석학이라는 분야로 알려지게 되었고, 복소수에 대한 이와 같은 탐구분야는 복소해석학이라고 한다. 함수해석학은 함수의 공간(특히 무한차원)의 탐구에 주목한다. 함수해석학의 많은 응용분야 중 하나가 양자역학이다. 많은 문제들이 자연스럽게 양과 그 양의 변화율의 관계로 귀착되고, 이러한 문제들이 미분방정식으로 다루어진다. 자연의 많은 현상들이 동역학계로 기술될 수 있다. 혼돈 이론은 이러한 예측 불가능한 현상을 탐구하는 데 상당한 기여를 한다.", "answer": "함수의 공간(특히 무한차원)의 탐구", "sentence": "함수해석학은 함수의 공간(특히 무한차원)의 탐구 에 주목한다.", "paragraph_sentence": '변화에 대한 이해와 묘사는 자연과학에 있어서 일반적인 주제이며, 미적분학은 변화를 탐구하는 강력한 도구로서 발전되었다. 함수는 변화하는 양을 묘사함에 있어서 중추적인 개념으로써 떠오르게 된다. 실수와 실변수로 구성된 함수의 엄밀한 탐구가 실해석학이라는 분야로 알려지게 되었고, 복소수에 대한 이와 같은 탐구 분야는 복소해석학이라고 한다. <hl> 함수해석학은 함수의 공간(특히 무한차원)의 탐구 에 주목한다. <hl> 함수해석학의 많은 응용분야 중 하나가 양자역학이다. 많은 문제들이 자연스럽게 양과 그 양의 변화율의 관계로 귀착되고, 이러한 문제들이 미분방정식으로 다루어진다. 자연의 많은 현상들이 동역학계로 기술될 수 있다. 혼돈 이론은 이러한 예측 불가능한 현상을 탐구하는 데 상당한 기여를 한다.', "paragraph_answer": '변화에 대한 이해와 묘사는 자연과학에 있어서 일반적인 주제이며, 미적분학은 변화를 탐구하는 강력한 도구로서 발전되었다. 함수는 변화하는 양을 묘사함에 있어서 중추적인 개념으로써 떠오르게 된다. 실수와 실변수로 구성된 함수의 엄밀한 탐구가 실해석학이라는 분야로 알려지게 되었고, 복소수에 대한 이와 같은 탐구 분야는 복소해석학이라고 한다. 함수해석학은 <hl> 함수의 공간(특히 무한차원)의 탐구 <hl>에 주목한다. 함수해석학의 많은 응용분야 중 하나가 양자역학이다. 많은 문제들이 자연스럽게 양과 그 양의 변화율의 관계로 귀착되고, 이러한 문제들이 미분방정식으로 다루어진다. 자연의 많은 현상들이 동역학계로 기술될 수 있다. 혼돈 이론은 이러한 예측 불가능한 현상을 탐구하는 데 상당한 기여를 한다.', "sentence_answer": "함수해석학은 <hl> 함수의 공간(특히 무한차원)의 탐구 <hl> 에 주목한다." } ``` The data fields are the same among all splits. - `question`: a `string` feature. - `paragraph`: a `string` feature. - `answer`: a `string` feature. - `sentence`: a `string` feature. - `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`. - `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`. - `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`. Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model, but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and `paragraph_sentence` feature is for sentence-aware question generation. ## Data Splits |train|validation|test | |----:|---------:|----:| |54556| 5766 |5766 | ## Citation Information ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

deepghs/danbooru2023-webp-4Mpixel_index

deepghs

--- license: mit task_categories: - image-classification - image-to-image - text-to-image language: - en - ja size_categories: - 1M<n<10M --- Index files of [KBlueLeaf/danbooru2023-webp-4Mpixel](https://huggingface.co/datasets/KBlueLeaf/danbooru2023-webp-4Mpixel). You can download images from KBlueLeaf/danbooru2023-webp-4Mpixel with [cheesechaser](https://github.com/deepghs/cheesechaser). ```python from cheesechaser.datapool import DanbooruWebpDataPool pool = DanbooruWebpDataPool() # download danbooru images with webp format, to directory /data/danbooru_webp pool.batch_download_to_directory( resource_ids=range(6000000, 6001000), dst_dir='/data/danbooru_webp', max_workers=12, ) ```

mteb/twentynewsgroups-clustering

mteb

--- language: - en ---

mteb/scidocs-reranking

mteb

--- language: - en ---

jkot/merged_preprocessed_parliament_commonvoice

jkot

--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 210499135424 num_examples: 219101 - name: test num_bytes: 11099630080 num_examples: 11555 download_size: 65027813279 dataset_size: 221598765504 --- # Dataset Card for "merged_preprocessed_parliament_commonvoice" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mteb/summeval

mteb

--- language: - en --- # SummEval The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total). Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total). Summaries were evaluated across 4 dimensions: coherence, consistency, fluency, relevance. Each source news article comes with the original reference from the CNN/DailyMail dataset and 10 additional crowdsources reference summaries. For this dataset, we averaged the 3 **expert** annotations to get the human scores. source: https://github.com/Yale-LILY/SummEval

yzwang/X2I-subject-driven

yzwang

--- license: apache-2.0 task_categories: - text-to-image - image-to-image language: - en size_categories: - 1M<n<10M --- # X2I Dataset * Project Page: [https://vectorspacelab.github.io/OmniGen/](https://vectorspacelab.github.io/OmniGen/) * Github: [https://github.com/VectorSpaceLab/OmniGen](https://github.com/VectorSpaceLab/OmniGen) * Paper: [https://arxiv.org/abs/2409.11340](https://arxiv.org/abs/2409.11340) * Model: [https://huggingface.co/Shitao/OmniGen-v1](https://huggingface.co/Shitao/OmniGen-v1) To achieve robust multi-task processing capabilities, it is essential to train the **OmniGen** on large-scale and diverse datasets. However, in the field of unified image generation, a readily available dataset has yet to emerge. For this reason, we have curated a large-scale **unified image generation** dataset with unified format for the **first time**, which we refer to as the **X2I dataset**, meaning **"anything to image"**. | Task| Datastet| | :-------- | :-------- | | Multi-modal Instruction| [X2I-mm-instruction](https://huggingface.co/datasets/yzwang/X2I-mm-instruction) | | Subject-driven Editing | [X2I-subject-driven](https://huggingface.co/datasets/yzwang/X2I-subject-driven) | | In-context Learning | [X2I-in-context-learning](https://huggingface.co/datasets/yzwang/X2I-in-context-learning) | | Computer Vision | [X2I-computer-vision](https://huggingface.co/datasets/yzwang/X2I-computer-vision) | | Text to Image Generation| [X2I-text-to-image](https://huggingface.co/datasets/yzwang/X2I-text-to-image) | ## X2I-subject-driven - **Web-Image** A self-built subject-driven editing dataset with 36,316 & 45,425 & 111,734 samples. ```python ## meta file: web-image-1.jsonl && web-image-2.jsonl && web-image-3.jsonl cd retrieval tar -zxvf download_images.tar.gz tar -zxvf download_images_two.tar.gz ``` - **GRIT-Entity** A subject-driven editing dataset with 1,708,742 samples. ```python ## meta file: grit-entity.jsonl cd grit/images1 tar -zxvf 00034.tar.gz # tar -zxvf 00066.tar.gz # ... cd grit/images2 tar -zxvf 00034.tar.gz # tar -zxvf 00066.tar.gz # ... cd grit/images3 tar -zxvf 00168.tar.gz # tar -zxvf 00187.tar.gz # ... ``` - **GRIT-Entity-New** A self-built subject-driven editing dataset with 676,603 samples. This datasets is smaller than GRIT-Entity but with higher qualtiy. ```python ## meta file: grit-entity-new.jsonl cd character tar -xzvf character.tar.gz cd human/human2 tar -xzvf human2.tar.gz cd human/human3 tar -xzvf human3.tar.gz cd single cat single.tar.gz.* | tar -xzvf - cd double cat double.tar.gz.* | tar -xzvf - cd triple cat triple.tar.gz.* | tar -xzvf - ```

linagora/linto-dataset-audio-ar-tn-augmented

linagora

--- language: - ar task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio license: cc-by-4.0 version: 1.0 dataset_info: - config_name: default features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string - config_name: ApprendreLeTunisienVCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 839147756.322 num_examples: 6146 download_size: 798894474 dataset_size: 839147756.322 - config_name: MASC_NoiseLess features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 1798927453.0 num_examples: 48 download_size: 1508394957 dataset_size: 1798927453.0 - config_name: MASC_NoiseLess_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 6297517576.0 num_examples: 336 download_size: 5218109270 dataset_size: 6297517576.0 - config_name: OneStory_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 2948770377.0 num_examples: 216 download_size: 2745380587 dataset_size: 2948770377.0 - config_name: TunSwitchCS_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 16211221231.134 num_examples: 37639 download_size: 18870351203 dataset_size: 16211221231.134 - config_name: TunSwitchTO_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 5926536342.08 num_examples: 15365 download_size: 5236455978 dataset_size: 5926536342.08 - config_name: Youtube_AbdelAzizErwi_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 39027242686.0 num_examples: 125 download_size: 30064752032 dataset_size: 39027242686.0 - config_name: Youtube_BayariBilionaireVCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 1557801334.0 num_examples: 30 download_size: 1524983572 dataset_size: 1557801334.0 - config_name: Youtube_DiwanFM_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 12125888408.0 num_examples: 252 download_size: 11966562052 dataset_size: 12125888408.0 - config_name: Youtube_HkeyetTounsiaMensia_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 3883840637.0 num_examples: 35 download_size: 3803268888 dataset_size: 3883840637.0 - config_name: Youtube_LobnaMajjedi_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 2126737013.0 num_examples: 14 download_size: 2045521265 dataset_size: 2126737013.0 - config_name: Youtube_MohamedKhammessi_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 3850743255.0 num_examples: 14 download_size: 3803407855 dataset_size: 3850743255.0 - config_name: Youtube_Shorts_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 8401284864.0 num_examples: 945 download_size: 8279119035 dataset_size: 8401284864.0 - config_name: Youtube_TNScrapped_V1_NoiseLess features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 2510511859.0 num_examples: 52 download_size: 2163493076 dataset_size: 2510511859.0 - config_name: Youtube_TNScrapped_V1_NoiseLess_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 8973984541.0 num_examples: 364 download_size: 7561296937 dataset_size: 8973984541.0 - config_name: Youtube_TV_VCA features: - name: audio_id dtype: string - name: audio dtype: audio - name: segments list: - name: end dtype: float64 - name: start dtype: float64 - name: transcript dtype: string - name: transcript_raw dtype: string - name: transcript dtype: string splits: - name: train num_bytes: 1357183734.0 num_examples: 28 download_size: 1317232730 dataset_size: 1357183734.0 configs: - config_name: default default: true data_files: - split: train path: data/*/train/train-* - config_name: ApprendreLeTunisienVCA data_files: - split: train path: data/ApprendreLeTunisien_VCA/train/train-* - config_name: MASC_NoiseLess data_files: - split: train path: data/MASC_NoiseLess/train/train-* - config_name: MASC_NoiseLess_VCA data_files: - split: train path: data/MASC_NoiseLess_VCA/train/train-* - config_name: OneStoryVCA data_files: - split: train path: data/OneStory_VCA/train/train-* - config_name: TunSwitchCS_VCA data_files: - split: train path: data/TunSwitchCS_VCA/train/train-* - config_name: TunSwitchTO_VCA data_files: - split: train path: data/TunSwitchTO_VCA/train/train-* - config_name: Youtube_AbdelAzizErwi_VCA data_files: - split: train path: data/Youtube_AbdelAzizErwi_VCA/train/train-* - config_name: Youtube_BayariBilionaireVCA data_files: - split: train path: data/Youtube_BayariBilionaire_VCA/train/train-* - config_name: Youtube_DiwanFM_VCA data_files: - split: train path: data/Youtube_DiwanFM_VCA/train/train-* - config_name: Youtube_HkeyetTounsiaMensia_VCA data_files: - split: train path: data/Youtube_HkeyetTounsiaMensia_VCA/train/train-* - config_name: Youtube_LobnaMajjedi_VCA data_files: - split: train path: data/Youtube_LobnaMajjedi_VCA/train/train-* - config_name: Youtube_MohamedKhammessi_VCA data_files: - split: train path: data/Youtube_MohamedKhammessi_VCA/train/train-* - config_name: Youtube_Shorts_VCA data_files: - split: train path: data/Youtube_Shorts_VCA/train/train-* - config_name: Youtube_TNScrapped_V1_NoiseLess data_files: - split: train path: data/Youtube_TNScrapped_V1_NoiseLess/train/train-* - config_name: Youtube_TNScrapped_V1_NoiseLess_VCA data_files: - split: train path: data/Youtube_TNScrapped_V1_NoiseLess_VCA/train/train-* - config_name: Youtube_TV_VCA data_files: - split: train path: data/Youtube_TV_VCA/train/train-* --- # LinTO DataSet Audio for Arabic Tunisian Augmented <br />*A collection of Tunisian dialect audio and its annotations for STT task* This is the augmented datasets used to train the Linto Tunisian dialect with code-switching STT [linagora/linto-asr-ar-tn](https://huggingface.co/linagora/linto-asr-ar-tn). * [Dataset Summary](#dataset-summary) * [Dataset composition](#dataset-composition) * [Sources](#sources) * [Content Types](#content-types) * [Languages and Dialects](#languages-and-dialects) * [Example use (python)](#example-use-python) * [License](#license) * [Citations](#citations) ## Dataset Summary The **LinTO DataSet Audio for Arabic Tunisian Augmented** is a dataset that builds on [**LinTO DataSet Audio for Arabic Tunisian**](https://huggingface.co/datasets/linagora/linto-dataset-audio-ar-tn), using a subset of the original audio data. Augmentation techniques, including noise reduction and SoftVC VITS Singing Voice Conversion (SVC), have been applied to enhance the dataset for improved performance in Arabic Tunisian Automatic Speech Recognition (ASR) tasks. ## Dataset Composition: The **LinTO DataSet Audio for Arabic Tunisian Augmented** comprises a diverse range of augmented audio samples using different techniques. Below is a breakdown of the dataset’s composition: ### Sources | **subset** | **audio duration** | **labeled audio duration** | **# audios** | **# segments** | **# words** | **# characters** | | --- | --- | --- | --- | --- | --- | --- | | ApprendreLeTunisienVCA | 2h 40m 6s | 2h 40m 6s | 6146 | 6146 | 8078 | 36687 | | MASC_NoiseLess | 2h 49m 56s | 1h 38m 17s | 48 | 1742 | 11909 | 59876 | | MASC_NoiseLess_VCA | 19h 49m 31s | 11h 27m 59s | 336 | 12194 | 83377 | 411999 | | OneStoryVCA | 9h 16m 51s | 9h 7m 32s | 216 | 2964 | 73962 | 341670 | | TunSwitchCS_VCA | 59h 39m 10s | 59h 39m 10s | 37639 | 37639 | 531727 | 2760268 | | TunSwitchTO_VCA | 18h 57m 34s | 18h 57m 34s | 15365 | 15365 | 129304 | 659295 | | Youtube_AbdelAzizErwi_VCA | 122h 51m 1s | 109h 32m 39s | 125 | 109700 | 657720 | 3117170 | | Youtube_BayariBilionaireVCA | 4h 54m 8s | 4h 35m 25s | 30 | 5400 | 39065 | 199155 | | Youtube_DiwanFM_VCA | 38h 10m 6s | 28h 18m 58s | 252 | 32690 | 212170 | 1066464 | | Youtube_HkeyetTounsiaMensia_VCA | 12h 13m 29s | 9h 53m 22s | 35 | 10626 | 73696 | 360990 | | Youtube_LobnaMajjedi_VCA | 6h 41m 38s | 6h 12m 31s | 14 | 6202 | 42938 | 211512 | | Youtube_MohamedKhammessi_VCA | 12h 7m 7s | 10h 58m 21s | 14 | 12775 | 92512 | 448987 | | Youtube_Shorts_VCA | 26h 26m 25s | 23h 45m 25s | 945 | 14154 | 201138 | 1021713 | | Youtube_TNScrapped_V1_NoiseLess | 4h 2m 9s | 2h 31m 05s | 52 | 2538 | 18777 | 92530 | | Youtube_TNScrapped_V1_NoiseLess_VCA | 28h 15m 1s | 17h 37m 36s | 364 | 17766 | 132587 | 642292 | | Youtube_TV_VCA | 4h 16m 16s | 3h 40m 56s | 28 | 4676 | 33376 | 311500 | | **TOTAL** | **373h 10m 28s** | **320h 36m 58s** | **61609** | **292257** | **2342336** | **11742108** | ### Data Proccessing: - **Noise Reduction**: Applying techniques to minimize background noise and enhance audio clarity for better model performance. For this, we used **Deezer [Spleeter](https://github.com/deezer/spleeter)**, a library with pretrained models, to separate vocals from music. - **Voice Conversion**: Modifying speaker characteristics (e.g., pitch) through voice conversion techniques to simulate diverse speaker profiles and enrich the dataset. For this, we chose **SoftVC VITS Singing Voice Conversion** ([SVC](https://github.com/voicepaw/so-vits-svc-fork)) to alter the original voices using 7 different pretrained models. The image below shows the difference between the original and the augmented audio:  - The first row shows the original waveform. - The second row shows the audio after noise reduction. - The last row shows the audio with voice conversion augmentation. ### Content Types - **FootBall**: Includes recordings of football news and reviews. - **Documentaries**: Audio from documentaries about history and nature. - **Podcasts**: Conversations and discussions from various podcast episodes. - **Authors**: Audio recordings of authors reading or discussing different stories: horror, children's literature, life lessons, and others. - **Lessons**: Learning resources for the Tunisian dialect. - **Others**: Mixed recordings with various subjects. ### Languages and Dialects - **Tunisian Arabic**: The primary focus of the dataset, including Tunisian Arabic and some Modern Standard Arabic (MSA). - **French**: Some instances of French code-switching. - **English**: Some instances of English code-switching. ### Characteristics - **Audio Duration**: The dataset contains more than 317 hours of audio recordings. - **Segments Duration**: This dataset contains segments, each with a duration of less than 30 seconds. - **Labeled Data**: Includes annotations and transcriptions for a significant portion of the audio content. ### Data Distribution - **Training Set**: Includes a diverse range of augmented audio with 5 to 7 different voices, as well as noise reduction applied to two datasets. ## Example use (python) - **Load the dataset in python**: ```python from datasets import load_dataset # dataset will be loaded as a DatasetDict of train and test dataset = load_dataset("linagora/linto-dataset-audio-ar-tn-augmented") ``` Check the containt of dataset: ```python example = dataset['train'][0] audio_array = example['audio']["array"] segments = example['segments'] transcription = example['transcript'] print(f"Audio array: {audio_array}") print(f"Segments: {segments}") print(f"Transcription: {transcription}") ``` **Example** ```bash Audio array: [0. 0. 0. ... 0. 0. 0.] Transcription: أسبقية قبل أنا ما وصلت خممت فيه كيما باش نحكيو من بعد إلا ما أنا كإنطريبرنور كباعث مشروع صارولي برشا مشاكل فالجستين و صارولي مشاكل مع لعباد لي كانت موفرتلي اللوجسيل ولا اللوجسيل أوف لنيه ولا لوجسيل بيراتي segments: [{'end': 14.113, 'start': 0.0, 'transcript': 'أسبقية قبل أنا ما وصلت خممت فيه كيما باش نحكيو من بعد إلا ما أنا كإنطريبرنور كباعث مشروع صارولي برشا مشاكل فالجستين و صارولي مشاكل مع لعباد لي كانت موفرتلي اللوجسيل ولا اللوجسيل أوف لنيه ولا لوجسيل بيراتي'}] ``` ## License Given that some of the corpora used for training and evaluation are available only under CC-BY-4.0 licenses, we have chosen to license the entire dataset under CC-BY-4.0. ## Citations When using the **LinTO DataSet Audio for Arabic Tunisian** corpus, please cite this page: ```bibtex @misc{linagora2024Linto-tn, author = {Hedi Naouara and Jérôme Louradour and Jean-Pierre Lorré}, title = {LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect}, year = {2024}, month = {October}, note = {Good Data Workshop, AAAI 2025}, howpublished = {\url{https://huggingface.co/linagora/linto-asr-ar-tn-0.1}}, } ``` ```bibtex @misc{abdallah2023leveraging, title={Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition}, author={Ahmed Amine Ben Abdallah and Ata Kabboudi and Amir Kanoun and Salah Zaiem}, year={2023}, eprint={2309.11327}, archivePrefix={arXiv}, primaryClass={eess.AS} } ``` ```bibtex @data{e1qb-jv46-21, doi = {10.21227/e1qb-jv46}, url = {https://dx.doi.org/10.21227/e1qb-jv46}, author = {Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha}, publisher = {IEEE Dataport}, title = {MASC: Massive Arabic Speech Corpus}, year = {2021} } ```

amitness/logits-mt-it-en-128

amitness

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: teacher_logits sequence: sequence: float64 - name: teacher_indices sequence: sequence: int64 - name: teacher_mask_indices sequence: int64 splits: - name: train num_bytes: 184467361976 num_examples: 40721350 - name: test num_bytes: 32556394204 num_examples: 7186121 download_size: 0 dataset_size: 217023756180 --- # Dataset Card for "logits-mt-it-en-128" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sal4ahm/RealCQA

sal4ahm

--- license: mit --- # RealCQA: Real-World Complex Question Answering Dataset This repository contains the dataset used in the paper "[RealCQA: Scientific Chart Question Answering as a Test-Bed for First-Order Logic](https://arxiv.org/pdf/2308.01979)" (ICDAR 2023). The dataset is designed to facilitate research in complex question answering, involving a diverse set of real-world images and associated textual question-answer pairs. ## Dataset Overview The RealCQA dataset consists of 28,266 images, and corresponding 2 million question-answer pairs organized into three complementary subsets. Each image is accompanied by a JSON file containing one or more question blocks. The dataset is structured to address a range of question-answering tasks that require an understanding of the visual content. ### Dataset Structure The dataset is organized into the following folders: - **Images** - `images`: Contains the first 10,000 images. - `images2`: Contains the next 10,000 images. - `images3`: Contains the remaining 8,266 images. - **JSON Files** - `jsons`: Contains the JSON files corresponding to the images in the `images` folder. - `jsons2`: Contains the JSON files corresponding to the images in the `images2` folder. - `jsons3`: Contains the JSON files corresponding to the images in the `images3` folder. - **QA Files** These are the QA created in our proposed dataset. - `qa`: Contains the QA files corresponding to the images in the `images` folder. - `qa2`: Contains the QA files corresponding to the images in the `images2` folder. - `qa3`: Contains the QA files corresponding to the images in the `images3` folder. ### File Details - **Images**: JPEG files named in the format `PMCxxxxxx_abc.jpg`, where `xxxxxx` represents the PubMed Central ID and `abc` represents an identifier specific to the image. - **JSON Files**: JSON files named in the same format as the images. These are groundtruth annotations from the https://chartinfo.github.io challenge, they provide annotations for chart type, text(OCR), text location, text type (axis/tick/legend), data used to plot the chart. - **QA Files**: QA files named in the same format as the images. Each QA file is a list of question blocks associated with the corresponding image we created in our proposed dataset. #### QA Structure Each QA file contains a list of question blocks in the following format: ```json [ { "taxonomy id": "2j", "QID": "16", "question": "Are all the bars in the chart visually horizontal?", "answer": "no", "answer_type": "Binary", "qa_id": "XbUzFtjqsEOF", "PMC_ID": "PMC8439477___g003" }, { "taxonomy id": "1a", "QID": "7a", "question": "What is the type of chart?", "answer": "Vertical Bar chart", "answer_type": "String", "qa_id": "wzcdDijkrHtt", "PMC_ID": "PMC8439477___g003" } ] ``` ### Dataset Loader To facilitate loading and using the dataset, we provide a custom dataset loader script, `dataset.py`. This script defines a PyTorch `Dataset` class to handle loading, preprocessing, and batching of the images and question-answer pairs. #### How to Use the Dataset Loader 1. **Setup and Requirements** Ensure you have the following Python packages installed: ```bash pip install torch torchvision Pillow ``` 2. **Dataset Loader Script** Use the provided `dataset.py` to load the dataset. The script is designed to load the dataset efficiently and handle both training and testing cases. ```python from dataset import RQADataset from torch.utils.data import DataLoader dataset = RQADataset(data_dir='.', split='train') # split='test' for RQA9357 split used in the paper # Test loading a single item print(f"Number of samples in dataset: {len(dataset)}") sample = dataset[0] print("Sample data:", sample) # Initialize DataLoader dataloader = DataLoader(dataset, batch_size=4, collate_fn=RQADataset.custom_collate) # Test DataLoader for batch in dataloader: print("Batch data:", batch) break # Load only one batch for testing ``` ### Citation If you use this dataset in your research, please cite the following paper: ```bibtex @InProceedings{10.1007/978-3-031-41682-8_5, author="Ahmed, Saleem and Jawade, Bhavin and Pandey, Shubham and Setlur, Srirangaraj and Govindaraju, Venu", editor="Fink, Gernot A. and Jain, Rajiv and Kise, Koichi and Zanibbi, Richard", title="RealCQA: Scientific Chart Question Answering as a Test-Bed for First-Order Logic", booktitle="Document Analysis and Recognition - ICDAR 2023", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="66--83", abstract="We present a comprehensive study of chart visual question-answering(QA) task, to address the challenges faced in comprehending and extracting data from chart visualizations within documents. Despite efforts to tackle this problem using synthetic charts, solutions are limited by the shortage of annotated real-world data. To fill this gap, we introduce a benchmark and dataset for chart visual QA on real-world charts, offering a systematic analysis of the task and a novel taxonomy for template-based chart question creation. Our contribution includes the introduction of a new answer type, `list', with both ranked and unranked variations. Our study is conducted on a real-world chart dataset from scientific literature, showcasing higher visual complexity compared to other works. Our focus is on template-based QA and how it can serve as a standard for evaluating the first-order logic capabilities of models. The results of our experiments, conducted on a real-world out-of-distribution dataset, provide a robust evaluation of large-scale pre-trained models and advance the field of chart visual QA and formal logic verification for neural networks in general. Our code and dataset is publicly available (https://github.com/cse-ai-lab/RealCQA).", isbn="978-3-031-41682-8" } } ``` ### License This dataset is licensed under the [MIT License](LICENSE). By using this dataset, you agree to abide by its terms and conditions. ### Contact For any questions or issues, please contact the authors of the paper or open an issue in this repository.

allenai/s2-naip

allenai

--- license: apache-2.0 --- AI2-S2-NAIP is a remote sensing dataset consisting of aligned NAIP, Sentinel-2, Sentinel-1, and Landsat images spanning the entire continental US. Data is divided into tiles. Each tile spans 512x512 pixels at 1.25 m/pixel in one of the 10 UTM projections covering the continental US. At each tile, the following data is available: - [National Agriculture Imagery Program (NAIP)](https://www.usgs.gov/centers/eros/science/usgs-eros-archive-aerial-photography-national-agriculture-imagery-program-naip): an image from 2019-2021 at 1.25 m/pixel (512x512). - [Sentinel-2 (L1C)](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2): between 16 and 32 images captured within a few months of the NAIP image at 10 m/pixel (64x64). - [Sentinel-1](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1): between 2 and 8 images captured within a few months of the NAIP image at 10 m/pixel (64x64). - [Landsat-8/9](https://www.usgs.gov/landsat-missions/landsat-8): 4 images captured in the same year as the NAIP image at 10 m/pixel (64x64). - [OpenStreetMap](https://www.openstreetmap.org): a GeoJSON containing buildings, roads, and 30 other categories. It uses pixel coordinates relative to the 512x512 NAIP image. - [WorldCover](https://worldcover2021.esa.int/): the 2021 land cover image at 10 m/pixel (64x64). AI2-S2-NAIP is applicable to several supervised and unsupervised tasks in remote sensing, including super-resolution (e.g. NAIP -> Sentinel-2), segmentation and detection (e.g. NAIP or Sentinel-2 -> OpenStreetMap or WorldCover), and multi-modal masked autoencoder pre-training. For questions or feedback about AI2-S2-NAIP, please open an issue on Github at https://github.com/allenai/satlas.  Structure --------- Once extracted, the dataset contains the different data types in different folders. Each folder contains files named by a tile ID, which consists of the UTM projection, column, and row. The column and row are based on tiles that are 512x512 pixels with pixel coordinates at 1.25 m/pixel, e.g. `32612_960_-6049.png` spans (614400, -3871360) to (615040, -3870720) in EPSG:32612 projection units. Here is an example of NAIP data: ``` naip/ 32612_960_-6049.png 32612_960_-6050.png 32612_960_-6051.png ... ``` And an example of Sentinel-2 data: ``` sentinel2/ 32612_960_-6049_16.tif 32612_960_-6049_32.tif 32612_960_-6049_8.tif 32612_960_-6050_16.tif ... ``` The Sentinel-2, Sentinel-1, and Landsat images are GeoTIFFS so they contain georeference metadata. Other data does not have georeference metadata, but data at each tile is aligned, so the georeference metadata from the above images is applicable to the other data as well with only a resolution shift. Mapping Longitude and Latitude to Tile -------------------------------------- Here is an example of mapping longitude and latitude to a tile. First install packages: pip install rasterio shapely utm Then launch Python shell: from rasterio.crs import CRS from rasterio.warp import transform_geom import shapely import utm # Define source location. src_crs = CRS.from_epsg(4326) src_point = shapely.Point(-122.331711, 47.648450) # Get UTM zone. _, _, zone_suffix, _ = utm.from_latlon(src_point.y, src_point.x) epsg_code = 32600 + zone_suffix dst_crs = CRS.from_epsg(epsg_code) # Transform to UTM CRS. dst_point = transform_geom(src_crs, dst_crs, src_point) dst_point = shapely.geometry.shape(dst_point) # dst_point is in projection coordinates (meters). # Now convert to pixel coordinates at 1.25 m/pixel. col = int(dst_point.x/1.25) row = int(dst_point.y/-1.25) # Print the prefix for the image filenames. print(f"{epsg_code}_{col//512}_{row//512}") # Print the prefix for the tar filenames to know which one to download. # These group together many 1.25 m/pixel 512x512 tiles into one tar file. print(f"{epsg_code}_{col//512//32}_{row//512//32}") So then you would download the tar file from the second prefix, extract it, and look at the file with name matching the first prefix. See visualize_tile.py for example of visualizing the data at a particular tile. Sentinel-2 ---------- The 10 m/pixel (`_8.tif`), 20 m/pixel (`_16.tif`), and 60 m/pixel (`_32.tif`) bands are stored separately. Pixel values are the L1C 16-bit values. The band order is as follows: - _8.tif (64x64): B02, B03, B04, B08 - _16.tif (32x32): B05, B06, B07, B8A, B11, B12 - _32.tif (16x16): B01, B09, B10 The GeoTIFFs contain multiple images concatenated along the channel axis. The CSV shows the original Sentinel-2 scene ID of each image. Sentinel-1 ---------- The Sentinel-1 bands are 10 m/pixel and ordered VV then VH. Only IW VV+VH scenes are used. The pixel values are 32-bit floating point values representing decibels 10*log10(x). We obtain the radiometric-calibrated and terrain-corrected images from Google Earth Engine so see https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD for details. The GeoTIFFs contain multiple images concatenated along the channel axis. The CSV shows the original Sentinel-1 scene ID of each image. NAIP ---- The NAIP image is 512x512 with four 8-bit bands: R, G, B, IR. It is encoded as PNG but the IR is alpha mask so cannot be visualized correctly in image viewer without removing the alpha mask. There are two NAIP images available, one under "naip" (2019-2022) and one under "oldnaip" (2015-2018). The CSV shows the original NAIP scene ID of each image. Landsat ------- We include OLI-TIRS images from Landsat-8 and Landsat-9. As with Sentinel-2, we select Landsat images that were captured within a few months of the NAIP image. We store the 15 m/pixel bands (i.e. B8) at 10 m/pixel, and the 30 m/pixel bands (all the others) at 20 m/pixel. There are separate GeoTIFFs for the 10 m/pixel (`_8.tif`) and 20 m/pixel (`_16.tif`). All pixel values are 16-bit. The band order is as follows: - _8.tif (64x64): B8 - _16.tif (32x32): B1, B2, B3, B4, B5, B6, B7, B9, B10, B11 The GeoTIFFS contain multiple images concatenated along the channel axis. The CSV shows the original Landsat scene ID of each image.

rethinklab/Bench2Drive

rethinklab

--- license: apache-2.0 viewer: false --- # **Bench2Drive**: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving. ## Description Bench2Drive is a benchmark designed for evaluating end-to-end autonomous driving algorithms in the closed-loop manner. It features: - **Comprehensive Scenario Coverage**: Bench2Drive is designed to test AD systems across 44 interactive scenarios, ensuring a thorough evaluation of an AD system's capability to handle real-world driving challenges. - **Granular Skill Assessment**: By structuring the evaluation across 220 short routes, each focusing on a specific driving scenario, Bench2Drive allows for detailed analysis and comparison of how different AD systems perform on individual tasks. - **Closed-Loop Evaluation Protocol**: Bench2Drive evaluates AD systems in a closed-loop manner, where the AD system's actions directly influence the environment. This setup offers an accurate assessment of AD systems' driving performance. - **Diverse Large-Scale Official Training Data**: Bench2Drive consists of a standardized training set of 10000 fully annotated clips under diverse scenarios, weathers, and towns, ensuring that all AD systems are trained under abundant yet similar conditions, which is crucial for fair algorithm-level comparisons. **Each clip named by: ScenarioName_TownID_RouteID_WeatherID.tar.gz.** For HD-map, please refer to https://huggingface.co/datasets/rethinklab/Bench2Drive-Map. For full set, please refer to https://huggingface.co/datasets/rethinklab/Bench2Drive-Full. For more information, please visit our GitHub repository: https://github.com/Thinklab-SJTU/Bench2Drive. ## License and Citation All assets and code are under the Apache 2.0 license unless specified otherwise. ```bibtex @article{jia2024bench, title={Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving}, author={Xiaosong Jia and Zhenjie Yang and Qifeng Li and Zhiyuan Zhang and Jiazi Bu and Junchi Yan}, journal={\url{https://github.com/Thinklab-SJTU/Bench2Drive}}, year={2024} } ```

skt/kobest_v1

skt

--- pretty_name: KoBEST annotations_creators: - expert-generated language_creators: - expert-generated language: - ko license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original configs: - config_name: boolq data_files: - split: train path: "boolq/train.jsonl" - split: test path: "boolq/test.jsonl" - split: validation path: "boolq/validation.jsonl" - config_name: copa data_files: - split: train path: "copa/train.jsonl" - split: test path: "copa/test.jsonl" - split: validation path: "copa/validation.jsonl" - config_name: hellaswag data_files: - split: train path: "hellaswag/train.jsonl" - split: test path: "hellaswag/test.jsonl" - split: validation path: "hellaswag/validation.jsonl" - config_name: sentineg data_files: - split: train path: "sentineg/train.jsonl" - split: test path: "sentineg/test.jsonl" - split: test_originated path: "sentineg/test_originated.jsonl" - split: validation path: "sentineg/validation.jsonl" - config_name: wic data_files: - split: train path: "wic/train.jsonl" - split: test path: "wic/test.jsonl" - split: validation path: "wic/validation.jsonl" --- # Dataset Card for KoBEST ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/SKT-LSL/KoBEST_datarepo - **Paper:** - **Point of Contact:** https://github.com/SKT-LSL/KoBEST_datarepo/issues ### Dataset Summary KoBEST is a Korean benchmark suite consists of 5 natural language understanding tasks that requires advanced knowledge in Korean. ### Supported Tasks and Leaderboards Boolean Question Answering, Choice of Plausible Alternatives, Words-in-Context, HellaSwag, Sentiment Negation Recognition ### Languages `ko-KR` ## Dataset Structure ### Data Instances #### KB-BoolQ An example of a data point looks as follows. ``` {'paragraph': '두아 리파(Dua Lipa, 1995년 8월 22일 ~ )는 잉글랜드의 싱어송라이터, 모델이다. BBC 사운드 오브 2016 명단에 노미닛되었다. 싱글 "Be the One"가 영국 싱글 차트 9위까지 오르는 등 성과를 보여주었다.', 'question': '두아 리파는 영국인인가?', 'label': 1} ``` #### KB-COPA An example of a data point looks as follows. ``` {'premise': '물을 오래 끓였다.', 'question': '결과', 'alternative_1': '물의 양이 늘어났다.', 'alternative_2': '물의 양이 줄어들었다.', 'label': 1} ``` #### KB-WiC An example of a data point looks as follows. ``` {'word': '양분', 'context_1': '토양에 [양분]이 풍부하여 나무가 잘 자란다. ', 'context_2': '태아는 모체로부터 [양분]과 산소를 공급받게 된다.', 'label': 1} ``` #### KB-HellaSwag An example of a data point looks as follows. ``` {'context': '모자를 쓴 투수가 타자에게 온 힘을 다해 공을 던진다. 공이 타자에게 빠른 속도로 다가온다. 타자가 공을 배트로 친다. 배트에서 깡 소리가 난다. 공이 하늘 위로 날아간다.', 'ending_1': '외야수가 떨어지는 공을 글러브로 잡는다.', 'ending_2': '외야수가 공이 떨어질 위치에 자리를 잡는다.', 'ending_3': '심판이 아웃을 외친다.', 'ending_4': '외야수가 공을 따라 뛰기 시작한다.', 'label': 3} ``` #### KB-SentiNeg An example of a data point looks as follows. ``` {'sentence': '택배사 정말 마음에 듬', 'label': 1} ``` ### Data Fields ### KB-BoolQ + `paragraph`: a `string` feature + `question`: a `string` feature + `label`: a classification label, with possible values `False`(0) and `True`(1) ### KB-COPA + `premise`: a `string` feature + `question`: a `string` feature + `alternative_1`: a `string` feature + `alternative_2`: a `string` feature + `label`: an answer candidate label, with possible values `alternative_1`(0) and `alternative_2`(1) ### KB-WiC + `target_word`: a `string` feature + `context_1`: a `string` feature + `context_2`: a `string` feature + `label`: a classification label, with possible values `False`(0) and `True`(1) ### KB-HellaSwag + `target_word`: a `string` feature + `context_1`: a `string` feature + `context_2`: a `string` feature + `label`: a classification label, with possible values `False`(0) and `True`(1) ### KB-SentiNeg + `sentence`: a `string` feature + `label`: a classification label, with possible values `Negative`(0) and `Positive`(1) ### Data Splits #### KB-BoolQ + train: 3,665 + dev: 700 + test: 1,404 #### KB-COPA + train: 3,076 + dev: 1,000 + test: 1,000 #### KB-WiC + train: 3,318 + dev: 1,260 + test: 1,260 #### KB-HellaSwag + train: 3,665 + dev: 700 + test: 1,404 #### KB-SentiNeg + train: 3,649 + dev: 400 + test: 397 + test_originated: 397 (Corresponding training data where the test set is originated from.) ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information ``` @misc{https://doi.org/10.48550/arxiv.2204.04541, doi = {10.48550/ARXIV.2204.04541}, url = {https://arxiv.org/abs/2204.04541}, author = {Kim, Dohyeong and Jang, Myeongjun and Kwon, Deuk Sin and Davis, Eric}, title = {KOBEST: Korean Balanced Evaluation of Significant Tasks}, publisher = {arXiv}, year = {2022}, } ``` [More Information Needed] ### Contributions Thanks to [@MJ-Jang](https://github.com/MJ-Jang) for adding this dataset.

mozilla-foundation/common_voice_13_0

mozilla-foundation

--- pretty_name: Common Voice Corpus 13.0 annotations_creators: - crowdsourced language_creators: - crowdsourced language_bcp47: - ab - ar - as - ast - az - ba - bas - be - bg - bn - br - ca - ckb - cnh - cs - cv - cy - da - de - dv - dyu - el - en - eo - es - et - eu - fa - fi - fr - fy-NL - ga-IE - gl - gn - ha - hi - hsb - hu - hy-AM - ia - id - ig - is - it - ja - ka - kab - kk - kmr - ko - ky - lg - lo - lt - lv - mdf - mhr - mk - ml - mn - mr - mrj - mt - myv - nan-tw - ne-NP - nl - nn-NO - oc - or - pa-IN - pl - pt - quy - rm-sursilv - rm-vallader - ro - ru - rw - sah - sat - sc - sk - skr - sl - sr - sv-SE - sw - ta - th - ti - tig - tk - tok - tr - tt - tw - ug - uk - ur - uz - vi - vot - yo - yue - zh-CN - zh-HK - zh-TW license: - cc0-1.0 multilinguality: - multilingual size_categories: ab: - 10K<n<100K ar: - 100K<n<1M as: - 1K<n<10K ast: - 1K<n<10K az: - n<1K ba: - 100K<n<1M bas: - 1K<n<10K be: - 1M<n<10M bg: - 10K<n<100K bn: - 1M<n<10M br: - 10K<n<100K ca: - 1M<n<10M ckb: - 100K<n<1M cnh: - 1K<n<10K cs: - 100K<n<1M cv: - 10K<n<100K cy: - 100K<n<1M da: - 10K<n<100K de: - 100K<n<1M dv: - 10K<n<100K dyu: - n<1K el: - 10K<n<100K en: - 1M<n<10M eo: - 1M<n<10M es: - 1M<n<10M et: - 10K<n<100K eu: - 100K<n<1M fa: - 100K<n<1M fi: - 10K<n<100K fr: - 100K<n<1M fy-NL: - 100K<n<1M ga-IE: - 10K<n<100K gl: - 10K<n<100K gn: - 1K<n<10K ha: - 10K<n<100K hi: - 10K<n<100K hsb: - 1K<n<10K hu: - 10K<n<100K hy-AM: - 1K<n<10K ia: - 10K<n<100K id: - 10K<n<100K ig: - 1K<n<10K is: - n<1K it: - 100K<n<1M ja: - 100K<n<1M ka: - 10K<n<100K kab: - 100K<n<1M kk: - 1K<n<10K kmr: - 10K<n<100K ko: - 1K<n<10K ky: - 10K<n<100K lg: - 100K<n<1M lo: - n<1K lt: - 10K<n<100K lv: - 10K<n<100K mdf: - n<1K mhr: - 100K<n<1M mk: - n<1K ml: - 1K<n<10K mn: - 10K<n<100K mr: - 10K<n<100K mrj: - 10K<n<100K mt: - 10K<n<100K myv: - 1K<n<10K nan-tw: - 10K<n<100K ne-NP: - n<1K nl: - 10K<n<100K nn-NO: - n<1K oc: - 1K<n<10K or: - 1K<n<10K pa-IN: - 1K<n<10K pl: - 100K<n<1M pt: - 100K<n<1M quy: - n<1K rm-sursilv: - 1K<n<10K rm-vallader: - 1K<n<10K ro: - 10K<n<100K ru: - 100K<n<1M rw: - 1M<n<10M sah: - 1K<n<10K sat: - n<1K sc: - 1K<n<10K sk: - 10K<n<100K skr: - 1K<n<10K sl: - 10K<n<100K sr: - 1K<n<10K sv-SE: - 10K<n<100K sw: - 100K<n<1M ta: - 100K<n<1M th: - 100K<n<1M ti: - n<1K tig: - n<1K tk: - 1K<n<10K tok: - 10K<n<100K tr: - 10K<n<100K tt: - 10K<n<100K tw: - n<1K ug: - 10K<n<100K uk: - 10K<n<100K ur: - 100K<n<1M uz: - 100K<n<1M vi: - 10K<n<100K vot: - n<1K yo: - 1K<n<10K yue: - 10K<n<100K zh-CN: - 100K<n<1M zh-HK: - 100K<n<1M zh-TW: - 100K<n<1M source_datasets: - extended|common_voice task_categories: - automatic-speech-recognition paperswithcode_id: common-voice extra_gated_prompt: "By clicking on “Access repository” below, you also agree to not attempt to determine the identity of speakers in the Common Voice dataset." --- # Dataset Card for Common Voice Corpus 13.0 ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://arxiv.org/abs/1912.06670 - **Leaderboard:** https://paperswithcode.com/dataset/common-voice - **Point of Contact:** [Vaibhav Srivastav](mailto:[email protected]) ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 27141 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 17689 validated hours in 108 languages, but more voices and languages are always added. Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing. ### Supported Tasks and Leaderboards The results for models trained on the Common Voice datasets are available via the [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=mozilla-foundation%2Fcommon_voice_11_0&only_verified=0&task=automatic-speech-recognition&config=ar&split=test&metric=wer) ### Languages ``` Abkhaz, Arabic, Armenian, Assamese, Asturian, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Bengali, Breton, Bulgarian, Cantonese, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dioula, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hill Mari, Hindi, Hungarian, Icelandic, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Korean, Kurmanji Kurdish, Kyrgyz, Lao, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Meadow Mari, Moksha, Mongolian, Nepali, Norwegian Nynorsk, Occitan, Odia, Persian, Polish, Portuguese, Punjabi, Quechua Chanka, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Saraiki, Sardinian, Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Taiwanese (Minnan), Tamil, Tatar, Thai, Tigre, Tigrinya, Toki Pona, Turkish, Turkmen, Twi, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh, Yoruba ``` ## How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi): ```python from datasets import load_dataset cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train", streaming=True) print(next(iter(cv_13))) ``` *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). ### Local ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train") batch_sampler = BatchSampler(RandomSampler(cv_13), batch_size=32, drop_last=False) dataloader = DataLoader(cv_13, batch_sampler=batch_sampler) ``` ### Streaming ```python from datasets import load_dataset from torch.utils.data import DataLoader cv_13 = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="train") dataloader = DataLoader(cv_13, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 13 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). ## Dataset Structure ### Data Instances A typical data point comprises the `path` to the audio file and its `sentence`. Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`. ```python { 'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5', 'path': 'et/clips/common_voice_et_18318995.mp3', 'audio': { 'path': 'et/clips/common_voice_et_18318995.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000 }, 'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.', 'up_votes': 2, 'down_votes': 0, 'age': 'twenties', 'gender': 'male', 'accent': '', 'locale': 'et', 'segment': '' } ``` ### Data Fields `client_id` (`string`): An id for which client (voice) made the recording `path` (`string`): The path to the audio file `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. `sentence` (`string`): The sentence the user was prompted to speak `up_votes` (`int64`): How many upvotes the audio file has received from reviewers `down_votes` (`int64`): How many downvotes the audio file has received from reviewers `age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`) `gender` (`string`): The gender of the speaker `accent` (`string`): Accent of the speaker `locale` (`string`): The locale of the speaker `segment` (`string`): Usually an empty field ### Data Splits The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other. The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality. The invalidated data is data has been invalidated by reviewers and received downvotes indicating that the data is of low quality. The reported data is data that has been reported, for different reasons. The other data is data that has not yet been reviewed. The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Data Preprocessing Recommended by Hugging Face The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice. Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_. In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation. ```python from datasets import load_dataset ds = load_dataset("mozilla-foundation/common_voice_13_0", "en", use_auth_token=True) def prepare_dataset(batch): """Function to preprocess the dataset with the .map method""" transcription = batch["sentence"] if transcription.startswith('"') and transcription.endswith('"'): # we can remove trailing quotation marks as they do not affect the transcription transcription = transcription[1:-1] if transcription[-1] not in [".", "?", "!"]: # append a full-stop to sentences that do not end in punctuation transcription = transcription + "." batch["sentence"] = transcription return batch ds = ds.map(prepare_dataset, desc="preprocess dataset") ``` ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ```

NbAiLab/NCC

NbAiLab

--- YAML tags: annotations_creators: - no-annotation language_creators: - found language: - en - nb - no - nn - sv - da - is - fo license: - other multilinguality: - multilingual pretty_name: NCC size_categories: - 2G<n<1B source_datasets: - original task_categories: - text-generation task_ids: - language-modeling extra_gated_prompt: "The Directive on Copyright in the Digital Single Market, which came into force on June 6 2019, amends the European Union copyright and database legislation and allows for Text and Data Mining (TDM) activities for research organizations and cultural heritage institutions. Under the terms of the aforementioned directive, by clicking on 'Access repository' you agree on using the text and data contained in this dataset for non-commercial scientific purposes only." --- # Dataset Card for NbAiLab/NCC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Data Fields](#data-fiels) - [Dataset Creation](#dataset-creation) - [Statistics](#statistics) - [Document Types](#document-types) - [Languages](#languages) - [Publish Periode](#publish-periode) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://github.com/NbAiLab/notram - **Repository:** https://github.com/NbAiLab/notram - **Paper:** https://arxiv.org/abs/2104.09617 - **Point of Contact:** [Freddy Wetjen](mailto:[email protected]) The Norwegian Colossal Corpus is a collection of multiple smaller Norwegian corpuses suitable for training large language models. We have done extensive cleaning on the datasets, and have made them available in a common format. The total size of the NCC is currently 45GB. ## How to Use ```python from datasets import load_dataset data = load_dataset("NbAiLab/NCC", streaming=True) ``` ## Download Data If you do not want to use the HuggingFace Dataset-library for training, or if you want to do additional pre-processing, it is also possible to download the files locally. ```bash # Clone the training set git clone https://huggingface.co/datasets/NbAiLab/NCC # Create one large training file of all shards without unpacking cat NCC/data/train*.gz > onefile.json.gz ``` <details> <summary>List of all the files.</summary> * [train-shard-0001-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0001-of-0046.json.gz) * [train-shard-0002-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0002-of-0046.json.gz) * [train-shard-0003-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0003-of-0046.json.gz) * [train-shard-0004-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0004-of-0046.json.gz) * [train-shard-0005-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0005-of-0046.json.gz) * [train-shard-0006-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0006-of-0046.json.gz) * [train-shard-0007-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0007-of-0046.json.gz) * [train-shard-0008-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0008-of-0046.json.gz) * [train-shard-0009-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0009-of-0046.json.gz) * [train-shard-0010-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0010-of-0046.json.gz) * [train-shard-0011-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0011-of-0046.json.gz) * [train-shard-0012-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0012-of-0046.json.gz) * [train-shard-0013-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0013-of-0046.json.gz) * [train-shard-0014-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0014-of-0046.json.gz) * [train-shard-0015-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0015-of-0046.json.gz) * [train-shard-0016-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0016-of-0046.json.gz) * [train-shard-0017-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0017-of-0046.json.gz) * [train-shard-0018-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0018-of-0046.json.gz) * [train-shard-0019-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0019-of-0046.json.gz) * [train-shard-0020-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0020-of-0046.json.gz) * [train-shard-0021-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0021-of-0046.json.gz) * [train-shard-0022-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0022-of-0046.json.gz) * [train-shard-0023-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0023-of-0046.json.gz) * [train-shard-0024-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0024-of-0046.json.gz) * [train-shard-0025-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0025-of-0046.json.gz) * [train-shard-0026-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0026-of-0046.json.gz) * [train-shard-0027-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0027-of-0046.json.gz) * [train-shard-0028-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0028-of-0046.json.gz) * [train-shard-0029-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0029-of-0046.json.gz) * [train-shard-0030-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0030-of-0046.json.gz) * [train-shard-0031-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0031-of-0046.json.gz) * [train-shard-0032-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0032-of-0046.json.gz) * [train-shard-0033-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0033-of-0046.json.gz) * [train-shard-0034-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0034-of-0046.json.gz) * [train-shard-0035-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0035-of-0046.json.gz) * [train-shard-0036-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0036-of-0046.json.gz) * [train-shard-0037-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0037-of-0046.json.gz) * [train-shard-0038-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0038-of-0046.json.gz) * [train-shard-0039-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0039-of-0046.json.gz) * [train-shard-0040-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0040-of-0046.json.gz) * [train-shard-0041-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0041-of-0046.json.gz) * [train-shard-0042-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0042-of-0046.json.gz) * [train-shard-0043-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0043-of-0046.json.gz) * [train-shard-0044-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0044-of-0046.json.gz) * [train-shard-0045-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0045-of-0046.json.gz) * [train-shard-0046-of-0046](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/train-shard-0046-of-0046.json.gz) * [validation-shard-0001-of-0001](https://huggingface.co/datasets/NbAiLab/NCC/resolve/main/data/validation-shard-0001-of-0001.json.gz) </details> ### Dataset Summary The NCC dataset contains json lines with language training data. Here is an example json line: ```json { "id": "1006205", "doc_type": "cc100", "publish_year": 2021, "lang_fasttext": "nn", "lang_fasttext_conf": "0.641", "text": "Eg har ein PLAN! KOS deg og ha ei fin helg" } ``` ## Data Fields |**id:** | String with id to source of line and a unique identifier| |:-----------|:------------| |**doc_type** | String describing type of media text extracted from (I.e. book,newspaper etc)| |**publish_year** | Integer. The year text published. When year is undetermined it is set to 2021.| |**lang_fasttext** | String. Language of text identified by FastText| |**lang_fasttext_conf** | String. Confidence calculated by FastText| |**text** | String. The complete utf-8 document. If longer than 1M characters it is split.| ### Dataset Creation We are providing a **train** and a **validation** split. The standard size of the validation is a single 1GB file, while train is sharded in 1GB chunks. All files are gzipped. Build date: 21012022 #### Initial Data Collection and Curation The procedure for the dataset creation is described in detail in our paper. ### Summary | Words | Documents | Words/Document | |--------------:|------------:|-----------------:| | 6,905,570,165 | 20,830,348 | 331 | ### Document Types | Source | Words | Documents | Words/Document | |--------------------------------------:|--------------:|------------:|-----------------:| | newspaper_ocr | 1,974,452,883 | 9,872,470 | 199 | | parliament | 1,273,353,169 | 9,321 | 136,611 | | books | 842,936,050 | 23,708 | 35,554 | | newspapers_online_nb | 487,189,627 | 3,446,348 | 141 | | maalfrid_regjeringen | 360,349,242 | 919,902 | 391 | | maalfrid_ssb | 279,732,847 | 851,982 | 328 | | maalfrid_uio | 181,916,296 | 771,480 | 235 | | government_nb | 134,127,104 | 3,476 | 38,586 | | wikipedia_download_nbo | 110,845,615 | 523,593 | 211 | | maalfrid_fylkesmannen | 102,849,898 | 463,021 | 222 | | publicreports | 78,347,879 | 3,298 | 23,756 | | maalfrid_nve | 66,656,315 | 301,966 | 220 | | maalfrid_patentstyret | 64,985,154 | 213,991 | 303 | | maalfrid_ntnu | 57,803,460 | 199,307 | 290 | | newspapers_online_nn | 42,205,558 | 167,347 | 252 | | lovdata_cd_odelsting_2005 | 36,370,948 | 1,933 | 18,815 | | maalfrid_vegvesen | 33,431,887 | 166,203 | 201 | | maalfrid_fhi | 32,784,098 | 144,363 | 227 | | maalfrid_norad | 32,720,034 | 93,097 | 351 | | maalfrid_skatteetaten | 32,567,691 | 82,589 | 394 | | maalfrid_uib | 28,425,322 | 115,729 | 245 | | wikipedia_download_nno | 27,061,858 | 143,265 | 188 | | maalfrid_forskningsradet | 24,076,984 | 73,368 | 328 | | maalfrid_nasjonalparkstyre | 21,309,995 | 93,871 | 227 | | government_nn | 18,316,345 | 1,063 | 17,230 | | maalfrid_nmbu | 18,082,476 | 69,719 | 259 | | maalfrid_oslomet | 17,710,771 | 47,022 | 376 | | maalfrid_domstol | 16,678,270 | 51,038 | 326 | | maalfrid_banenor | 16,445,420 | 70,360 | 233 | | maalfrid_nav | 16,272,635 | 74,101 | 219 | | maalfrid_landbruksdirektoratet | 13,119,567 | 47,983 | 273 | | maalfrid_helsedirektoratet | 13,008,787 | 49,344 | 263 | | maalfrid_nokut | 10,101,424 | 38,552 | 262 | | maalfrid_hi | 10,046,751 | 39,065 | 257 | | maalfrid_norges-bank | 9,924,489 | 37,171 | 266 | | maalfrid_udir | 9,868,345 | 38,736 | 254 | | maalfrid_vkm | 9,824,529 | 32,230 | 304 | | maalfrid_nbim | 9,629,725 | 18,131 | 531 | | maalfrid_miljodirektoratet | 9,496,631 | 34,711 | 273 | | maalfrid_distriktssenteret | 9,375,506 | 38,525 | 243 | | maalfrid_ngu | 9,231,905 | 34,619 | 266 | | maalfrid_ptil | 9,214,434 | 34,250 | 269 | | maalfrid_nord | 8,992,352 | 44,800 | 200 | | maalfrid_fiskeridir | 8,297,897 | 33,446 | 248 | | maalfrid_hivolda | 7,820,709 | 26,473 | 295 | | maalfrid_difi | 7,789,290 | 35,733 | 217 | | maalfrid_mattilsynet | 7,492,831 | 27,002 | 277 | | maalfrid_havarikommisjonen | 7,440,410 | 24,989 | 297 | | maalfrid_kulturradet | 7,196,423 | 22,437 | 320 | | maalfrid_ks | 6,915,503 | 27,439 | 252 | | maalfrid_kystverket | 6,713,070 | 30,975 | 216 | | maalfrid_udi | 6,433,540 | 19,134 | 336 | | maalfrid_uia | 5,964,644 | 23,861 | 249 | | maalfrid_hjelpemiddeldatabasen | 5,892,662 | 34,192 | 172 | | maalfrid_khrono | 5,859,186 | 19,970 | 293 | | maalfrid_helsetilsynet | 5,803,000 | 18,365 | 315 | | maalfrid_moreforsk | 5,622,025 | 21,579 | 260 | | maalfrid_jernbanedirektoratet | 5,461,268 | 21,666 | 252 | | maalfrid_veiviseren | 5,316,521 | 18,026 | 294 | | lovdata_cd_somb_rundskriv_2005 | 5,264,746 | 3,215 | 1,637 | | maalfrid_dsb | 5,199,259 | 17,814 | 291 | | lovdata_cd_sentrale_forskrifter_2005 | 5,037,694 | 11,467 | 439 | | maalfrid_husbanken | 4,711,069 | 15,053 | 312 | | maalfrid_legemiddelverket | 4,689,988 | 20,192 | 232 | | maalfrid_vetinst | 4,674,951 | 14,492 | 322 | | maalfrid_imdi | 4,636,355 | 15,290 | 303 | | maalfrid_forsvarsbygg | 4,567,318 | 18,886 | 241 | | maalfrid_sdir | 4,540,110 | 15,202 | 298 | | maalfrid_konkurransetilsynet | 4,512,807 | 12,617 | 357 | | maalfrid_dsa | 4,498,837 | 15,898 | 282 | | maalfrid_arkivverket | 4,493,280 | 16,515 | 272 | | maalfrid_hiof | 4,473,731 | 23,119 | 193 | | maalfrid_ehelse | 4,379,984 | 22,553 | 194 | | maalfrid_inn | 4,326,704 | 26,277 | 164 | | maalfrid_klagenemndssekretariatet | 4,181,685 | 11,916 | 350 | | maalfrid_sprakradet | 4,097,815 | 15,187 | 269 | | maalfrid_dibk | 3,967,428 | 15,509 | 255 | | maalfrid_nhh | 3,962,033 | 15,678 | 252 | | maalfrid_kartverket | 3,732,184 | 18,710 | 199 | | maalfrid_riksrevisjonen | 3,680,555 | 10,922 | 336 | | maalfrid_toll | 3,510,061 | 13,777 | 254 | | maalfrid_nibio | 3,456,026 | 17,104 | 202 | | maalfrid_met | 3,446,762 | 18,282 | 188 | | maalfrid_bufdir | 3,354,740 | 11,470 | 292 | | maalfrid_artsdatabanken | 3,193,511 | 9,009 | 354 | | maalfrid_politiet | 3,167,395 | 10,501 | 301 | | maalfrid_nkom | 3,127,687 | 10,002 | 312 | | maalfrid_vestlandfylke | 3,060,166 | 12,075 | 253 | | maalfrid_uis | 2,924,821 | 9,838 | 297 | | maalfrid_sykkelbynettverket | 2,820,702 | 11,818 | 238 | | maalfrid_nlr | 2,646,014 | 15,851 | 166 | | maalfrid_seniorporten | 2,616,054 | 8,111 | 322 | | maalfrid_npd | 2,597,831 | 10,742 | 241 | | maalfrid_aldringoghelse | 2,430,767 | 6,788 | 358 | | maalfrid_custompublish | 2,430,747 | 9,184 | 264 | | maalfrid_bioteknologiradet | 2,393,891 | 5,996 | 399 | | maalfrid_arbeidstilsynet | 2,379,597 | 6,882 | 345 | | maalfrid_nyemetoder | 2,376,468 | 10,771 | 220 | | maalfrid_riksantikvaren | 2,257,491 | 8,756 | 257 | | maalfrid_sjt | 2,238,168 | 11,189 | 200 | | lovdata_cd_lokaleforskrifter_2005 | 2,176,221 | 22,274 | 97 | | maalfrid_hvl | 2,149,292 | 9,395 | 228 | | maalfrid_luftfartstilsynet | 2,101,272 | 9,866 | 212 | | maalfrid_dfo | 2,073,203 | 9,165 | 226 | | maalfrid_ldo | 2,047,969 | 7,299 | 280 | | maalfrid_kompetansenorge | 1,952,035 | 10,245 | 190 | | maalfrid_forbrukerradet | 1,945,089 | 7,330 | 265 | | maalfrid_himolde | 1,913,699 | 9,975 | 191 | | maalfrid_usn | 1,793,297 | 7,403 | 242 | | lovdata_cd_norgeslover_2005 | 1,760,884 | 1,386 | 1,270 | | maalfrid_naku | 1,754,510 | 5,239 | 334 | | maalfrid_medietilsynet | 1,608,424 | 6,611 | 243 | | maalfrid_matematikksenteret | 1,567,505 | 7,298 | 214 | | maalfrid_forskningsetikk | 1,545,336 | 5,545 | 278 | | maalfrid_diku | 1,542,929 | 6,241 | 247 | | maalfrid_godeidrettsanlegg | 1,506,577 | 6,115 | 246 | | maalfrid_dirmin | 1,467,255 | 5,303 | 276 | | maalfrid_diskrimineringsnemnda | 1,463,291 | 4,168 | 351 | | maalfrid_naturfag | 1,450,662 | 5,976 | 242 | | maalfrid_arbeidsretten | 1,440,074 | 4,754 | 302 | | lovdata_cd_rtv_rundskriv_2005 | 1,366,872 | 9,596 | 142 | | maalfrid_fellesstudentsystem | 1,359,292 | 10,321 | 131 | | maalfrid_nupi | 1,286,395 | 5,491 | 234 | | maalfrid_kriminalitetsforebygging | 1,201,477 | 4,667 | 257 | | maalfrid_anskaffelser | 1,187,544 | 5,479 | 216 | | maalfrid_folketrygdfondet | 1,183,502 | 4,253 | 278 | | maalfrid_miljopakken | 1,170,252 | 5,513 | 212 | | maalfrid_nih | 1,116,791 | 5,271 | 211 | | maalfrid_statsbygg | 1,103,635 | 4,439 | 248 | | lovdata_cd_skatt_rundskriv_2005 | 1,102,142 | 398 | 2,769 | | maalfrid_nb | 1,055,200 | 4,135 | 255 | | maalfrid_npolar | 1,051,181 | 2,653 | 396 | | maalfrid_unit | 1,049,621 | 6,329 | 165 | | maalfrid_valgdirektoratet | 1,009,941 | 9,131 | 110 | | maalfrid_barneombudet | 980,751 | 2,807 | 349 | | maalfrid_datatilsynet | 974,679 | 2,965 | 328 | | maalfrid_lottstift | 959,590 | 3,578 | 268 | | maalfrid_aho | 953,568 | 4,528 | 210 | | maalfrid_sykehuspartner | 939,625 | 4,579 | 205 | | maalfrid_naturfagsenteret | 897,049 | 3,859 | 232 | | maalfrid_khio | 849,973 | 3,377 | 251 | | maalfrid_spesialenheten | 824,209 | 2,127 | 387 | | maalfrid_xn--miljlftet-o8ab | 803,011 | 3,384 | 237 | | maalfrid_samordnaopptak | 792,595 | 2,368 | 334 | | maalfrid_helsenorge | 780,465 | 3,034 | 257 | | maalfrid_skrivesenteret | 777,204 | 4,161 | 186 | | maalfrid_mareano | 760,645 | 3,724 | 204 | | maalfrid_fiskeridirektoratet | 755,997 | 2,444 | 309 | | maalfrid_sykehusinnkjop | 738,720 | 4,340 | 170 | | maalfrid_matportalen | 630,990 | 2,370 | 266 | | maalfrid_spk | 613,180 | 2,152 | 284 | | maalfrid_justervesenet | 595,014 | 1,904 | 312 | | maalfrid_pasientsikkerhetsprogrammet | 594,399 | 4,684 | 126 | | maalfrid_nhn | 579,713 | 3,581 | 161 | | maalfrid_sshf | 572,570 | 1,897 | 301 | | maalfrid_bibliotekutvikling | 560,126 | 3,216 | 174 | | maalfrid_nysgjerrigper | 559,207 | 3,019 | 185 | | maalfrid_nodnett | 538,021 | 2,689 | 200 | | maalfrid_une | 513,586 | 1,255 | 409 | | maalfrid_giek | 512,569 | 1,796 | 285 | | maalfrid_samas | 501,177 | 2,548 | 196 | | maalfrid_kriminalomsorgen | 496,062 | 1,951 | 254 | | maalfrid_kjonnsforskning | 483,376 | 1,426 | 338 | | maalfrid_kunstkultursenteret | 470,009 | 1,435 | 327 | | lovdata_cd_rundskriv_lovavdeling_2005 | 469,295 | 405 | 1,158 | | maalfrid_nynorsksenteret | 460,165 | 2,085 | 220 | | maalfrid_ceres | 448,920 | 1,950 | 230 | | maalfrid_stami | 445,031 | 1,160 | 383 | | maalfrid_nsm | 442,110 | 1,536 | 287 | | maalfrid_gjenopptakelse | 420,205 | 1,467 | 286 | | maalfrid_nfi | 420,128 | 1,523 | 275 | | maalfrid_nidsenter | 410,785 | 1,631 | 251 | | maalfrid_nasjonalmuseet | 390,036 | 1,087 | 358 | | maalfrid_forbrukertilsynet | 387,579 | 1,227 | 315 | | maalfrid_natursekken | 378,442 | 3,563 | 106 | | maalfrid_fordelingsutvalget | 355,121 | 1,385 | 256 | | maalfrid_digdir | 349,548 | 2,105 | 166 | | maalfrid_forsvaret | 331,183 | 1,215 | 272 | | maalfrid_beccle | 329,568 | 1,517 | 217 | | maalfrid_romsenter | 329,304 | 1,133 | 290 | | maalfrid_geonorge | 301,869 | 1,622 | 186 | | maalfrid_universell | 263,740 | 2,155 | 122 | | maalfrid_ovf | 262,542 | 930 | 282 | | maalfrid_forbrukereuropa | 259,420 | 1,018 | 254 | | maalfrid_politihogskolen | 258,615 | 1,229 | 210 | | maalfrid_vinmonopolet | 245,685 | 671 | 366 | | maalfrid_energimerking | 237,243 | 1,033 | 229 | | maalfrid_ombudsmann | 225,947 | 418 | 540 | | maalfrid_vea-fs | 224,712 | 1,261 | 178 | | maalfrid_traumebevisst | 224,297 | 2,417 | 92 | | maalfrid_npe | 205,102 | 1,000 | 205 | | maalfrid_pkh | 201,503 | 791 | 254 | | maalfrid_helfo | 193,880 | 988 | 196 | | maalfrid_opplaringslovutvalget | 193,590 | 549 | 352 | | maalfrid_regionaleforskningsfond | 187,261 | 989 | 189 | | maalfrid_nafkam | 177,295 | 571 | 310 | | maalfrid_jernbanemagasinet | 174,152 | 412 | 422 | | maalfrid_polarhistorie | 171,386 | 382 | 448 | | maalfrid_aasentunet | 161,626 | 529 | 305 | | maalfrid_riksteatret | 159,991 | 798 | 200 | | maalfrid_realfagsloyper | 157,166 | 748 | 210 | | maalfrid_koro | 153,304 | 574 | 267 | | maalfrid_squarespace | 146,931 | 504 | 291 | | maalfrid_politietssikkerhetstjeneste | 143,781 | 469 | 306 | | maalfrid_unknown | 139,263 | 700 | 198 | | maalfrid_whocc | 121,616 | 656 | 185 | | maalfrid_konfliktraadet | 120,258 | 372 | 323 | | maalfrid_okokrim | 115,842 | 372 | 311 | | maalfrid_brreg | 112,787 | 571 | 197 | | maalfrid_riksmekleren | 110,737 | 558 | 198 | | maalfrid_sismo | 110,700 | 309 | 358 | | maalfrid_radetfordyreetikk | 99,241 | 441 | 225 | | maalfrid_akkreditert | 99,040 | 503 | 196 | | maalfrid_sivilforsvaret | 97,679 | 514 | 190 | | maalfrid_lanekassen | 95,286 | 301 | 316 | | maalfrid_digidel | 95,140 | 607 | 156 | | maalfrid_generaladvokaten | 91,385 | 294 | 310 | | maalfrid_uit | 90,273 | 602 | 149 | | maalfrid_nyinorge | 88,466 | 199 | 444 | | maalfrid_lokforerskolen | 87,224 | 468 | 186 | | maalfrid_varsom | 85,382 | 563 | 151 | | maalfrid_ffi | 80,137 | 220 | 364 | | maalfrid_kulturminnefondet | 79,767 | 411 | 194 | | maalfrid_unesco | 76,951 | 382 | 201 | | maalfrid_yrkesfisker | 74,807 | 501 | 149 | | maalfrid_dekom | 72,148 | 1,307 | 55 | | maalfrid_omsorgsforskning | 71,675 | 321 | 223 | | maalfrid_lektor2 | 67,385 | 549 | 122 | | maalfrid_openaccess | 63,554 | 192 | 331 | | maalfrid_ssn | 63,036 | 302 | 208 | | maalfrid_lokalhistorie | 59,854 | 241 | 248 | | maalfrid_nlb | 57,872 | 200 | 289 | | maalfrid_riksadvokaten | 57,563 | 155 | 371 | | maalfrid_laudim | 57,500 | 393 | 146 | | maalfrid_denkulturelleskolesekken | 46,018 | 243 | 189 | | maalfrid_sivilrett | 44,062 | 142 | 310 | | maalfrid_htu | 43,330 | 169 | 256 | | maalfrid_yr | 40,646 | 562 | 72 | | maalfrid_informasjonskompetanse | 40,351 | 330 | 122 | | maalfrid_dep | 38,882 | 126 | 308 | | maalfrid_finansportalen | 38,506 | 180 | 213 | | maalfrid_feide | 36,715 | 267 | 137 | | maalfrid_kulturped | 36,013 | 96 | 375 | | maalfrid_fug | 34,158 | 120 | 284 | | maalfrid_kulturoghelse | 33,424 | 184 | 181 | | maalfrid_helseklage | 32,756 | 124 | 264 | | maalfrid_nbsk | 30,674 | 211 | 145 | | maalfrid_matogindustri | 29,922 | 194 | 154 | | maalfrid_sinn | 27,541 | 150 | 183 | | maalfrid_transport21 | 25,317 | 90 | 281 | | maalfrid_konkursradet | 23,505 | 76 | 309 | | maalfrid_vergemal | 23,271 | 77 | 302 | | maalfrid_norec | 22,496 | 78 | 288 | | maalfrid_pts | 20,459 | 78 | 262 | | maalfrid_nasjonaleturistveger | 19,922 | 110 | 181 | | maalfrid_iearth | 19,281 | 146 | 132 | | maalfrid_hjelpelinjen | 19,209 | 85 | 225 | | maalfrid_russamtalen | 17,999 | 65 | 276 | | maalfrid_xn--kvinneligomskjring-1ub | 17,701 | 77 | 229 | | maalfrid_nynorskbok | 17,600 | 96 | 183 | | maalfrid_regjeringsadvokaten | 17,416 | 55 | 316 | | maalfrid_memu | 17,311 | 98 | 176 | | maalfrid_xn--tilbakefring-2jb | 15,814 | 49 | 322 | | maalfrid_xn--forskerfr-t8a | 15,724 | 172 | 91 | | maalfrid_ringerikefengsel | 15,669 | 28 | 559 | | maalfrid_skeivtarkiv | 15,537 | 69 | 225 | | maalfrid_samfunnskunnskap | 15,110 | 60 | 251 | | maalfrid_fordelingsutvalet | 15,017 | 34 | 441 | | maalfrid_skattefunn | 14,599 | 51 | 286 | | maalfrid_shiprep | 14,165 | 142 | 99 | | maalfrid_haldenfengsel | 13,625 | 37 | 368 | | maalfrid_sevuppt | 13,332 | 52 | 256 | | maalfrid_forbrukerklageutvalget | 12,698 | 49 | 259 | | maalfrid_mhfa | 11,999 | 144 | 83 | | maalfrid_ah | 11,787 | 36 | 327 | | maalfrid_nettvett | 11,002 | 43 | 255 | | maalfrid_uh-it | 10,828 | 273 | 39 | | maalfrid_fishgen | 10,199 | 28 | 364 | | maalfrid_designavgang | 10,164 | 75 | 135 | | maalfrid_global | 9,051 | 41 | 220 | | maalfrid_havmiljo | 8,607 | 68 | 126 | | maalfrid_valg | 8,516 | 47 | 181 | | maalfrid_miljoklagenemnda | 7,797 | 35 | 222 | | maalfrid_altinn | 7,695 | 49 | 157 | | maalfrid_spinn-inn | 7,674 | 47 | 163 | | maalfrid_kantinekurset | 7,217 | 53 | 136 | | maalfrid_bastoyfengsel | 7,142 | 56 | 127 | | maalfrid_norskpetroleum | 6,083 | 119 | 51 | | maalfrid_voldsoffererstatning | 5,827 | 26 | 224 | | maalfrid_musikkbasertmiljobehandling | 5,186 | 39 | 132 | | maalfrid_prosjektveiviseren | 5,019 | 14 | 358 | | maalfrid_aldersvennlig | 4,919 | 32 | 153 | | maalfrid_barentswatch | 4,829 | 32 | 150 | | maalfrid_fmfiavo@fylkesmannen | 4,702 | 68 | 69 | | maalfrid_kk-utvalget | 4,697 | 19 | 247 | | maalfrid_agropub | 4,434 | 17 | 260 | | maalfrid_utdanningiverden | 4,266 | 13 | 328 | | maalfrid_overgangsbolig | 3,769 | 35 | 107 | | maalfrid_forsvaretsmuseer | 3,706 | 34 | 109 | | maalfrid_okopark | 3,282 | 12 | 273 | | maalfrid_pst | 2,866 | 14 | 204 | | maalfrid_sikkerhverdag | 2,697 | 18 | 149 | | maalfrid_arkitektur | 2,436 | 15 | 162 | | maalfrid_velgekte | 2,287 | 10 | 228 | | maalfrid_addlab | 2,109 | 12 | 175 | | maalfrid_romerikefengsel | 2,088 | 19 | 109 | | maalfrid_utdanning | 2,009 | 12 | 167 | | maalfrid_grunderskolen | 1,994 | 7 | 284 | | maalfrid_umb | 1,934 | 8 | 241 | | maalfrid_oslofengsel | 1,756 | 8 | 219 | | maalfrid_hjorteviltregisteret | 1,600 | 5 | 320 | | maalfrid_alleteller | 1,511 | 7 | 215 | | maalfrid_webhuset | 1,409 | 5 | 281 | | maalfrid_lykillinn | 1,349 | 4 | 337 | | maalfrid_kulturfag | 1,215 | 6 | 202 | | maalfrid_unimus | 940 | 4 | 235 | | maalfrid_anleggsregisteret | 928 | 5 | 185 | | maalfrid_mangfoldsprisen | 597 | 3 | 199 | | maalfrid_algae2future | 456 | 8 | 57 | | maalfrid_mammapresenterer | 447 | 2 | 223 | | maalfrid_karriereveiledning | 391 | 27 | 14 | | maalfrid_nodsms | 351 | 4 | 87 | | maalfrid_kildekompasset | 302 | 1 | 302 | | maalfrid_praksisfou | 297 | 1 | 297 | | maalfrid_retttilaalese | 246 | 3 | 82 | | maalfrid_indreostfoldfengsel | 215 | 3 | 71 | | maalfrid_xn--kroppsvingsforskning-gcc | 205 | 2 | 102 | | maalfrid_pahoyden | 154 | 1 | 154 | | maalfrid_norren | 42 | 1 | 42 | ### Languages | Language | Words | Documents | Words/Document | |-----------:|--------------:|------------:|-----------------:| | no | 5,050,752,505 | 17,177,223 | 294 | | da | 940,216,574 | 574,211 | 1,637 | | en | 474,855,361 | 1,526,795 | 311 | | nn | 299,753,996 | 987,701 | 303 | | fr | 49,409,701 | 108,071 | 457 | | de | 27,159,878 | 85,230 | 318 | | sv | 18,773,092 | 118,753 | 158 | | es | 10,057,791 | 42,177 | 238 | | fi | 8,104,322 | 46,710 | 173 | | et | 3,309,661 | 24,183 | 136 | | cs | 2,652,151 | 21,793 | 121 | | pt | 2,550,218 | 16,407 | 155 | | oc | 2,123,730 | 4,927 | 431 | | nl | 1,984,501 | 11,813 | 167 | | zh | 1,470,751 | 8,146 | 180 | | uk | 1,459,484 | 5,096 | 286 | | ca | 1,370,260 | 4,476 | 306 | | it | 1,293,230 | 8,479 | 152 | | la | 1,281,920 | 797 | 1,608 | | ru | 1,231,482 | 6,796 | 181 | | pl | 852,304 | 9,396 | 90 | | eu | 831,276 | 3,195 | 260 | | hu | 659,973 | 8,499 | 77 | | fa | 494,551 | 2,047 | 241 | | ja | 351,634 | 4,994 | 70 | | is | 309,422 | 1,207 | 256 | | id | 226,296 | 2,033 | 111 | | ar | 205,632 | 1,173 | 175 | | sl | 140,913 | 1,858 | 75 | | vi | 139,122 | 982 | 141 | | so | 128,303 | 592 | 216 | | hr | 124,033 | 1,081 | 114 | | el | 117,624 | 618 | 190 | | lv | 106,626 | 123 | 866 | | tr | 92,680 | 1,630 | 56 | | ro | 80,804 | 635 | 127 | | sr | 71,953 | 970 | 74 | | lt | 70,148 | 869 | 80 | | gl | 65,152 | 692 | 94 | | war | 56,369 | 274 | 205 | | ko | 56,057 | 1,006 | 55 | | th | 54,067 | 367 | 147 | | am | 44,818 | 317 | 141 | | sk | 39,416 | 1,000 | 39 | | ml | 35,575 | 156 | 228 | | ceb | 35,337 | 331 | 106 | | sq | 34,461 | 238 | 144 | | tl | 30,839 | 177 | 174 | | kk | 27,827 | 72 | 386 | | eo | 24,187 | 859 | 28 | | mn | 21,540 | 22 | 979 | | sw | 18,670 | 72 | 259 | | pnb | 18,403 | 80 | 230 | | sh | 17,807 | 213 | 83 | | gu | 16,973 | 13 | 1,305 | | bg | 16,495 | 100 | 164 | | ur | 15,650 | 169 | 92 | | mk | 13,305 | 65 | 204 | | ckb | 9,119 | 43 | 212 | | ku | 9,071 | 57 | 159 | | ast | 7,919 | 73 | 108 | | az | 7,907 | 59 | 134 | | ms | 7,051 | 483 | 14 | | uz | 6,924 | 56 | 123 | | ta | 4,180 | 60 | 69 | | fy | 3,841 | 68 | 56 | | ga | 3,761 | 174 | 21 | | hy | 3,456 | 43 | 80 | | pa | 3,299 | 17 | 194 | | hi | 2,783 | 39 | 71 | | be | 2,556 | 62 | 41 | | bo | 2,551 | 1 | 2,551 | | ht | 2,534 | 11 | 230 | | jv | 2,341 | 91 | 25 | | min | 2,206 | 18 | 122 | | cy | 2,052 | 52 | 39 | | bs | 2,047 | 66 | 31 | | als | 1,918 | 66 | 29 | | su | 1,888 | 29 | 65 | | nds | 1,869 | 162 | 11 | | ps | 1,832 | 15 | 122 | | bn | 1,797 | 22 | 81 | | qu | 1,498 | 14 | 107 | | ilo | 1,126 | 25 | 45 | | mt | 968 | 16 | 60 | | si | 942 | 29 | 32 | | te | 888 | 18 | 49 | | my | 784 | 15 | 52 | | af | 741 | 32 | 23 | | io | 715 | 15 | 47 | | tt | 684 | 22 | 31 | | km | 674 | 11 | 61 | | br | 645 | 40 | 16 | | gn | 638 | 11 | 58 | | jbo | 611 | 27 | 22 | | as | 584 | 2 | 292 | | ug | 581 | 6 | 96 | | kv | 562 | 3 | 187 | | kn | 544 | 22 | 24 | | pam | 480 | 2 | 240 | | kw | 475 | 19 | 25 | | vep | 419 | 34 | 12 | | he | 412 | 18 | 22 | | ka | 351 | 20 | 17 | | yo | 281 | 9 | 31 | | wa | 268 | 38 | 7 | | ky | 228 | 10 | 22 | | azb | 216 | 1 | 216 | | ba | 203 | 5 | 40 | | gom | 174 | 12 | 14 | | ia | 140 | 15 | 9 | | mr | 138 | 10 | 13 | | lmo | 134 | 27 | 4 | | tg | 129 | 3 | 43 | | lb | 115 | 26 | 4 | | pms | 115 | 16 | 7 | | vec | 67 | 3 | 22 | | rue | 67 | 2 | 33 | | sco | 61 | 6 | 10 | | ie | 59 | 11 | 5 | | hsb | 57 | 3 | 19 | | ne | 56 | 6 | 9 | | bar | 46 | 7 | 6 | | cbk | 46 | 2 | 23 | | or | 44 | 2 | 22 | | mg | 38 | 8 | 4 | | os | 36 | 3 | 12 | | tk | 36 | 4 | 9 | | arz | 31 | 1 | 31 | | li | 29 | 6 | 4 | | gd | 29 | 2 | 14 | | eml | 24 | 5 | 4 | | diq | 20 | 2 | 10 | | lrc | 20 | 1 | 20 | | dsb | 19 | 1 | 19 | | yue | 19 | 1 | 19 | | nap | 16 | 1 | 16 | | nah | 14 | 2 | 7 | | wuu | 14 | 1 | 14 | | sd | 14 | 1 | 14 | | frr | 13 | 3 | 4 | | rm | 12 | 2 | 6 | | cv | 12 | 1 | 12 | | scn | 9 | 2 | 4 | | bh | 8 | 1 | 8 | | bcl | 8 | 1 | 8 | | co | 7 | 1 | 7 | | ce | 4 | 1 | 4 | | new | 4 | 1 | 4 | | vo | 3 | 2 | 1 | | mzn | 3 | 1 | 3 | | gv | 3 | 1 | 3 | | lo | 2 | 1 | 2 | ### Publish Periode | Decade | Words | Documents | Words/Document | |---------:|--------------:|------------:|-----------------:| | 2020 | 4,090,213,596 | 10,934,550 | 523 | | 2010 | 355,391,417 | 2,415,563 | 1,511 | | 2000 | 447,853,330 | 1,705,354 | 2,773 | | 1990 | 767,392,364 | 2,513,364 | 3,051 | | 1980 | 160,980,586 | 538,665 | 3,011 | | 1970 | 186,113,674 | 829,646 | 2,222 | | 1960 | 149,421,535 | 834,219 | 1,807 | | 1950 | 97,863,608 | 478,628 | 2,041 | | 1940 | 122,648,278 | 570,154 | 2,307 | | 1930 | 35,635,053 | 697 | 508,420 | | 1920 | 50,381,418 | 1,049 | 484,836 | | 1910 | 62,599,984 | 1,221 | 504,678 | | 1900 | 60,019,080 | 1,130 | 527,329 | | 1890 | 86,781,861 | 1,777 | 485,878 | | 1880 | 58,546,570 | 1,064 | 553,442 | | 1870 | 26,492,662 | 632 | 407,191 | | 1860 | 39,176,930 | 698 | 543,151 | | 1850 | 53,801,490 | 846 | 634,038 | | 1840 | 30,434,939 | 522 | 581,593 | | 1830 | 18,189,838 | 368 | 481,719 | | 1820 | 4,721,154 | 144 | 338,350 | | 1810 | 910,798 | 57 | 124,880 | ## Considerations for Using the Data This corpus contains data under copyright and is not allowed to be used outide the National Library of Norway. The dataset should not be distributed. ### Discussion of Biases Please refer to our paper. ### Dataset Curators [Freddy Wetjen](mailto:[email protected]) and [Per Egil Kummervold](mailto:[email protected]) ## License Various licences applies to different parts of the corpus. Every document in the corpus has a tag telling what **"doc_type"** it belongs to. If you are unable to accept any of the licenses, you should filter out the **"doc_type"** with a conflicting license. | Doc_type | License | | :-------- | :------------- | | government_nb, government_nn, parliament, publicreports, lovdata_cd_\*, maalfrid_\* | [NLOD 2.0](https://data.norge.no/nlod/en/2.0/)| | newspapers_ocr, newspapers_pdf, books| [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)| | newspapers_online_nb, newspapers_online_nn | [CC BY-NC 2.0](https://creativecommons.org/licenses/by-nc/2.0/)| | opensubtitles, wikipedia | [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) | ### Citation Information We are preparing an article with detailed information about this corpus. Until it is published, please cite out paper discussing the first version of this corpus: ``` @inproceedings{kummervold-etal-2021-operationalizing, title = {Operationalizing a National Digital Library: The Case for a {N}orwegian Transformer Model}, author = {Kummervold, Per E and De la Rosa, Javier and Wetjen, Freddy and Brygfjeld, Svein Arne", booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)}, year = "2021", address = "Reykjavik, Iceland (Online)", publisher = {Link{"o}ping University Electronic Press, Sweden}, url = "https://aclanthology.org/2021.nodalida-main.3", pages = "20--29", abstract = "In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library. The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models in several token and sequence classification tasks for both Norwegian Bokm{aa}l and Norwegian Nynorsk. Our model also improves the mBERT performance for other languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore, we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.", } ```

Meranti/CLAP_freesound

Meranti

--- task_categories: - audio-classification language: - en tags: - audio - text - contrastive learning pretty_name: freesound size_categories: - 1M<n<10M --- # LAION-Audio-630K Freesound Dataset [LAION-Audio-630K](https://github.com/LAION-AI/audio-dataset/blob/main/laion-audio-630k/README.md) is the largest audio-text dataset publicly available and a magnitude larger than previous audio-text datasets (by 2022-11-05). Notably, it combines eight distinct datasets, which includes the Freesound dataset. Specifically, this Hugging face repository contains two versions of Freesound dataset. Details of each dataset (e.g. how captions are made etc.) could be found in the "datacard" column of the table below. - **Freesound (full)**: The complete Freesound dataset, available at `/freesound` folder. - **Freesound (no overlap)**: Made based on Freesound(full), with samples from ESC50, FSD50K, Urbansound8K and Clotho removed. available at `/freesound_no_overlap` folder. As of the structure and format of `freesound` and `freesound_no_overlap` folder, please refer to [this page](https://github.com/LAION-AI/audio-dataset/blob/main/data_preprocess/README.md). | Name |Duration |Number of Samples |Data Type | Metadata | Data Card | |--------------------------------------------------|-------------------------|--------------------|--------- |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------- | | Freesound (no overlap) |2817.31hrs | 460801 |1-2 captions per audio, audio | [website](https://freesound.org/) <br> [csv]()|[data card](/data_card/freesound.md)| | Freesound (full) |3033.38hrs | 515581 |1-2 captions per audio, audio | [website](https://freesound.org/) <br> [csv]() |[data card](/data_card/freesound.md)| ## Metadata csv file For each of the two datasets, we provide a metadata csv file including the following columns: - **audio_filename**: The filename of the audio file in `.tar` files. `exemple: 2394.flac` - **caption_i**: the i-th caption of the audio file - **freesound_id**: The freesound id of the audio file. - **username**: The username of the uploader of the audio file. - **freesound_url**: The url of the audio file in freesound.org - **username**: The freesound username of the uploader of the audio file. - **license**: The license of the audio file. `http://creativecommons.org/licenses/by/3.0/` ## Credits & Licence - **!!!TERM OF USE!!!**: **By downloading files in this repository, you agree that you will use them <u> for research purposes only </u>. If you want to use Freesound clips in LAION-Audio-630K for commercial purposes, please contact Frederic Font Corbera at [email protected].** ### Freesound Credit: All audio clips from Freesound are released under Creative Commons (CC) licenses, while each clip has its own license as defined by the clip uploader in Freesound, some of them requiring attribution to their original authors and some forbidding further commercial reuse. Specifically, here is the statistics about licenses of audio clips involved in LAION-Audio-630K: | License | Number of Samples | | :--- | :--- | | http://creativecommons.org/publicdomain/zero/1.0/ | 260134 | | https://creativecommons.org/licenses/by/4.0/ | 97090 | | http://creativecommons.org/licenses/by/3.0/ | 89337 | | http://creativecommons.org/licenses/by-nc/3.0/ | 31680 | | https://creativecommons.org/licenses/by-nc/4.0/ | 26736 | | http://creativecommons.org/licenses/sampling+/1.0/ | 11116 | ## Acknowledgement The whole collection process as well as all usage of the LAION-Audio-630K are conducted by Germany non-profit pure research organization [LAION](https://laion.ai/). All contributors and collectors of the dataset are considered as open source contributors affiliated to LAION. These community contributors (Discord ids) include but not limited to: @marianna13#7139, @Chr0my#0173, @PiEquals4#1909, @Yuchen Hui#8574, @Antoniooooo#4758, @IYWO#9072, krishna#1648, @dicknascarsixtynine#3885, and @turian#1607. We would like to appreciate all of them for their efforts on the LAION-Audio-630k dataset.

AI4Math/MathVista

AI4Math

--- annotations_creators: - expert-generated - found language_creators: - expert-generated - found language: - en - zh - fa license: cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - multiple-choice - question-answering - visual-question-answering - text-classification task_ids: - multiple-choice-qa - closed-domain-qa - open-domain-qa - visual-question-answering - multi-class-classification paperswithcode_id: mathvista pretty_name: MathVista tags: - multi-modal-qa - math-qa - figure-qa - geometry-qa - math-word-problem - textbook-qa - vqa - arithmetic-reasoning - statistical-reasoning - algebraic-reasoning - geometry-reasoning - numeric-common-sense - scientific-reasoning - logical-reasoning - geometry-diagram - synthetic-scene - chart - plot - scientific-figure - table - function-plot - abstract-scene - puzzle-test - document-image - medical-image - mathematics - science - chemistry - biology - physics - engineering - natural-science configs: - config_name: default data_files: - split: testmini path: data/testmini-* - split: test path: data/test-* dataset_info: features: - name: pid dtype: string - name: question dtype: string - name: image dtype: string - name: decoded_image dtype: image - name: choices sequence: string - name: unit dtype: string - name: precision dtype: float64 - name: answer dtype: string - name: question_type dtype: string - name: answer_type dtype: string - name: metadata struct: - name: category dtype: string - name: context dtype: string - name: grade dtype: string - name: img_height dtype: int64 - name: img_width dtype: int64 - name: language dtype: string - name: skills sequence: string - name: source dtype: string - name: split dtype: string - name: task dtype: string - name: query dtype: string splits: - name: testmini num_bytes: 142635198.0 num_examples: 1000 - name: test num_bytes: 648291350.22 num_examples: 5141 download_size: 885819490 dataset_size: 790926548.22 --- # Dataset Card for MathVista - [Dataset Description](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#dataset-description) - [Paper Information](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#paper-information) - [Dataset Examples](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#dataset-examples) - [Leaderboard](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#leaderboard) - [Dataset Usage](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#dataset-usage) - [Data Downloading](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-downloading) - [Data Format](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-format) - [Data Visualization](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-visualization) - [Data Source](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#data-source) - [Automatic Evaluation](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#automatic-evaluation) - [License](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#license) - [Citation](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/README.md#citation) ## Dataset Description **MathVista** is a consolidated Mathematical reasoning benchmark within Visual contexts. It consists of **three newly created datasets, IQTest, FunctionQA, and PaperQA**, which address the missing visual domains and are tailored to evaluate logical reasoning on puzzle test figures, algebraic reasoning over functional plots, and scientific reasoning with academic paper figures, respectively. It also incorporates **9 MathQA datasets** and **19 VQA datasets** from the literature, which significantly enrich the diversity and complexity of visual perception and mathematical reasoning challenges within our benchmark. In total, **MathVista** includes **6,141 examples** collected from **31 different datasets**. ## Paper Information - Paper: https://arxiv.org/abs/2310.02255 - Code: https://github.com/lupantech/MathVista - Project: https://mathvista.github.io/ - Visualization: https://mathvista.github.io/#visualization - Leaderboard: https://mathvista.github.io/#leaderboard ## Dataset Examples Examples of our newly annotated datasets: IQTest, FunctionQA, and PaperQA: <img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/our_new_3_datasets.png" style="zoom:40%;" /> <details> <summary>🔍 Click to expand/collapse more examples</summary> Examples of seven mathematical reasoning skills: 1. Arithmetic Reasoning <img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/ari.png" style="zoom:40%;" /> 2. Statistical Reasoning <img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/sta.png" style="zoom:40%;" /> 3. Algebraic Reasoning <img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/alg.png" style="zoom:40%;" /> 4. Geometry Reasoning <img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/geo.png" style="zoom:40%;" /> 5. Numeric common sense <img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/num.png" style="zoom:40%;" /> 6. Scientific Reasoning <img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/sci.png" style="zoom:40%;" /> 7. Logical Reasoning <img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/skills/log.png" style="zoom:40%;" /> </details> ## Leaderboard 🏆 The leaderboard for the *testmini* set (1,000 examples) is available [here](https://mathvista.github.io/#leaderboard). 🏆 The leaderboard for the *test* set (5,141 examples) and the automatic evaluation on [CodaLab](https://codalab.org/) are under construction. ## Dataset Usage ### Data Downloading All the data examples were divided into two subsets: *testmini* and *test*. - **testmini**: 1,000 examples used for model development, validation, or for those with limited computing resources. - **test**: 5,141 examples for standard evaluation. Notably, the answer labels for test will NOT be publicly released. You can download this dataset by the following command (make sure that you have installed [Huggingface Datasets](https://huggingface.co/docs/datasets/quickstart)): ```python from datasets import load_dataset dataset = load_dataset("AI4Math/MathVista") ``` Here are some examples of how to access the downloaded dataset: ```python # print the first example on the testmini set print(dataset["testmini"][0]) print(dataset["testmini"][0]['pid']) # print the problem id print(dataset["testmini"][0]['question']) # print the question text print(dataset["testmini"][0]['query']) # print the query text print(dataset["testmini"][0]['image']) # print the image path print(dataset["testmini"][0]['answer']) # print the answer dataset["testmini"][0]['decoded_image'] # display the image # print the first example on the test set print(dataset["test"][0]) ``` ### Data Format The dataset is provided in json format and contains the following attributes: ```json { "question": [string] The question text, "image": [string] A file path pointing to the associated image, "choices": [list] Choice options for multiple-choice problems. For free-form problems, this could be a 'none' value, "unit": [string] The unit associated with the answer, e.g., "m^2", "years". If no unit is relevant, it can be a 'none' value, "precision": [integer] The number of decimal places the answer should be rounded to, "answer": [string] The correct answer for the problem, "question_type": [string] The type of question: "multi_choice" or "free_form", "answer_type": [string] The format of the answer: "text", "integer", "float", or "list", "pid": [string] Problem ID, e.g., "1", "metadata": { "split": [string] Data split: "testmini" or "test", "language": [string] Question language: "English", "Chinese", or "Persian", "img_width": [integer] The width of the associated image in pixels, "img_height": [integer] The height of the associated image in pixels, "source": [string] The source dataset from which the problem was taken, "category": [string] The category of the problem: "math-targeted-vqa" or "general-vqa", "task": [string] The task of the problem, e.g., "geometry problem solving", "context": [string] The visual context type of the associated image, "grade": [string] The grade level of the problem, e.g., "high school", "skills": [list] A list of mathematical reasoning skills that the problem tests }, "query": [string] the query text used as input (prompt) for the evaluation model } ``` ### Data Visualization 🎰 You can explore the dataset in an interactive way [here](https://mathvista.github.io/#visualization). <details> <summary>Click to expand/collapse the visualization page screeshot.</summary> <img src="https://raw.githubusercontent.com/lupantech/MathVista/main/assets/data_visualizer.png" style="zoom:40%;" /> </details> ### Data Source The **MathVista** dataset is derived from three newly collected datasets: IQTest, FunctionQA, and Paper, as well as 28 other source datasets. Details can be found in the [source.json](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/source.json) file. All these source datasets have been preprocessed and labeled for evaluation purposes. ### Automatic Evaluation 🔔 To automatically evaluate a model on the dataset, please refer to our GitHub repository [here](https://github.com/lupantech/MathVista/tree/main). ## License The new contributions to our dataset are distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license, including - The creation of three datasets: IQTest, FunctionQA, and Paper; - The filtering and cleaning of source datasets; - The standard formalization of instances for evaluation purposes; - The annotations of metadata. The copyright of the images and the questions belongs to the original authors, and the source of every image and original question can be found in the `metadata` field and in the [source.json](https://huggingface.co/datasets/AI4Math/MathVista/blob/main/source.json) file. Alongside this license, the following conditions apply: - **Purpose:** The dataset was primarily designed for use as a test set. - **Commercial Use:** The dataset can be used commercially as a test set, but using it as a training set is prohibited. By accessing or using this dataset, you acknowledge and agree to abide by these terms in conjunction with the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license. ## Citation If you use the **MathVista** dataset in your work, please kindly cite the paper using this BibTeX: ``` @inproceedings{lu2024mathvista, author = {Lu, Pan and Bansal, Hritik and Xia, Tony and Liu, Jiacheng and Li, Chunyuan and Hajishirzi, Hannaneh and Cheng, Hao and Chang, Kai-Wei and Galley, Michel and Gao, Jianfeng}, title = {MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2024} } ```

li2017dailydialog/daily_dialog

li2017dailydialog

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification paperswithcode_id: dailydialog pretty_name: DailyDialog tags: - emotion-classification - dialog-act-classification dataset_info: features: - name: dialog sequence: string - name: act sequence: class_label: names: '0': __dummy__ '1': inform '2': question '3': directive '4': commissive - name: emotion sequence: class_label: names: '0': no emotion '1': anger '2': disgust '3': fear '4': happiness '5': sadness '6': surprise splits: - name: train num_bytes: 7296715 num_examples: 11118 - name: test num_bytes: 655844 num_examples: 1000 - name: validation num_bytes: 673943 num_examples: 1000 download_size: 4475921 dataset_size: 8626502 --- # Dataset Card for "daily_dialog" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [http://yanran.li/dailydialog](http://yanran.li/dailydialog) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 4.48 MB - **Size of the generated dataset:** 8.63 MB - **Total amount of disk used:** 13.11 MB ### Dataset Summary We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 4.48 MB - **Size of the generated dataset:** 8.63 MB - **Total amount of disk used:** 13.11 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "act": [2, 1, 1, 1, 1, 2, 3, 2, 3, 4], "dialog": "[\"Good afternoon . This is Michelle Li speaking , calling on behalf of IBA . Is Mr Meng available at all ? \", \" This is Mr Meng ...", "emotion": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] } ``` ### Data Fields The data fields are the same among all splits. #### default - `dialog`: a `list` of `string` features. - `act`: a `list` of classification labels, with possible values including `__dummy__` (0), `inform` (1), `question` (2), `directive` (3) and `commissive` (4). - `emotion`: a `list` of classification labels, with possible values including `no emotion` (0), `anger` (1), `disgust` (2), `fear` (3), `happiness` (4), `sadness` (5) and `surprise` (6). ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|11118| 1000|1000| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information DailyDialog dataset is licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). ### Citation Information ``` @InProceedings{li2017dailydialog, author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi}, title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset}, booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)}, year = {2017} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@julien-c](https://github.com/julien-c) for adding this dataset.

open-source-metrics/diffusers-dependents

open-source-metrics

--- license: apache-2.0 pretty_name: diffusers metrics tags: - github-stars dataset_info: features: - name: name dtype: string - name: stars dtype: int64 - name: forks dtype: int64 splits: - name: package num_bytes: 2680 num_examples: 62 - name: repository num_bytes: 92837 num_examples: 1976 download_size: 55374 dataset_size: 95517 --- # diffusers metrics This dataset contains metrics about the huggingface/diffusers package. Number of repositories in the dataset: 160 Number of packages in the dataset: 2 ## Package dependents This contains the data available in the [used-by](https://github.com/huggingface/diffusers/network/dependents) tab on GitHub. ### Package & Repository star count This section shows the package and repository star count, individually. Package | Repository :-------------------------:|:-------------------------:  |  There are 0 packages that have more than 1000 stars. There are 3 repositories that have more than 1000 stars. The top 10 in each category are the following: *Package* [JoaoLages/diffusers-interpret](https://github.com/JoaoLages/diffusers-interpret): 121 [samedii/perceptor](https://github.com/samedii/perceptor): 1 *Repository* [gradio-app/gradio](https://github.com/gradio-app/gradio): 9168 [divamgupta/diffusionbee-stable-diffusion-ui](https://github.com/divamgupta/diffusionbee-stable-diffusion-ui): 4264 [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui): 3527 [bes-dev/stable_diffusion.openvino](https://github.com/bes-dev/stable_diffusion.openvino): 925 [nateraw/stable-diffusion-videos](https://github.com/nateraw/stable-diffusion-videos): 899 [sharonzhou/long_stable_diffusion](https://github.com/sharonzhou/long_stable_diffusion): 360 [Eventual-Inc/Daft](https://github.com/Eventual-Inc/Daft): 251 [JoaoLages/diffusers-interpret](https://github.com/JoaoLages/diffusers-interpret): 121 [GT4SD/gt4sd-core](https://github.com/GT4SD/gt4sd-core): 113 [brycedrennan/imaginAIry](https://github.com/brycedrennan/imaginAIry): 104 ### Package & Repository fork count This section shows the package and repository fork count, individually. Package | Repository :-------------------------:|:-------------------------:  |  There are 0 packages that have more than 200 forks. There are 2 repositories that have more than 200 forks. The top 10 in each category are the following: *Package* *Repository* [gradio-app/gradio](https://github.com/gradio-app/gradio): 574 [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui): 377 [bes-dev/stable_diffusion.openvino](https://github.com/bes-dev/stable_diffusion.openvino): 108 [divamgupta/diffusionbee-stable-diffusion-ui](https://github.com/divamgupta/diffusionbee-stable-diffusion-ui): 96 [nateraw/stable-diffusion-videos](https://github.com/nateraw/stable-diffusion-videos): 73 [GT4SD/gt4sd-core](https://github.com/GT4SD/gt4sd-core): 34 [sharonzhou/long_stable_diffusion](https://github.com/sharonzhou/long_stable_diffusion): 29 [coreweave/kubernetes-cloud](https://github.com/coreweave/kubernetes-cloud): 20 [bananaml/serverless-template-stable-diffusion](https://github.com/bananaml/serverless-template-stable-diffusion): 15 [AmericanPresidentJimmyCarter/yasd-discord-bot](https://github.com/AmericanPresidentJimmyCarter/yasd-discord-bot): 9 [NickLucche/stable-diffusion-nvidia-docker](https://github.com/NickLucche/stable-diffusion-nvidia-docker): 9 [vopani/waveton](https://github.com/vopani/waveton): 9 [harubaru/discord-stable-diffusion](https://github.com/harubaru/discord-stable-diffusion): 9

MBZUAI/Bactrian-X

MBZUAI

--- license: cc-by-nc-4.0 task_categories: - text-generation language: - af - ar - az - bn - cs - de - en - es - et - fi - fr - gl - gu - he - hi - hr - id - it - ja - ka - kk - km - ko - lt - lv - mk - ml - mn - mr - my - ne - nl - pl - ps - pt - ro - ru - si - sl - sv - sw - ta - te - th - tl - tr - uk - ur - vi - xh - zh tags: - instruction-finetuning - multilingual pretty_name: Bactrian-X --- # Dataset Card for "Bactrian-X" ## Table of Contents - [Dataset Description](#a-dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#b-dataset-structure) - [Data Fields](#data-fields) - [Data Instances](#data-instances) - [Data in 52 Languages](#data-in-52-languages) - [Dataset Creation](#c-dataset-creation) - [Considerations for Using the Data](#d-considerations-for-using-the-data) - [Additional Information](#e-additional-information) ## A. Dataset Description - **Homepage:** https://github.com/mbzuai-nlp/Bactrian-X - **Repository:** https://huggingface.co/datasets/MBZUAI/Bactrian-X - **Paper:** to-be-soon released ### Dataset Summary <h3 align="center"> <img src="https://raw.githubusercontent.com/fajri91/eval_picts/master/BactrianX_dataset.jpg" width="950" align="center"> </h3> The Bactrain-X dataset is a collection of 3.4M instruction-response pairs in 52 languages, that are obtained by translating 67K English instructions ([alpaca-52k](https://github.com/tatsu-lab/stanford_alpaca) + [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data)) into 51 languages using Google Translate API. The translated instructions are then fed to ChatGPT (`gpt-3.5-turbo`) to obtain its natural responses, resulting in 3.4M instruction-response pairs in 52 languages (52 languages x 67k instances = 3.4M instances). ### Languages We follow 52 languages of [mBART-50](https://arxiv.org/abs/2008.00401), with details can be found [below](#data-in-52-languages). ## B. Dataset Structure ### Data Fields The data fields are as follows: * `instruction`: describes the task the model should perform. Each of the 67K instructions is unique. * `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input. * `output`: the answer to the instruction as generated by `gpt-3.5-turbo`. * `id`: the unique index for each data piece in a specific language. The same `id` across splits corresponds to the same original English data. ### Data Instances An example in `en` looks as follows. ```json { "instruction": "What is the main idea of this text?", "input": "The robotics industry has become one of the fastest growing sectors in today’s economy. Robotics technology is being applied in various industries and applications, such as manufacturing, healthcare, and defense.", "output": "The main idea of the text is that the robotics industry is rapidly growing and its technology is being used in many different industries and applications.", "id": "alpaca-107" } ``` An example in `ar` looks as follows. ```json { "instruction": "قم بالوصول إلى إعدادات المتصفح الافتراضية على جهاز الكمبيوتر.", "input": "", "output": "لا يمكن لي كمساعد افتراضي القيام بهذه المهمة كونها تحتاج إلى الوصول إلى الجهاز المادي. يرجى الرجوع إلى دليل المستخدم للكمبيوتر الخاص بك أو البحث على الإنترنت لمعرفة كيفية الوصول إلى إعدادات المتصفح الافتراضية على جهاز الكمبيوتر الخاص بك.", "id": "alpaca-41997" } ``` An example in `id` looks as follows. ```json { "instruction": "Berapa banyak angka penting yang harus digunakan untuk nilai 0,000187?", "input": "", "output": "Ada tiga angka penting dalam nilai 0,000187.", "id": "alpaca-40382" } ``` An example in `de` looks as follows. ```json { "instruction": "Komponieren Sie ein Musikstück mit einem Titel, der Trauer bezeichnet.", "input": "", "output": ""Schmerzendes Herz"\n\nDas Stück beginnt mit einer langsamen, traurigen Melodie, gespielt von einem Solo-Violoncello. Die Harmonien, die von einem Klavier begleitet werden, sind durcheinander und dissonant, um ein Gefühl von Schmerz und Trauer zu vermitteln.\n\nIm Laufe des Stücks treten andere Instrumente ein, wie Streicher, Flöte und Oboe, um die Melodie zu unterstützen und zu intensivieren. Der Rhythmus bleibt langsam und schwerfällig, um die Schwere des Schmerzes darzustellen.\n\nIn der Mitte des Stücks gibt es einen Höhepunkt, wo die Melodie und Harmonien sich verdichten und ein Gefühl von Überwältigung erzeugen. Der Solo-Violoncello spielt eine traurige und schnelle Phrase, die die Verzweiflung und Isolation des Protagonisten ausdrückt.\n\nDas Stück endet mit einer Wiederholung der ersten Melodie, gespielt von einem Solo-Klavier, während die anderen Instrumente auslaufen. Die Harmonien werden jedoch weicher und sanfter, um einen Hauch von Hoffnung zu vermitteln, dass der Schmerz langsam vergeht.", "id": "alpaca-33619" } ``` An example in `fr` looks as follows. ```json { "instruction": "Les Ceanothus ont-ils des racines peu profondes ?", "input": "", "output": "En tant qu'IA, je ne peux pas répondre avec une certitude absolue, mais voici des informations générales sur les racines de Ceanothus : \n\nLes Ceanothus ont tendance à avoir un système racinaire étendu et peu profond. Les racines peuvent s'étaler jusqu'à 4 à 6 fois la taille de la plante et s'étendre à environ 60 à 90 cm de profondeur. Cependant, cela peut varier en fonction du type de sol et de la variété de Ceanothus. Certaines variétés de Ceanothus pourraient avoir des racines plus profondes si les conditions s'y prêtent.", "id": "dolly-10940" } ``` ### Data in 52 Languages | No | Languages | Code | Train | | ---|---------------- | ----- | ----- | | 1 | [Afrikaans](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/af) | af_ZA | 67017 | | 2 | [Arabic](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ar) | ar_AR | 67017 | | 3 | [Azerbaijani](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/az) | az_AZ | 67017 | | 4 | [Bengali](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/bn) | bn_IN | 67017 | | 5 | [Czech](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/cs) | cs_CZ | 67017 | | 6 | [German](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/de) | de_DE | 67017 | | 7 | [English](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/en) | en_XX | 67017 | | 8 | [Spanish](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/es) | es_XX | 67017 | | 9 | [Estonian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/et) | et_EE | 67017 | | 10 | [Persian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/fa) | fa_IR | 67017 | | 11 | [Finnish](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/fi) | fi_FI | 67017 | | 12 | [French](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/fr) | fr_XX | 67017 | | 13 | [Galician](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/gl) | gl_ES | 67017 | | 14 | [Gujarati](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/gu) | gu_IN | 67017 | | 15 | [Hebrew](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/he) | he_IL | 67017 | | 16 | [Hindi](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/hi) | hi_IN | 67017 | | 17 | [Croatian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/hr) | hr_HR | 67017 | | 18 | [Indonesian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/id) | id_ID | 67017 | | 19 | [Italian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/it) | it_IT | 67017 | | 20 | [Japanese](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ja) | ja_XX | 67017 | | 21 | [Georgian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ka) | ka_GE | 67017 | | 22 | [Kazakh](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/kk) | kk_KZ | 67017 | | 23 | [Khmer](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/km) | km_KH | 67017 | | 24 | [Korean](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ko) | ko_KR | 67017 | | 25 | [Lithuanian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/lt) | lt_LT | 67017 | | 26 | [Latvian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/lv) | lv_LV | 67017 | | 27 | [Macedonian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/mk) | mk_MK | 67017 | | 28 | [Malayalam](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ml) | ml_IN | 67017 | | 29 | [Mongolian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/mn) | mn_MN | 67017 | | 30 | [Marathi](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/mr) | mr_IN | 67017 | | 31 | [Burmese](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/my) | my_MM | 67017 | | 32 | [Nepali](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ne) | ne_NP | 67017 | | 33 | [Dutch](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/nl) | nl_XX | 67017 | | 34 | [Polish](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/pl) | pl_PL | 67017 | | 35 | [Pashto](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ps) | ps_AF | 67017 | | 36 | [Portuguese](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/pt) | pt_XX | 67017 | | 37 | [Romanian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ro) | ro_RO | 67017 | | 38 | [Russian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ru) | ru_RU | 67017 | | 39 | [Sinhala](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/si) | si_LK | 67017 | | 40 | [Slovene](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/sl) | sl_SI | 67017 | | 41 | [Swedish](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/sv) | sv_SE | 67017 | | 42 | [Swahili](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/sw) | sw_KE | 67017 | | 43 | [Tamil](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ta) | ta_IN | 67017 | | 44 | [Telugu](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/te) | te_IN | 67017 | | 45 | [Thai](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/th) | th_TH | 67017 | | 46 | [Tagalog](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/tl) | tl_XX | 67017 | | 47 | [Turkish](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/tr) | tr_TR | 67017 | | 48 | [Ukrainian](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/uk) | uk_UA | 67017 | | 49 | [Urdu](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/ur) | ur_PK | 67017 | | 50 | [Vietnamese](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/vi) | vi_VN | 67017 | | 51 | [Xhosa](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/xh) | xh_ZA | 67017 | | 52 | [Chinese](https://huggingface.co/datasets/MBZUAI/Bactrian-X/viewer/zh) | zh_CN | 67017 | ## C. Dataset Creation 1. English Instructions: The English instuctions are obtained from [alpaca-53k](https://github.com/tatsu-lab/stanford_alpaca), and [dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data). 2. Instruction Translation: The instructions (and inputs) are translated into 51 languages using Google Translation API (conducted on April 2023). 3. Output Generation: We generate output from `gpt-3.5-turbo` for each language (conducted on April 2023). ## D. Considerations for Using the Data ### Social Impact of Dataset NLP for everyone: this dataset helps to democratize the cutting-edge instruction-following models in 52 languages. This dataset also allows the first experiment on the multilingual LoRA-based LLaMA model. ### Discussion of Biases (1) Translation bias; (2) Potential English-culture bias in the translated dataset. ### Other Known Limitations The `Bactrian-X` data is generated by a language model (`gpt-3.5-turbo`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections. ## E. Additional Information ### Dataset Curators [Haonan Li](https://haonan-li.github.io/) and [Fajri Koto](http://www.fajrikoto.com) ### Licensing Information The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). ### Citation Information ``` @misc{li2023bactrianx, title={Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation}, author={Haonan Li and Fajri Koto and Minghao Wu and Alham Fikri Aji and Timothy Baldwin}, year={2023}, eprint={2305.15011}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@haonan-li](https://github.com/haonan-li), [@fajri91](https://github.com/fajri91) for adding this dataset.

chujiezheng/wizard_of_wikipedia

chujiezheng

--- license: cc-by-nc-4.0 language: - en --- Wizard-of-Wikipedia data for the Findings of EMNLP 2020 paper "Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation" [GitHub repo](https://github.com/chujiezheng/DiffKS). [Original paper](https://arxiv.org/abs/2009.09378). ```bib @inproceedings{zheng-etal-2020-diffks, title="{D}ifference-aware Knowledge Selection for Knowledge-grounded Conversation Generation", author="Zheng, Chujie and Cao, Yunbo and Jiang, Daxin and Huang, Minlie", booktitle="Findings of EMNLP", year="2020" } ```

BAAI/IndustryCorpus2

BAAI

--- license: apache-2.0 language: - en - zh size_categories: - n>1T extra_gated_prompt: "You agree to not use the dataset to conduct experiments that cause harm to human subjects." extra_gated_fields: Company/Organization: text Country: country --- Industry models play a vital role in promoting the intelligent transformation and innovative development of enterprises. High-quality industry data is the key to improving the performance of large models and realizing the implementation of industry applications. However, the data sets currently used for industry model training generally have problems such as small data volume, low quality, and lack of professionalism. In June, we released the [IndustryCorpus](https://huggingface.co/datasets/BAAI/IndustryCorpus) dataset: We have further upgraded and iterated on this dataset, and the iterative contents are as follows: - Data source: Based on the original data, we introduced more high-quality data sources, such as pile, bigcode, open-web-math and other mathematical and code data - Update the industry category system: In order to better fit the industry classification system, we combined the national economic industry classification system (20 categories) formulated by the National Bureau of Statistics and the world knowledge system to redesign the industry categories, setting up 31 industry categories, basically covering the current mainstream industries - Data semantic quality screening: We decentralized the IndustryCorpus high-quality data production plan, and used the rule filtering + model filtering solution in the IndustryCorpus2.0 open source data, which greatly improved the overall data quality; - Data quality stratification: In order to further integrate data quality at different levels, we stratify and organize the data based on the quality assessment score, dividing the data into three levels: high, middle, and low. - Data size: 1TB for Chinese and 2.2TB for English The data processing process is consistent with IndustryCorpus  ## Data Perspective ### Industry Data Distribution The disk size of each industry data after full process processing is as follows | Industry category | Data size (GB) | Industry category | Data size (GB) | | :-------------------------------------------------: | :------------: | :-----------------------------------------------: | :------------: | | Programming | 11.0 | News | 51.0 | | Biomedicine | 61.7 | Petrochemical | 40.2 | | Medical health-psychology and Chinese medicine | 271.7 | Aerospace | 38.6 | | Tourism and geography | 64.0 | Mining | 8.9 | | Law and justice | 238.5 | Finance and economics | 145.8 | | Mathematics-statistics | 156.7 | Literature and emotions | 105.5 | | Other information services_information security | 1.8 | Transportation | 40.5 | | Fire safety_food safety | 4.3 | Science and technology_scientific research | 101.6 | | Automobile | 39.3 | Water Conservancy_Ocean | 20.2 | | Accommodation-catering-hotel | 29.6 | Computer-communication | 157.8 | | Film and television entertainment | 209.4 | Subject education | 340.9 | | Real estate-construction | 105.2 | Artificial intelligence-machine learning | 7.7 | | Electric power and energy | 68.7 | Current affairs-government affairs-administration | 271.5 | | Agriculture, forestry, animal husbandry and fishery | 111.9 | Sports | 262.5 | | Games | 37.6 | Other manufacturing | 47.2 | | Others | 188.6 | | | | Total (GB) | 3276G | | | The industry data distribution chart in the summary data set is as follows  From the distribution chart, we can see that subject education, sports, current affairs, law, medical health, film and television entertainment account for most of the overall data. The data of these industries are widely available on the Internet and textbooks, and the high proportion of them is in line with expectations. It is worth mentioning that since we have supplemented the data of mathematics, we can see that the proportion of mathematics data is also high, which is inconsistent with the proportion of mathematics Internet corpus data. ### dataset repo series All our data repos have a unified naming format, f"BAAI/IndustryCorpus2_{name}", where `name` corresponds to the English name of the industry. The list of industry names is shown below ``` { "交通运输": "transportation", "医学_健康_心理_中医": "medicine_health_psychology_traditional_chinese_medicine", "数学_统计学": "mathematics_statistics", "时政_政务_行政": "current_affairs_government_administration", "消防安全_食品安全": "fire_safety_food_safety", "石油化工": "petrochemical", "计算机_通信": "computer_communication", "人工智能_机器学习": "artificial_intelligence_machine_learning", "其他信息服务_信息安全": "other_information_services_information_security", "学科教育_教育": "subject_education_education", "文学_情感": "literature_emotion", "水利_海洋": "water_resources_ocean", "游戏": "game", "科技_科学研究": "technology_scientific_research", "采矿": "mining", "住宿_餐饮_酒店": "accommodation_catering_hotel", "其他制造": "other_manufacturing", "影视_娱乐": "film_entertainment", "新闻传媒": "news_media", "汽车": "automobile", "生物医药": "biomedicine", "航空航天": "aerospace", "金融_经济": "finance_economics", "体育": "sports", "农林牧渔": "agriculture_forestry_animal_husbandry_fishery", "房地产_建筑": "real_estate_construction", "旅游_地理": "tourism_geography", "法律_司法": "law_judiciary", "电力能源": "electric_power_energy", "计算机编程_代码": "computer_programming_code", } ``` ### Data quality stratification We filter the entire data according to data quality, remove extremely low-quality data, and divide the available data into three independent groups: Low, Middle, and Hight, to facilitate data matching and combination during model training. The distribution of data of different qualities is shown below. It can be seen that the data quality distribution trends of Chinese and English are basically the same, with the largest number of middle data, followed by middle data, and the least number of low data; in addition, it can be observed that the proportion of hight data in English is higher than that in Chinese (with a larger slope), which is also in line with the current trend of distribution of different languages.  ## Industry Category Classification In order to improve the coverage of industry classification in the data set to actual industries and align with the industry catalog defined in the national standard, we refer to the national economic industry classification system and the world knowledge system formulated by the National Bureau of Statistics, merge and integrate the categories, and design the final 31 industry categories covering Chinese and English. The category table names are as follows - Data construction of industry classification model - Data construction Data source: pre-training corpus sampling and open source text classification data, of which pre-training corpus accounts for 90%. Through data sampling, the ratio of Chinese and English data is guaranteed to be 1:1 Label construction: Use the LLM model to make multiple classification judgments on the data, and select the data with consistent multiple judgments as training data Data scale: 36K The overall process of data construction is as follows:  - Model training: Parameter update: add classification head to pre-trained BERT model for text classification model training Model selection: considering model performance and inference efficiency, we selected a 0.5B scale model. Through comparative experiments, we finally selected BGE-M3 and full parameter training as our base model Training hyperparameters: full parameter training, max_length = 2048, lr = 1e-5, batch_size = 64, validation set evaluation acc: 86%  ## Data quality assessment - Why should we filter low-quality data? Below is low-quality data extracted from the data. It can be seen that this kind of data is harmful to the learning of the model. ``` {"text": "\\_\\__\n\nTranslated from *Chinese Journal of Biochemistry and Molecular Biology*, 2007, 23(2): 154--159 \\[译自:中国生物化学与分子生物学报\\]\n"} {"text": "#ifndef _IMGBMP_H_\n#define _IMGBMP_H_\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\nconst uint8_t bmp[]={\n\\/\\/-- 调入了一幅图像：D:\\我的文档\\My Pictures\\12864-555.bmp --*\\/\n\\/\\/-- 宽度x高度=128x64 --\n0x00,0x06,0x0A,0xFE,0x0A,0xC6,0x00,0xE0,0x00,0xF0,0x00,0xF8,0x00,0x00,0x00,0x00,\n0x00,0x00,0xFE,0x7D,0xBB,0xC7,0xEF,0xEF,0xEF,0xEF,0xEF,0xEF,0xEF,0xC7,0xBB,0x7D,\n0xFE,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x08,\n0x0C,0xFE,0xFE,0x0C,0x08,0x20,0x60,0xFE,0xFE,0x60,0x20,0x00,0x00,0x00,0x78,0x48,\n0xFE,0x82,0xBA,0xBA,0x82,0xBA,0xBA,0x82,0xBA,0xBA,0x82,0xBA,0xBA,0x82,0xFE,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,0x01,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xFE,0xFF,\n0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0xFF,0xFF,0x00,0x00,0xFE,0xFF,0x03,\n0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0xFF,0xFE,0x00,0x00,0x00,0x00,0xC0,0xC0,\n0xC0,0x00,0x00,0x00,0x00,0xFE,0xFF,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,\n0xFF,0xFE,0x00,0x00,0xFE,0xFF,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0x03,0xFF,\n0xFE,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xFF,0xFF,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xFF,0xFF,0x00,0x00,0xFF,0xFF,0x0C,\n0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0xFF,0xFF,0x00,0x00,0x00,0x00,0xE1,0xE1,\n0xE1,0x00,0x00,0x00,0x00,0xFF,0xFF,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0xFF,0xFF,0x00,0x00,0xFF,0xFF,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0x0C,0xFF,\n0xFF,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x0F,0x1F,\n0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x1F,0x0F,0x00,0x00,0x0F,0x1F,0x18,\n0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x1F,0x0F,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x0F,0x1F,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,\n0x1F,0x0F,0x00,0x00,0x0F,0x1F,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x18,0x1F,\n0x0F,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0xE2,0x92,0x8A,0x86,0x00,0x00,0x7C,0x82,0x82,0x82,0x7C,\n0x00,0xFE,0x00,0x82,0x92,0xAA,0xC6,0x00,0x00,0xC0,0xC0,0x00,0x7C,0x82,0x82,0x82,\n0x7C,0x00,0x00,0x02,0x02,0x02,0xFE,0x00,0x00,0xC0,0xC0,0x00,0x7C,0x82,0x82,0x82,\n0x7C,0x00,0x00,0xFE,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x24,0xA4,0x2E,0x24,0xE4,0x24,0x2E,0xA4,0x24,0x00,0x00,0x00,0xF8,0x4A,0x4C,\n0x48,0xF8,0x48,0x4C,0x4A,0xF8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xC0,0x20,0x10,0x10,\n0x10,0x10,0x20,0xC0,0x00,0x00,0xC0,0x20,0x10,0x10,0x10,0x10,0x20,0xC0,0x00,0x00,\n0x00,0x12,0x0A,0x07,0x02,0x7F,0x02,0x07,0x0A,0x12,0x00,0x00,0x00,0x0B,0x0A,0x0A,\n0x0A,0x7F,0x0A,0x0A,0x0A,0x0B,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,\n0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x1F,0x20,0x40,0x40,\n0x40,0x50,0x20,0x5F,0x80,0x00,0x1F,0x20,0x40,0x40,0x40,0x50,0x20,0x5F,0x80,0x00,\n}; \n\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif \\/\\/ _IMGBMP_H_ _SSD1306_16BIT_H_\n"} ``` - Data construction Data source: Random sampling of pre-trained corpus Label construction: Design data scoring rules, use LLM model to perform multiple rounds of scoring, and select data with a difference of less than 2 in multiple rounds of scoring Data scale: 20k scoring data, Chinese and English ratio 1:1 Data scoring prompt ``` quality_prompt = """Below is an extract from a web page. Evaluate whether the page has a high natural language value and could be useful in an naturanl language task to train a good language model using the additive 5-point scoring system described below. Points are accumulated based on the satisfaction of each criterion: - Zero score if the content contains only some meaningless content or private content, such as some random code, http url or copyright information, personally identifiable information, binary encoding of images. - Add 1 point if the extract provides some basic information, even if it includes some useless contents like advertisements and promotional material. - Add another point if the extract is written in good style, semantically fluent, and free of repetitive content and grammatical errors. - Award a third point tf the extract has relatively complete semantic content, and is written in a good and fluent style, the entire content expresses something related to the same topic, rather than a patchwork of several unrelated items. - A fourth point is awarded if the extract has obvious educational or literary value, or provides a meaningful point or content, contributes to the learning of the topic, and is written in a clear and consistent style. It may be similar to a chapter in a textbook or tutorial, providing a lot of educational content, including exercises and solutions, with little to no superfluous information. The content is coherent and focused, which is valuable for structured learning. - A fifth point is awarded if the extract has outstanding educational value or is of very high information density, provides very high value and meaningful content, does not contain useless information, and is well suited for teaching or knowledge transfer. It contains detailed reasoning, has an easy-to-follow writing style, and can provide deep and thorough insights. The extract: <{EXAMPLE}>. After examining the extract: - Briefly justify your total score, up to 50 words. - Conclude with the score using the format: "Quality score: <total points>" ... """ ``` - Model training Model selection: Similar to the classification model, we also used a 0.5b scale model and compared beg-m3 and qwen-0.5b. The final experiment showed that bge-m3 had the best overall performance Model hyperparameters: base bge-m3, full parameter training, lr=1e-5, batch_size=64, max_length = 2048 Model evaluation: On the validation set, the consistency rate of the model and GPT4 in sample quality judgment was 90%.  - Training benefits from high-quality data In order to verify whether high-quality data can bring more efficient training efficiency, we extracted high-quality data from the 50b data before screening under the same base model. It can be considered that the distribution of the two data is roughly the same, and autoregressive training is performed. As can be seen from the curve, the 14B tokens of the model trained with high-quality data can achieve the performance of the model with 50B of ordinary data. High-quality data can greatly improve training efficiency.  In addition, high-quality data can be added to the model as data in the pre-training annealing stage to further improve the model effect. To verify this conjecture, when training the industry model, we added pre-training data converted from high-quality data after screening and some instruction data to the annealing stage of the model. It can be seen that the performance of the model has been greatly improved.  Finally, high-quality pre-training predictions contain a wealth of high-value knowledge content, from which instruction data can be extracted to further improve the richness and knowledge of instruction data. This also gave rise to the [BAAI/IndustryInstruction](https://huggingface.co/datasets/BAAI/IndustryInstruction) project, which we will explain in detail there. ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc {beijing_academy_of_artificial_intelligence, author= { Xiaofeng Shi and Lulu Zhao and Hua Zhou and Donglin Hao}, title = { IndustryCorpus2}, year = 2024, url = { https://huggingface.co/datasets/BAAI/IndustryCorpus2 }, doi = { 10.57967/hf/3488 }, publisher = { Hugging Face } } ```

Lichess/standard-chess-games

Lichess

--- license: cc0-1.0 pretty_name: Lichess Standard Rated Games dataset_info: features: - name: Event dtype: string - name: Site dtype: string - name: White dtype: string - name: Black dtype: string - name: Result dtype: string - name: WhiteTitle dtype: string - name: BlackTitle dtype: string - name: WhiteElo dtype: int16 - name: BlackElo dtype: int16 - name: WhiteRatingDiff dtype: int16 - name: BlackRatingDiff dtype: int16 - name: UTCDate dtype: date32 - name: UTCTime dtype: time32[s] - name: ECO dtype: string - name: Opening dtype: string - name: Termination dtype: string - name: TimeControl dtype: string - name: movetext dtype: string configs: - config_name: default data_files: - split: train path: data/*/*/* tags: - chess - games - game - lichess size_categories: - 1B<n<10B --- > [!CAUTION] > This dataset is still a work in progress and some breaking changes might occur. In the meantime, please use https://database.lichess.org/#standard_games > # Dataset Card for the Lichess Rated Standard Chess Games Dataset ## Dataset Description **6,298,645,464** standard rated games, played on [lichess.org](https://lichess.org), updated monthly from the [database dumps](https://database.lichess.org/#standard_games). This version of the data is meant for data analysis. If you need PGN files you can find those [here](https://database.lichess.org/#standard_games). That said, once you have a subset of interest, it is trivial to convert it back to PGN as shown in the [Dataset Usage](#dataset-usage) section. This dataset is hive-partitioned into multiple parquet files on two keys: `year` and `month`: ```bash . ├── data │   └── year=2015 │   ├── month=01 │   │   ├── train-00000-of-00003.parquet │   │   ├── train-00001-of-00003.parquet │   │   └── train-00002-of-00003.parquet │   ├── month=02 │   │   ├── train-00000-of-00003.parquet │   │   ├── train-00001-of-00003.parquet │   │   └── train-00002-of-00003.parquet │   ├── ... ``` ### Dataset Usage <!-- Using the `datasets` library: ```python from datasets import load_dataset dset = load_dataset("Lichess/chess-evaluations", split="train") ``` Using the `polars` library: Using DuckDB: Using `python-chess`: --> ## Dataset Details ### Dataset Sample <!-- One row of the dataset looks like this: ```python { "Event":, "Site":, } ``` --> ### Dataset Fields <!-- Every row of the dataset contains the following fields: - **`Event`**: `string`, - **`Site`**: `string`, --> ### Notes - About 6% of the games include Stockfish analysis evaluations: [%eval 2.35] (235 centipawn advantage), [%eval #-4] (getting mated in 4), always from White's point of view. - The WhiteElo and BlackElo tags contain Glicko2 ratings. - The `movetext` column contains clock information as PGN %clk comments since April 2017. - The schema doesn't include the `Date` header, typically part of the [Seven Tag Roster](https://en.wikipedia.org/wiki/Portable_Game_Notation#Seven_Tag_Roster) as we deemed the `UTCDate` field to be enough. - A future version of the data will include the addition of a `UCI` column containing the corresponding moves in [UCI format](https://en.wikipedia.org/wiki/Universal_Chess_Interface).

Graphcore/wikipedia-bert-128

Graphcore

--- language: - en license: - cc-by-sa-3.0 ---

deepghs/subsplease_animes

deepghs

--- license: other language: - en tags: - anime size_categories: - n<1K source_datasets: - myanimelist - nyaasi - subsplease --- This is an integration database of subsplease, myanimelist and nyaasi. You can know which animes are the hottest ones currently, and which of them have well-seeded magnet links. This database is refreshed daily. ## Current Animes 858 animes, 11282 episodes in total, Last updated on: `2025-01-21 07:59:42 UTC`. | ID | Post | Bangumi | Type | Episodes | Status | Score | Nyaasi | Magnets | Seeds | Downloads | Updated At | |------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:-----------|:--------------------|:--------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------|------------:|:-----------------| | 57334 | [](https://myanimelist.net/anime/57334/Dandadan) | [Dandadan](https://subsplease.org/shows/dandadan) | TV | 12 / 12 | **Finished Airing** | 8.59 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dandadan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57334__dandadan.txt) | **908** | 50977 | 2024-12-19 16:01 | | 57592 | [](https://myanimelist.net/anime/57592/Dr_Stone__Science_Future) | [Dr. Stone S4](https://subsplease.org/shows/dr-stone-s4) | TV | 2 / 12 | Currently Airing | 8.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dr+Stone+S4+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57592__dr_stone_science_future.txt) | **802** | 14308 | 2025-01-16 15:01 | | 59514 | [](https://myanimelist.net/anime/59514/Sentai_Red_Isekai_de_Boukensha_ni_Naru) | [Sentai Red Isekai de Boukensha ni Naru](https://subsplease.org/shows/sentai-red-isekai-de-boukensha-ni-naru) | TV | 2 / 12 | Currently Airing | 6.8 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sentai+Red+Isekai+de+Boukensha+ni+Naru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59514__sentai_red_isekai_de_boukensha_ni_naru.txt) | **606** | 10948 | 2025-01-19 16:02 | | 58502 | [](https://myanimelist.net/anime/58502/Zenshuu) | [Zenshuu](https://subsplease.org/shows/zenshuu) | TV | 3 / 12 | Currently Airing | 7.29 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Zenshuu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58502__zenshuu.txt) | **594** | 12996 | 2025-01-19 17:17 | | 57719 | [](https://myanimelist.net/anime/57719/Akuyaku_Reijou_Tensei_Ojisan) | [Akuyaku Reijou Tensei Ojisan](https://subsplease.org/shows/akuyaku-reijou-tensei-ojisan) | TV | 2 / 12 | Currently Airing | 7.58 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Akuyaku+Reijou+Tensei+Ojisan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57719__akuyaku_reijou_tensei_ojisan.txt) | **559** | 12515 | 2025-01-16 16:04 | | 59730 | [](https://myanimelist.net/anime/59730/A-Rank_Party_wo_Ridatsu_shita_Ore_wa_Moto_Oshiego-tachi_to_Meikyuu_Shinbu_wo_Mezasu) | [Aparida](https://subsplease.org/shows/aparida) | TV | 2 / 24 | Currently Airing | 6.92 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Aparida+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59730__a_rank_party_wo_ridatsu_shita_ore_wa_moto_oshiego_tachi_to_meikyuu_shinbu_wo_mezasu.txt) | **522** | 11092 | 2025-01-18 17:47 | | 57648 | [](https://myanimelist.net/anime/57648/Nihon_e_Youkoso_Elf-san) | [Nihon e Youkoso Elf-san](https://subsplease.org/shows/nihon-e-youkoso-elf-san) | TV | 2 / 12 | Currently Airing | 7.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Nihon+e+Youkoso+Elf+san+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57648__nihon_e_youkoso_elf_san.txt) | **502** | 10760 | 2025-01-17 15:02 | | 58600 | [](https://myanimelist.net/anime/58600/Ameku_Takao_no_Suiri_Karte) | [Ameku Takao no Suiri Karte](https://subsplease.org/shows/ameku-takao-no-suiri-karte) | TV | 3 / 12 | Currently Airing | 7.4 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ameku+Takao+no+Suiri+Karte+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58600__ameku_takao_no_suiri_karte.txt) | **477** | 12174 | 2025-01-08 18:02 | | 59144 | [](https://myanimelist.net/anime/59144/Fuguushoku_Kanteishi_ga_Jitsu_wa_Saikyou_Datta) | [Fugukan](https://subsplease.org/shows/fugukan) | TV | 2 / 12 | Currently Airing | 6.6 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fugukan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59144__fuguushoku_kanteishi_ga_jitsu_wa_saikyou_datta.txt) | **470** | 11246 | 2025-01-16 15:47 | | 58822 | [](https://myanimelist.net/anime/58822/Izure_Saikyou_no_Renkinjutsushi) | [Izure Saikyou no Renkinjutsushi](https://subsplease.org/shows/izure-saikyou-no-renkinjutsushi) | TV | 3 / 12 | Currently Airing | 6.85 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Izure+Saikyou+no+Renkinjutsushi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58822__izure_saikyou_no_renkinjutsushi.txt) | **469** | 13336 | 2025-01-15 16:02 | | 59349 | [](https://myanimelist.net/anime/59349/Salaryman_ga_Isekai_ni_Ittara_Shitennou_ni_Natta_Hanashi) | [Salaryman ga Isekai ni Ittara Shitennou ni Natta Hanashi](https://subsplease.org/shows/salaryman-ga-isekai-ni-ittara-shitennou-ni-natta-hanashi) | TV | 4 / 12 | Currently Airing | 6.53 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Salaryman+ga+Isekai+ni+Ittara+Shitennou+ni+Natta+Hanashi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59349__salaryman_ga_isekai_ni_ittara_shitennou_ni_natta_hanashi.txt) | **462** | 12053 | 2025-01-20 15:47 | | 59561 | [](https://myanimelist.net/anime/59561/Around_40_Otoko_no_Isekai_Tsuuhan) | [Around 40 Otoko no Isekai Tsuuhan](https://subsplease.org/shows/around-40-otoko-no-isekai-tsuuhan) | TV | 2 / 13 | Currently Airing | 6.83 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Around+40+Otoko+no+Isekai+Tsuuhan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59561__around_40_otoko_no_isekai_tsuuhan.txt) | **443** | 11410 | 2025-01-16 13:48 | | 59135 | [](https://myanimelist.net/anime/59135/Class_no_Daikirai_na_Joshi_to_Kekkon_suru_Koto_ni_Natta) | [Class no Daikirai na Joshi to Kekkon suru Koto ni Natta](https://subsplease.org/shows/class-no-daikirai-na-joshi-to-kekkon-suru-koto-ni-natta) | TV | 3 / 12 | Currently Airing | 7.18 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Class+no+Daikirai+na+Joshi+to+Kekkon+suru+Koto+ni+Natta+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59135__class_no_daikirai_na_joshi_to_kekkon_suru_koto_ni_natta.txt) | **439** | 10820 | 2025-01-17 17:02 | | 59265 | [](https://myanimelist.net/anime/59265/Magic_Maker__Isekai_Mahou_no_Tsukurikata) | [Magic Maker - Isekai Mahou no Tsukurikata](https://subsplease.org/shows/magic-maker-isekai-mahou-no-tsukurikata) | TV | 2 / 12 | Currently Airing | 6.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Magic+Maker+Isekai+Mahou+no+Tsukurikata+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59265__magic_maker_isekai_mahou_no_tsukurikata.txt) | **439** | 10860 | 2025-01-15 17:01 | | 59002 | [](https://myanimelist.net/anime/59002/Hazure_Skill_Kinomi_Master__Skill_no_Mi_Tabetara_Shinu_wo_Mugen_ni_Taberareru_You_ni_Natta_Ken_ni_Tsuite) | [Kinomi Master](https://subsplease.org/shows/kinomi-master) | TV | 3 / 12 | Currently Airing | 6.18 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kinomi+Master+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59002__hazure_skill_kinomi_master_skill_no_mi_tabetara_shinu_wo_mugen_ni_taberareru_you_ni_natta_ken_ni_tsuite.txt) | **437** | 13258 | 2025-01-14 16:16 | | 58437 | [](https://myanimelist.net/anime/58437/Botsuraku_Yotei_no_Kizoku_dakedo_Hima_Datta_kara_Mahou_wo_Kiwametemita) | [Botsuraku Yotei no Kizoku dakedo, Hima Datta kara Mahou wo Kiwametemita](https://subsplease.org/shows/botsuraku-yotei-no-kizoku-dakedo-hima-datta-kara-mahou-wo-kiwametemita) | TV | 4 / ? | Currently Airing | 6.44 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Botsuraku+Yotei+no+Kizoku+dakedo+Hima+Datta+kara+Mahou+wo+Kiwametemita+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58437__botsuraku_yotei_no_kizoku_dakedo_hima_datta_kara_mahou_wo_kiwametemita.txt) | **420** | 11110 | 2025-01-20 18:17 | | 58473 | [](https://myanimelist.net/anime/58473/S-Rank_Monster_no_Behemoth_dakedo_Neko_to_Machigawarete_Elf_Musume_no_Pet_toshite_Kurashitemasu) | [Beheneko](https://subsplease.org/shows/beheneko) | TV | 4 / 12 | Currently Airing | 6.16 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Beheneko+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58473__s_rank_monster_no_behemoth_dakedo_neko_to_machigawarete_elf_musume_no_pet_toshite_kurashitemasu.txt) | **414** | 11978 | 2025-01-18 14:02 | | 55830 | [](https://myanimelist.net/anime/55830/Fate_strange_Fake) | [Fate Strange Fake](https://subsplease.org/shows/fate-strange-fake) | TV | 1 / ? | **Not yet aired** | N/A | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fate+Strange+Fake+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55830__fate_strange_fake.txt) | **399** | 14836 | 2024-12-31 14:02 | | 58853 | [](https://myanimelist.net/anime/58853/Kuroiwa_Medaka_ni_Watashi_no_Kawaii_ga_Tsuujinai) | [Kuroiwa Medaka ni Watashi no Kawaii ga Tsuujinai](https://subsplease.org/shows/kuroiwa-medaka-ni-watashi-no-kawaii-ga-tsuujinai) | TV | 3 / 12 | Currently Airing | 6.61 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kuroiwa+Medaka+ni+Watashi+no+Kawaii+ga+Tsuujinai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58853__kuroiwa_medaka_ni_watashi_no_kawaii_ga_tsuujinai.txt) | **380** | 7997 | 2025-01-20 17:02 | | 57066 | [](https://myanimelist.net/anime/57066/Dungeon_ni_Deai_wo_Motomeru_no_wa_Machigatteiru_Darou_ka_V__Houjou_no_Megami-hen) | [Dungeon ni Deai wo Motomeru no wa Machigatteiru Darou ka S5](https://subsplease.org/shows/dungeon-ni-deai-wo-motomeru-no-wa-machigatteiru-darou-ka-s5) | TV | 11 / 15 | Currently Airing | 8.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dungeon+ni+Deai+wo+Motomeru+no+wa+Machigatteiru+Darou+ka+S5+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57066__dungeon_ni_deai_wo_motomeru_no_wa_machigatteiru_darou_ka_v_houjou_no_megami_hen.txt) | **366** | 20556 | 2024-12-19 13:02 | | 58082 | [](https://myanimelist.net/anime/58082/NEET_Kunoichi_to_Nazeka_Dousei_Hajimemashita) | [NEET Kunoichi to Nazeka Dousei Hajimemashita](https://subsplease.org/shows/neet-kunoichi-to-nazeka-dousei-hajimemashita) | TV | 3 / 24 | Currently Airing | 6.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+NEET+Kunoichi+to+Nazeka+Dousei+Hajimemashita+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58082__neet_kunoichi_to_nazeka_dousei_hajimemashita.txt) | **350** | 8984 | 2025-01-18 16:32 | | 59226 | [](https://myanimelist.net/anime/59226/Ao_no_Exorcist__Yosuga-hen) | [Ao no Exorcist - Yosuga-hen](https://subsplease.org/shows/ao-no-exorcist-yosuga-hen) | TV | 3 / 12 | Currently Airing | 7.71 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ao+no+Exorcist+Yosuga+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59226__ao_no_exorcist_yosuga_hen.txt) | **343** | 6986 | 2025-01-18 18:02 | | 55842 | [](https://myanimelist.net/anime/55842/Okinawa_de_Suki_ni_Natta_Ko_ga_Hougen_Sugite_Tsurasugiru) | [Okitsura](https://subsplease.org/shows/okitsura) | TV | 3 / 12 | Currently Airing | 6.88 | 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[Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57152__mahoutsukai_no_yakusoku.txt) | **256** | 5610 | 2025-01-20 15:32 | | 58426 | [](https://myanimelist.net/anime/58426/Shikanoko_Nokonoko_Koshitantan) | [Shikanoko Nokonoko Koshitantan](https://subsplease.org/shows/shikanoko-nokonoko-koshitantan) | TV | 12 / 12 | **Finished Airing** | 7.02 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shikanoko+Nokonoko+Koshitantan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58426__shikanoko_nokonoko_koshitantan.txt) | **250** | 15044 | 2024-09-18 14:31 | | 52299 | [](https://myanimelist.net/anime/52299/Ore_dake_Level_Up_na_Ken) | [Solo Leveling](https://subsplease.org/shows/solo-leveling) | TV | 16 / 12 | **Finished Airing** | 8.27 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Solo+Leveling+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52299__ore_dake_level_up_na_ken.txt) | **248** | 53657 | 2025-01-18 17:32 | | 55994 | [](https://myanimelist.net/anime/55994/Sword_Art_Online_Alternative__Gun_Gale_Online_II) | [Sword Art Online Alternative - Gun Gale Online S2](https://subsplease.org/shows/sword-art-online-alternative-gun-gale-online-s2) | TV | 12 / 12 | **Finished Airing** | 7.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sword+Art+Online+Alternative+Gun+Gale+Online+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55994__sword_art_online_alternative_gun_gale_online_ii.txt) | **239** | 13319 | 2024-12-20 17:32 | | 49458 | [](https://myanimelist.net/anime/49458/Kono_Subarashii_Sekai_ni_Shukufuku_wo_3) | [Kono Subarashii Sekai ni Shukufuku wo! S3](https://subsplease.org/shows/kono-subarashii-sekai-ni-shukufuku-wo-s3) | TV | 11 / 11 | **Finished Airing** | 8.36 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kono+Subarashii+Sekai+ni+Shukufuku+wo+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49458__kono_subarashii_sekai_ni_shukufuku_wo_3.txt) | **238** | 31432 | 2024-06-19 15:01 | | 58739 | [](https://myanimelist.net/anime/58739/Momentary_Lily) | [Momentary Lily](https://subsplease.org/shows/momentary-lily) | TV | 3 / 13 | Currently Airing | 5.79 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Momentary+Lily+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58739__momentary_lily.txt) | **237** | 6450 | 2025-01-16 17:38 | | 54744 | [](https://myanimelist.net/anime/54744/Tokidoki_Bosotto_Russia-go_de_Dereru_Tonari_no_Alya-san) | [Tokidoki Bosotto Russia-go de Dereru Tonari no Alya-san](https://subsplease.org/shows/tokidoki-bosotto-russia-go-de-dereru-tonari-no-alya-san) | TV | 12 / 12 | **Finished Airing** | 7.61 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tokidoki+Bosotto+Russia+go+de+Dereru+Tonari+no+Alya+san+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54744__tokidoki_bosotto_russia_go_de_dereru_tonari_no_alya_san.txt) | **230** | 24690 | 2024-09-18 15:02 | | 51119 | [](https://myanimelist.net/anime/51119/Grisaia__Phantom_Trigger) | [Grisaia - Phantom Trigger](https://subsplease.org/shows/grisaia-phantom-trigger) | TV | 3 / 13 | Currently Airing | 6.69 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Grisaia+Phantom+Trigger+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51119__grisaia_phantom_trigger.txt) | **230** | 6486 | 2025-01-15 16:32 | | 59113 | [](https://myanimelist.net/anime/59113/Farmagia) | [Farmagia](https://subsplease.org/shows/farmagia) | TV | 2 / 12 | Currently Airing | 5.5 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Farmagia+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59113__farmagia.txt) | **224** | 5774 | 2025-01-17 15:32 | | 59989 | [](https://myanimelist.net/anime/59989/Kami_no_Tou__Koubou-sen) | [Tower of God S2](https://subsplease.org/shows/tower-of-god-s2) | TV | 26 / 13 | **Finished Airing** | 6.9 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tower+of+God+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59989__kami_no_tou_koubou_sen.txt) | **224** | 16162 | 2024-12-29 15:02 | | 53924 | [](https://myanimelist.net/anime/53924/Jibaku_Shounen_Hanako-kun_2) | [Jibaku Shounen Hanako-kun S2](https://subsplease.org/shows/jibaku-shounen-hanako-kun-s2) | TV | 2 / 12 | Currently Airing | 7.89 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jibaku+Shounen+Hanako+kun+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53924__jibaku_shounen_hanako_kun_2.txt) | **220** | 3289 | 2025-01-19 10:02 | | 57524 | [](https://myanimelist.net/anime/57524/Make_Heroine_ga_Oosugiru) | [Make Heroine ga Oosugiru!](https://subsplease.org/shows/make-heroine-ga-oosugiru) | TV | 12 / 12 | **Finished Airing** | 8.16 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Make+Heroine+ga+Oosugiru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57524__make_heroine_ga_oosugiru.txt) | **218** | 20272 | 2024-09-28 17:02 | | 56894 | [](https://myanimelist.net/anime/56894/Dragon_Ball_Daima) | [Dragon Ball Daima](https://subsplease.org/shows/dragon-ball-daima) | TV | 14 / ? | Currently Airing | 7.8 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dragon+Ball+Daima+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56894__dragon_ball_daima.txt) | **210** | 14437 | 2025-01-17 16:46 | | 54853 | [](https://myanimelist.net/anime/54853/Maou_2099) | [Maou 2099](https://subsplease.org/shows/maou-2099) | TV | 12 / 12 | **Finished Airing** | 7.49 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Maou+2099+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54853__maou_2099.txt) | **210** | 13346 | 2024-12-28 17:31 | | 57050 | [](https://myanimelist.net/anime/57050/Kisaki_Kyouiku_kara_Nigetai_Watashi) | [Kisaki Kyouiku kara Nigetai Watashi](https://subsplease.org/shows/kisaki-kyouiku-kara-nigetai-watashi) | TV | 3 / 12 | Currently Airing | 6.12 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kisaki+Kyouiku+kara+Nigetai+Watashi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57050__kisaki_kyouiku_kara_nigetai_watashi.txt) | **206** | 5177 | 2025-01-19 14:17 | | 51122 | [](https://myanimelist.net/anime/51122/Ookami_to_Koushinryou__Merchant_Meets_the_Wise_Wolf) | [Spice and Wolf (2024)](https://subsplease.org/shows/spice-and-wolf-2024) | TV | 25 / 25 | **Finished Airing** | 8.11 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Spice+and+Wolf+2024+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51122__ookami_to_koushinryou_merchant_meets_the_wise_wolf.txt) | **206** | 19466 | 2024-09-23 18:03 | | 60022 | [](https://myanimelist.net/anime/60022/One_Piece_Fan_Letter) | [One Piece Fan Letter](https://subsplease.org/shows/one-piece-fan-letter) | TV Special | 1 / 1 | **Finished Airing** | 9.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+One+Piece+Fan+Letter+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/60022__one_piece_fan_letter.txt) | **200** | 14512 | 2024-10-20 17:06 | | 58059 | [](https://myanimelist.net/anime/58059/Tsue_to_Tsurugi_no_Wistoria) | [Tsue to Tsurugi no Wistoria](https://subsplease.org/shows/tsue-to-tsurugi-no-wistoria) | TV | 12 / 12 | **Finished Airing** | 7.89 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tsue+to+Tsurugi+no+Wistoria+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58059__tsue_to_tsurugi_no_wistoria.txt) | **194** | 22157 | 2024-09-29 09:32 | | 56653 | [](https://myanimelist.net/anime/56653/BanG_Dream_Ave_Mujica) | [BanG Dream! Ave Mujica](https://subsplease.org/shows/bang-dream-ave-mujica) | TV | 3 / 13 | Currently Airing | 8.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+BanG+Dream+Ave+Mujica+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56653__bang_dream_ave_mujica.txt) | **188** | 4279 | 2025-01-16 16:01 | | 58172 | [](https://myanimelist.net/anime/58172/Nageki_no_Bourei_wa_Intai_shitai) | [Nageki no Bourei wa Intai shitai](https://subsplease.org/shows/nageki-no-bourei-wa-intai-shitai) | TV | 13 / 13 | **Finished Airing** | 7.08 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Nageki+no+Bourei+wa+Intai+shitai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58172__nageki_no_bourei_wa_intai_shitai.txt) | **187** | 13089 | 2024-12-22 16:32 | | 57891 | [](https://myanimelist.net/anime/57891/Hitoribocchi_no_Isekai_Kouryaku) | [Hitoribocchi no Isekai Kouryaku](https://subsplease.org/shows/hitoribocchi-no-isekai-kouryaku) | TV | 12 / 12 | **Finished Airing** | 6.58 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hitoribocchi+no+Isekai+Kouryaku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57891__hitoribocchi_no_isekai_kouryaku.txt) | **180** | 13586 | 2024-12-12 15:03 | | 53888 | [](https://myanimelist.net/anime/53888/Spy_x_Family_Movie__Code__White) | [Spy x Family - Code White](https://subsplease.org/shows/spy-x-family-code-white) | Movie | 1 / 1 | **Finished Airing** | 8.14 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Spy+x+Family+Code+White+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53888__spy_x_family_movie_code_white.txt) | **177** | 12266 | 2024-09-07 05:51 | | 55150 | [](https://myanimelist.net/anime/55150/Yarinaoshi_Reijou_wa_Ryuutei_Heika_wo_Kouryakuchuu) | [Yarinaoshi Reijou wa Ryuutei Heika wo Kouryakuchuu](https://subsplease.org/shows/yarinaoshi-reijou-wa-ryuutei-heika-wo-kouryakuchuu) | TV | 12 / 12 | **Finished Airing** | 7.05 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yarinaoshi+Reijou+wa+Ryuutei+Heika+wo+Kouryakuchuu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55150__yarinaoshi_reijou_wa_ryuutei_heika_wo_kouryakuchuu.txt) | **170** | 9806 | 2024-12-25 14:32 | | 50306 | [](https://myanimelist.net/anime/50306/Seirei_Gensouki_2) | [Seirei Gensouki S2](https://subsplease.org/shows/seirei-gensouki-s2) | TV | 12 / 12 | **Finished Airing** | 6.87 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Seirei+Gensouki+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50306__seirei_gensouki_2.txt) | **170** | 10374 | 2024-12-23 18:32 | | 58066 | [](https://myanimelist.net/anime/58066/Sorairo_Utility_TV) | [Sorairo Utility](https://subsplease.org/shows/sorairo-utility) | TV | 4 / 12 | Currently Airing | 6.79 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sorairo+Utility+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58066__sorairo_utility_tv.txt) | **169** | 4508 | 2025-01-17 17:02 | | 57944 | [](https://myanimelist.net/anime/57944/Party_kara_Tsuihou_sareta_Sono_Chiyushi_Jitsu_wa_Saikyou_ni_Tsuki) | [Party kara Tsuihou sareta Sono Chiyushi, Jitsu wa Saikyou ni Tsuki](https://subsplease.org/shows/party-kara-tsuihou-sareta-sono-chiyushi-jitsu-wa-saikyou-ni-tsuki) | TV | 12 / 12 | **Finished Airing** | 5.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Party+kara+Tsuihou+sareta+Sono+Chiyushi+Jitsu+wa+Saikyou+ni+Tsuki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57944__party_kara_tsuihou_sareta_sono_chiyushi_jitsu_wa_saikyou_ni_tsuki.txt) | **168** | 11443 | 2024-12-21 19:32 | | 57611 | [](https://myanimelist.net/anime/57611/Kimi_wa_Meido-sama) | [Kimi wa Meido-sama](https://subsplease.org/shows/kimi-wa-meido-sama) | TV | 12 / 12 | **Finished Airing** | 7.08 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kimi+wa+Meido+sama+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57611__kimi_wa_meido_sama.txt) | **160** | 9901 | 2024-12-21 19:47 | | 57864 | [](https://myanimelist.net/anime/57864/Monogatari_Series__Off___Monster_Season) | [Monogatari Series - Off & Monster Season](https://subsplease.org/shows/monogatari-series-off-monster-season) | ONA | 15 / 14 | **Finished Airing** | 8.71 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Monogatari+Series+Off+Monster+Season+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57864__monogatari_series_off_monster_season.txt) | **156** | 12783 | 2024-10-19 14:32 | | 56228 | [](https://myanimelist.net/anime/56228/Rekishi_ni_Nokoru_Akujo_ni_Naru_zo) | [Rekishi ni Nokoru Akujo ni Naru zo](https://subsplease.org/shows/rekishi-ni-nokoru-akujo-ni-naru-zo) | TV | 13 / 13 | **Finished Airing** | 7.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Rekishi+ni+Nokoru+Akujo+ni+Naru+zo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56228__rekishi_ni_nokoru_akujo_ni_naru_zo.txt) | **156** | 9925 | 2024-12-24 16:02 | | 52588 | [](https://myanimelist.net/anime/52588/Kaijuu_8-gou) | [Kaijuu 8-gou](https://subsplease.org/shows/kaijuu-8-gou) | TV | 12 / 12 | **Finished Airing** | 8.3 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kaijuu+8+gou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52588__kaijuu_8_gou.txt) | **154** | 35930 | 2024-06-29 14:31 | | 52034 | [](https://myanimelist.net/anime/52034/Oshi_no_Ko) | [Oshi no Ko](https://subsplease.org/shows/oshi-no-ko) | TV | 25 / 11 | **Finished Airing** | 8.59 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Oshi+no+Ko+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52034__oshi_no_ko.txt) | **153** | 38562 | 2024-10-06 11:02 | | 52367 | [](https://myanimelist.net/anime/52367/Isekai_Shikkaku) | [Isekai Shikkaku](https://subsplease.org/shows/isekai-shikkaku) | TV | 12 / 12 | **Finished Airing** | 7.28 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+Shikkaku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52367__isekai_shikkaku.txt) | **152** | 16367 | 2024-09-24 15:03 | | 58714 | [](https://myanimelist.net/anime/58714/Saikyou_no_Shienshoku_Wajutsushi_de_Aru_Ore_wa_Sekai_Saikyou_Clan_wo_Shitagaeru) | [Wajutsushi](https://subsplease.org/shows/wajutsushi) | TV | 12 / 12 | **Finished Airing** | 7.63 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Wajutsushi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58714__saikyou_no_shienshoku_wajutsushi_de_aru_ore_wa_sekai_saikyou_clan_wo_shitagaeru.txt) | **152** | 10824 | 2024-12-16 17:02 | | 54724 | [](https://myanimelist.net/anime/54724/Nige_Jouzu_no_Wakagimi) | [Nige Jouzu no Wakagimi](https://subsplease.org/shows/nige-jouzu-no-wakagimi) | TV | 13 / 12 | **Finished Airing** | 7.82 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Nige+Jouzu+no+Wakagimi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54724__nige_jouzu_no_wakagimi.txt) | **148** | 13549 | 2024-09-28 16:02 | | 56752 | [](https://myanimelist.net/anime/56752/Shiguang_Dailiren__Yingdu_Pian) | [Link Click - Bridon Arc](https://subsplease.org/shows/link-click-bridon-arc) | ONA | 4 / 6 | Currently Airing | 8.72 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Link+Click+Bridon+Arc+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56752__shiguang_dailiren_yingdu_pian.txt) | **146** | 3544 | 2025-01-17 05:02 | | 58445 | [](https://myanimelist.net/anime/58445/Sayounara_Ryuusei_Konnichiwa_Jinsei) | [Sayounara Ryuusei, Konnichiwa Jinsei](https://subsplease.org/shows/sayounara-ryuusei-konnichiwa-jinsei) | TV | 12 / 12 | **Finished Airing** | 6.35 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sayounara+Ryuusei+Konnichiwa+Jinsei+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58445__sayounara_ryuusei_konnichiwa_jinsei.txt) | **146** | 10466 | 2024-12-19 18:17 | | 57058 | [](https://myanimelist.net/anime/57058/Ore_wa_Subete_wo_Parry_suru__Gyaku_Kanchigai_no_Sekai_Saikyou_wa_Boukensha_ni_Naritai) | [Ore wa Subete wo Parry suru](https://subsplease.org/shows/ore-wa-subete-wo-parry-suru) | TV | 12 / 12 | **Finished Airing** | 6.91 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ore+wa+Subete+wo+Parry+suru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57058__ore_wa_subete_wo_parry_suru_gyaku_kanchigai_no_sekai_saikyou_wa_boukensha_ni_naritai.txt) | **143** | 20480 | 2024-09-19 15:01 | | 58259 | [](https://myanimelist.net/anime/58259/Douse_Koishite_Shimaunda) | [Douse, Koishite Shimaunda](https://subsplease.org/shows/douse-koishite-shimaunda) | TV | 2 / 12 | Currently Airing | 6.36 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Douse+Koishite+Shimaunda+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58259__douse_koishite_shimaunda.txt) | **138** | 3652 | 2025-01-16 18:23 | | 55071 | [](https://myanimelist.net/anime/55071/Amagami-san_Chi_no_Enmusubi) | [Amagami-san Chi no Enmusubi](https://subsplease.org/shows/amagami-san-chi-no-enmusubi) | TV | 14 / 24 | Currently Airing | 7.09 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Amagami+san+Chi+no+Enmusubi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55071__amagami_san_chi_no_enmusubi.txt) | **137** | 6728 | 2025-01-14 16:31 | | 55888 | [](https://myanimelist.net/anime/55888/Mushoku_Tensei_II__Isekai_Ittara_Honki_Dasu_Part_2) | [Mushoku Tensei S2](https://subsplease.org/shows/mushoku-tensei-s2) | TV | 25 / 12 | **Finished Airing** | 8.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mushoku+Tensei+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55888__mushoku_tensei_ii_isekai_ittara_honki_dasu_part_2.txt) | **130** | 40056 | 2024-06-30 15:32 | | 56964 | [](https://myanimelist.net/anime/56964/Raise_wa_Tanin_ga_Ii) | [Raise wa Tanin ga Ii](https://subsplease.org/shows/raise-wa-tanin-ga-ii) | TV | 12 / 12 | **Finished Airing** | 7.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Raise+wa+Tanin+ga+Ii+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56964__raise_wa_tanin_ga_ii.txt) | **128** | 6360 | 2024-12-23 15:57 | | 54968 | [](https://myanimelist.net/anime/54968/Giji_Harem) | [Giji Harem](https://subsplease.org/shows/giji-harem) | TV | 12 / 12 | **Finished Airing** | 7.85 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Giji+Harem+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54968__giji_harem.txt) | **128** | 10545 | 2024-09-19 16:32 | | 55887 | [](https://myanimelist.net/anime/55887/Kekkon_suru_tte_Hontou_desu_ka) | [Kekkon suru tte, Hontou desu ka](https://subsplease.org/shows/kekkon-suru-tte-hontou-desu-ka) | TV | 12 / 12 | **Finished Airing** | 7.13 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kekkon+suru+tte+Hontou+desu+ka+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55887__kekkon_suru_tte_hontou_desu_ka.txt) | **126** | 7543 | 2024-12-19 16:32 | | 53802 | [](https://myanimelist.net/anime/53802/25-jigen_no_Ririsa) | [2.5-jigen no Ririsa](https://subsplease.org/shows/2-5-jigen-no-ririsa) | TV | 24 / 24 | **Finished Airing** | 7.42 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+2+5+jigen+no+Ririsa+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53802__2_5_jigen_no_ririsa.txt) | **120** | 9724 | 2024-12-13 13:32 | | 55265 | [](https://myanimelist.net/anime/55265/Tensei_Kizoku_Kantei_Skill_de_Nariagaru) | [Tensei Kizoku, Kantei Skill de Nariagaru](https://subsplease.org/shows/tensei-kizoku-kantei-skill-de-nariagaru) | TV | 24 / 12 | **Finished Airing** | 7.16 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tensei+Kizoku+Kantei+Skill+de+Nariagaru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55265__tensei_kizoku_kantei_skill_de_nariagaru.txt) | **119** | 14160 | 2024-12-22 16:17 | | 52481 | [](https://myanimelist.net/anime/52481/Gimai_Seikatsu) | [Gimai Seikatsu](https://subsplease.org/shows/gimai-seikatsu) | TV | 12 / 12 | **Finished Airing** | 7.35 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gimai+Seikatsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52481__gimai_seikatsu.txt) | **117** | 11828 | 2024-09-19 12:32 | | 58516 | [](https://myanimelist.net/anime/58516/Ao_no_Exorcist__Yuki_no_Hate-hen) | [Ao no Exorcist - Yuki no Hate-hen](https://subsplease.org/shows/ao-no-exorcist-yuki-no-hate-hen) | TV | 12 / 12 | **Finished Airing** | 7.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ao+no+Exorcist+Yuki+no+Hate+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58516__ao_no_exorcist_yuki_no_hate_hen.txt) | **117** | 7419 | 2024-12-21 18:02 | | 54722 | [](https://myanimelist.net/anime/54722/Mahou_Shoujo_ni_Akogarete) | [Mahou Shoujo ni Akogarete](https://subsplease.org/shows/mahou-shoujo-ni-akogarete) | TV | 13 / 13 | **Finished Airing** | 7.64 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahou+Shoujo+ni+Akogarete+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54722__mahou_shoujo_ni_akogarete.txt) | **115** | 21299 | 2024-03-27 16:03 | | 54492 | [](https://myanimelist.net/anime/54492/Kusuriya_no_Hitorigoto) | [Kusuriya no Hitorigoto](https://subsplease.org/shows/kusuriya-no-hitorigoto) | TV | 26 / 24 | **Finished Airing** | 8.88 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kusuriya+no+Hitorigoto+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54492__kusuriya_no_hitorigoto.txt) | **114** | 26761 | 2025-01-17 17:17 | | 54913 | [](https://myanimelist.net/anime/54913/Shinmai_Ossan_Boukensha_Saikyou_Party_ni_Shinu_hodo_Kitaerarete_Muteki_ni_Naru) | [Shinmai Ossan Boukensha](https://subsplease.org/shows/shinmai-ossan-boukensha) | TV | 12 / 12 | **Finished Airing** | 7.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shinmai+Ossan+Boukensha+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54913__shinmai_ossan_boukensha_saikyou_party_ni_shinu_hodo_kitaerarete_muteki_ni_naru.txt) | **114** | 15496 | 2024-09-23 17:31 | | 54595 | [](https://myanimelist.net/anime/54595/Kage_no_Jitsuryokusha_ni_Naritakute_2nd_Season) | [Kage no Jitsuryokusha ni Naritakute! S2](https://subsplease.org/shows/kage-no-jitsuryokusha-ni-naritakute-s2) | TV | 12 / 12 | **Finished Airing** | 8.31 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kage+no+Jitsuryokusha+ni+Naritakute+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54595__kage_no_jitsuryokusha_ni_naritakute_2nd_season.txt) | **112** | 37333 | 2023-12-20 14:31 | | 54769 | [](https://myanimelist.net/anime/54769/Sousei_no_Aquarion__Myth_of_Emotions) | [Sousei no Aquarion - Myth of Emotions](https://subsplease.org/shows/sousei-no-aquarion-myth-of-emotions) | TV | 2 / 12 | Currently Airing | 5.69 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sousei+no+Aquarion+Myth+of+Emotions+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54769__sousei_no_aquarion_myth_of_emotions.txt) | **111** | 3268 | 2025-01-16 21:50 | | 57892 | [](https://myanimelist.net/anime/57892/Hazurewaku_no_Joutai_Ijou_Skill_de_Saikyou_ni_Natta_Ore_ga_Subete_wo_Juurin_suru_made) | [Hazurewaku](https://subsplease.org/shows/hazurewaku) | TV | 12 / 12 | **Finished Airing** | 6.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hazurewaku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57892__hazurewaku_no_joutai_ijou_skill_de_saikyou_ni_natta_ore_ga_subete_wo_juurin_suru_made.txt) | **109** | 16326 | 2024-09-26 18:08 | | 49889 | [](https://myanimelist.net/anime/49889/Tsuki_ga_Michibiku_Isekai_Douchuu_2nd_Season) | [Tsuki ga Michibiku Isekai Douchuu S2](https://subsplease.org/shows/tsuki-ga-michibiku-isekai-douchuu-s2) | TV | 25 / 25 | **Finished Airing** | 7.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tsuki+ga+Michibiku+Isekai+Douchuu+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49889__tsuki_ga_michibiku_isekai_douchuu_2nd_season.txt) | **104** | 20204 | 2024-06-24 15:02 | | 54726 | [](https://myanimelist.net/anime/54726/Tsuma_Shougakusei_ni_Naru) | [Tsuma, Shougakusei ni Naru](https://subsplease.org/shows/tsuma-shougakusei-ni-naru) | TV | 12 / 12 | **Finished Airing** | 7.52 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tsuma+Shougakusei+ni+Naru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54726__tsuma_shougakusei_ni_naru.txt) | **104** | 5908 | 2024-12-15 14:47 | | 53033 | [](https://myanimelist.net/anime/53033/Mecha-ude_TV) | [Mecha-ude](https://subsplease.org/shows/mecha-ude) | TV | 12 / 12 | **Finished Airing** | 6.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mecha+ude+1080p+mkv) | 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[Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+VTuber+Nandaga+Haishin+Kiri+Wasuretara+Densetsu+ni+Natteta+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54284__vtuber_nandaga_haishin_kiri_wasuretara_densetsu_ni_natteta.txt) | **103** | 7842 | 2024-09-22 15:32 | | 57810 | [](https://myanimelist.net/anime/57810/Shoushimin_Series) | [Shoushimin Series](https://subsplease.org/shows/shoushimin-series) | TV | 10 / 10 | **Finished Airing** | 7.29 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shoushimin+Series+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57810__shoushimin_series.txt) | **102** | 9220 | 2024-09-14 18:02 | | 52347 | [](https://myanimelist.net/anime/52347/Shangri-La_Frontier__Kusoge_Hunter_Kamige_ni_Idoman_to_su) | [Shangri-La Frontier](https://subsplease.org/shows/shangri-la-frontier) | TV | 41 / 25 | **Finished Airing** | 8.08 | 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S3](https://subsplease.org/shows/mahouka-koukou-no-rettousei-s3) | TV | 13 / 13 | **Finished Airing** | 7.04 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahouka+Koukou+no+Rettousei+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50713__mahouka_koukou_no_rettousei_3rd_season.txt) | **101** | 14810 | 2024-06-28 16:02 | | 56400 | [](https://myanimelist.net/anime/56400/Maou-sama_Retry_R) | [Maou-sama, Retry! R](https://subsplease.org/shows/maou-sama-retry-r) | TV | 12 / 12 | **Finished Airing** | 5.9 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Maou+sama+Retry+R+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56400__maou_sama_retry_r.txt) | **101** | 7334 | 2024-12-14 15:17 | | 54839 | [](https://myanimelist.net/anime/54839/Yoru_no_Kurage_wa_Oyogenai) | [Yoru no Kurage wa Oyogenai](https://subsplease.org/shows/yoru-no-kurage-wa-oyogenai) | TV | 12 / 12 | **Finished Airing** | 7.78 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yoru+no+Kurage+wa+Oyogenai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54839__yoru_no_kurage_wa_oyogenai.txt) | **101** | 12574 | 2024-06-22 16:32 | | 54837 | [](https://myanimelist.net/anime/54837/Akuyaku_Reijou_Level_99__Watashi_wa_Ura-Boss_desu_ga_Maou_dewa_Arimasen) | [Akuyaku Reijou Level 99](https://subsplease.org/shows/akuyaku-reijou-level-99) | TV | 12 / 12 | **Finished Airing** | 7.13 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Akuyaku+Reijou+Level+99+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54837__akuyaku_reijou_level_99_watashi_wa_ura_boss_desu_ga_maou_dewa_arimasen.txt) | **99** | 16912 | 2024-03-26 15:31 | | 47160 | [](https://myanimelist.net/anime/47160/Goblin_Slayer_II) | [Goblin Slayer S2](https://subsplease.org/shows/goblin-slayer-s2) | TV | 12 / 12 | **Finished Airing** | 7.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Goblin+Slayer+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47160__goblin_slayer_ii.txt) | **98** | 28553 | 2023-12-22 14:31 | | 53410 | [](https://myanimelist.net/anime/53410/Yuru_Camp△_Season_3) | [Yuru Camp S3](https://subsplease.org/shows/yuru-camp-s3) | TV | 15 / 12 | **Finished Airing** | 8.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yuru+Camp+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53410__yuru_camp_season_3.txt) | **98** | 9672 | 2024-10-26 23:39 | | 53835 | [](https://myanimelist.net/anime/53835/Unnamed_Memory) | [Unnamed Memory](https://subsplease.org/shows/unnamed-memory) | TV | 14 / 12 | **Finished Airing** | 6.71 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Unnamed+Memory+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53835__unnamed_memory.txt) | **96** | 11493 | 2025-01-14 16:04 | | 60108 | [](https://myanimelist.net/anime/60108/One_Piece__Gyojin_Tou-hen) | [One Piece Log - Fish-Man Island Saga](https://subsplease.org/shows/one-piece-log-fish-man-island-saga) | TV | 12 / 21 | Currently Airing | 8.28 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+One+Piece+Log+Fish+Man+Island+Saga+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/60108__one_piece_gyojin_tou_hen.txt) | **96** | 5718 | 2025-01-19 02:01 | | 49613 | [](https://myanimelist.net/anime/49613/Chiyu_Mahou_no_Machigatta_Tsukaikata) | [Chiyu Mahou no Machigatta Tsukaikata](https://subsplease.org/shows/chiyu-mahou-no-machigatta-tsukaikata) | TV | 13 / 13 | **Finished Airing** | 7.53 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Chiyu+Mahou+no+Machigatta+Tsukaikata+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49613__chiyu_mahou_no_machigatta_tsukaikata.txt) | **95** | 20023 | 2024-03-29 16:01 | | 54717 | [](https://myanimelist.net/anime/54717/Mahoutsukai_Precure_Mirai_Days) | [Mahoutsukai Precure!! Mirai Days](https://subsplease.org/shows/mahoutsukai-precure-mirai-days) | TV | 2 / 12 | Currently Airing | 7.17 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahoutsukai+Precure+Mirai+Days+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54717__mahoutsukai_precure_mirai_days.txt) | **94** | 1311 | 2025-01-19 08:28 | | 56062 | [](https://myanimelist.net/anime/56062/Naze_Boku_no_Sekai_wo_Daremo_Oboeteinai_no_ka) | [Naze Boku no Sekai wo Daremo Oboeteinai no ka](https://subsplease.org/shows/naze-boku-no-sekai-wo-daremo-oboeteinai-no-ka) | TV | 12 / 12 | **Finished Airing** | 6.24 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Naze+Boku+no+Sekai+wo+Daremo+Oboeteinai+no+ka+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56062__naze_boku_no_sekai_wo_daremo_oboeteinai_no_ka.txt) | **93** | 10494 | 2024-09-28 15:17 | | 56923 | [](https://myanimelist.net/anime/56923/Lv2_kara_Cheat_datta_Motoyuusha_Kouho_no_Mattari_Isekai_Life) | [Lv2 kara Cheat datta Motoyuusha Kouho no Mattari Isekai Life](https://subsplease.org/shows/lv2-kara-cheat-datta-motoyuusha-kouho-no-mattari-isekai-life) | TV | 12 / 12 | **Finished Airing** | 6.82 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Lv2+kara+Cheat+datta+Motoyuusha+Kouho+no+Mattari+Isekai+Life+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56923__lv2_kara_cheat_datta_motoyuusha_kouho_no_mattari_isekai_life.txt) | **92** | 16449 | 2024-06-24 14:32 | | 52742 | [](https://myanimelist.net/anime/52742/Haikyuu_Movie__Gomisuteba_no_Kessen) | [Haikyuu!! Movie - Gomisuteba no Kessen](https://subsplease.org/shows/haikyuu-movie-gomisuteba-no-kessen) | Movie | 1 / 1 | **Finished Airing** | 8.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Haikyuu+Movie+Gomisuteba+no+Kessen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52742__haikyuu_movie_gomisuteba_no_kessen.txt) | **91** | 7138 | 2024-11-03 00:13 | | 51105 | [](https://myanimelist.net/anime/51105/NieR_Automata_Ver11a) | [NieR Automata Ver1.1a](https://subsplease.org/shows/nier-automata-ver1-1a) | TV | 25 / 12 | **Finished Airing** | 7.53 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+NieR+Automata+Ver1+1a+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51105__nier_automata_ver1_1a.txt) | **90** | 14917 | 2024-09-27 16:01 | | 53516 | [](https://myanimelist.net/anime/53516/Tensei_shitara_Dainana_Ouji_Datta_node_Kimama_ni_Majutsu_wo_Kiwamemasu) | [Dainanaoji](https://subsplease.org/shows/dainanaoji) | TV | 12 / 12 | **Finished Airing** | 7.44 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dainanaoji+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53516__tensei_shitara_dainana_ouji_datta_node_kimama_ni_majutsu_wo_kiwamemasu.txt) | **89** | 15362 | 2024-06-17 16:32 | | 54855 | [](https://myanimelist.net/anime/54855/Senpai_wa_Otokonoko) | [Senpai wa Otokonoko](https://subsplease.org/shows/senpai-wa-otokonoko) | TV | 12 / 12 | **Finished Airing** | 7.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Senpai+wa+Otokonoko+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54855__senpai_wa_otokonoko.txt) | **88** | 5804 | 2024-09-26 18:32 | | 56352 | [](https://myanimelist.net/anime/56352/Loop_7-kaime_no_Akuyaku_Reijou_wa_Moto_Tekikoku_de_Jiyuu_Kimama_na_Hanayome_Seikatsu_wo_Mankitsu_suru) | [7th Time Loop](https://subsplease.org/shows/7th-time-loop) | TV | 12 / 12 | **Finished Airing** | 7.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+7th+Time+Loop+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56352__loop_7_kaime_no_akuyaku_reijou_wa_moto_tekikoku_de_jiyuu_kimama_na_hanayome_seikatsu_wo_mankitsu_suru.txt) | **87** | 14182 | 2024-03-24 14:16 | | 51019 | [](https://myanimelist.net/anime/51019/Kimetsu_no_Yaiba__Katanakaji_no_Sato-hen) | [Kimetsu no Yaiba - Katanakaji no Sato-hen](https://subsplease.org/shows/kimetsu-no-yaiba-katanakaji-no-sato-hen) | TV | 11 / 11 | **Finished Airing** | 8.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kimetsu+no+Yaiba+Katanakaji+no+Sato+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51019__kimetsu_no_yaiba_katanakaji_no_sato_hen.txt) | **83** | 45582 | 2023-06-19 03:10 | | 50265 | [](https://myanimelist.net/anime/50265/Spy_x_Family) | [Spy x Family](https://subsplease.org/shows/spy-x-family) | TV | 37 / 12 | **Finished Airing** | 8.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Spy+x+Family+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50265__spy_x_family.txt) | **83** | 30638 | 2023-12-23 16:34 | | 57646 | [](https://myanimelist.net/anime/57646/Mob_kara_Hajimaru_Tansaku_Eiyuutan) | [Mob kara Hajimaru Tansaku Eiyuutan](https://subsplease.org/shows/mob-kara-hajimaru-tansaku-eiyuutan) | TV | 12 / 12 | **Finished Airing** | 5.82 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mob+kara+Hajimaru+Tansaku+Eiyuutan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57646__mob_kara_hajimaru_tansaku_eiyuutan.txt) | **83** | 9665 | 2024-09-14 14:02 | | 57362 | [](https://myanimelist.net/anime/57362/Hoshifuru_Oukoku_no_Nina) | [Hoshifuru Oukoku no Nina](https://subsplease.org/shows/hoshifuru-oukoku-no-nina) | TV | 12 / 12 | **Finished Airing** | 7.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hoshifuru+Oukoku+no+Nina+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57362__hoshifuru_oukoku_no_nina.txt) | **83** | 4825 | 2024-12-23 15:02 | | 57217 | [](https://myanimelist.net/anime/57217/Katsute_Mahou_Shoujo_to_Aku_wa_Tekitai_shiteita) | [Katsute Mahou Shoujo to Aku wa Tekitai shiteita](https://subsplease.org/shows/katsute-mahou-shoujo-to-aku-wa-tekitai-shiteita) | TV | 12 / 12 | **Finished Airing** | 7.58 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Katsute+Mahou+Shoujo+to+Aku+wa+Tekitai+shiteita+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57217__katsute_mahou_shoujo_to_aku_wa_tekitai_shiteita.txt) | **82** | 7723 | 2024-09-24 14:47 | | 58883 | [](https://myanimelist.net/anime/58883/Dead_Dead_Demons_Dededede_Destruction_OVA) | [Dead Dead Demons Dededede Destruction](https://subsplease.org/shows/dead-dead-demons-dededede-destruction) | OVA | 18 / 17 | **Finished Airing** | 7.93 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dead+Dead+Demons+Dededede+Destruction+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58883__dead_dead_demons_dededede_destruction_ova.txt) | **81** | 6923 | 2024-09-20 03:22 | | 53127 | [](https://myanimelist.net/anime/53127/Fate_strange_Fake__Whispers_of_Dawn) | [Fate strange Fake - Whispers of Dawn](https://subsplease.org/shows/fate-strange-fake-whispers-of-dawn) | TV Special | 1 / 1 | **Finished Airing** | 8.19 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fate+strange+Fake+Whispers+of+Dawn+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53127__fate_strange_fake_whispers_of_dawn.txt) | **81** | 15005 | 2023-07-02 17:05 | | 53128 | [](https://myanimelist.net/anime/53128/Atri__My_Dear_Moments) | [Atri - My Dear Moments](https://subsplease.org/shows/atri-my-dear-moments) | TV | 13 / 13 | **Finished Airing** | 7.18 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Atri+My+Dear+Moments+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53128__atri_my_dear_moments.txt) | **80** | 6933 | 2024-10-05 16:32 | | 51180 | [](https://myanimelist.net/anime/51180/Youkoso_Jitsuryoku_Shijou_Shugi_no_Kyoushitsu_e_3rd_Season) | [Youkoso Jitsuryoku Shijou Shugi no Kyoushitsu e S3](https://subsplease.org/shows/youkoso-jitsuryoku-shijou-shugi-no-kyoushitsu-e-s3) | TV | 13 / 13 | **Finished Airing** | 7.96 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Youkoso+Jitsuryoku+Shijou+Shugi+no+Kyoushitsu+e+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51180__youkoso_jitsuryoku_shijou_shugi_no_kyoushitsu_e_3rd_season.txt) | **80** | 15992 | 2024-03-27 14:01 | | 49073 | [](https://myanimelist.net/anime/49073/Kidou_Senshi_Gundam_SEED_Freedom) | [Mobile Suit Gundam SEED Freedom](https://subsplease.org/shows/mobile-suit-gundam-seed-freedom) | Movie | 1 / 1 | **Finished Airing** | 7.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mobile+Suit+Gundam+SEED+Freedom+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49073__kidou_senshi_gundam_seed_freedom.txt) | **80** | 6615 | 2024-11-25 04:35 | | 53723 | [](https://myanimelist.net/anime/53723/Acro_Trip) | [Acro Trip](https://subsplease.org/shows/acro-trip) | TV | 12 / 12 | **Finished Airing** | 6.74 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Acro+Trip+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53723__acro_trip.txt) | **80** | 4369 | 2024-12-11 14:17 | | 53356 | [](https://myanimelist.net/anime/53356/Shuumatsu_Train_Doko_e_Iku) | [Shuumatsu Train Doko e Iku](https://subsplease.org/shows/shuumatsu-train-doko-e-iku) | TV | 13 / 12 | **Finished Airing** | 7.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shuumatsu+Train+Doko+e+Iku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53356__shuumatsu_train_doko_e_iku.txt) | 79 | 9596 | 2024-06-24 12:32 | | 54900 | [](https://myanimelist.net/anime/54900/Wind_Breaker) | [Wind Breaker](https://subsplease.org/shows/wind-breaker) | TV | 13 / 13 | **Finished Airing** | 7.74 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Wind+Breaker+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54900__wind_breaker.txt) | 78 | 14817 | 2024-06-27 17:32 | | 59571 | [](https://myanimelist.net/anime/59571/Shingeki_no_Kyojin_Movie__Kanketsu-hen_-_The_Last_Attack) | [Shingeki no Kyojin - The Final Season Part 3](https://subsplease.org/shows/shingeki-no-kyojin-the-final-season-part-3) | Movie | 2 / 1 | **Finished Airing** | 8.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shingeki+no+Kyojin+The+Final+Season+Part+3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59571__shingeki_no_kyojin_movie_kanketsu_hen_the_last_attack.txt) | 78 | 23702 | 2023-11-05 07:26 | | 51958 | [](https://myanimelist.net/anime/51958/Kono_Subarashii_Sekai_ni_Bakuen_wo) | [Kono Subarashii Sekai ni Bakuen wo!](https://subsplease.org/shows/kono-subarashii-sekai-ni-bakuen-wo) | TV | 12 / 12 | **Finished Airing** | 7.5 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kono+Subarashii+Sekai+ni+Bakuen+wo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51958__kono_subarashii_sekai_ni_bakuen_wo.txt) | 78 | 20615 | 2023-06-21 16:01 | | 50392 | [](https://myanimelist.net/anime/50392/Mato_Seihei_no_Slave) | [Mato Seihei no Slave](https://subsplease.org/shows/mato-seihei-no-slave) | TV | 12 / 12 | **Finished Airing** | 6.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mato+Seihei+no+Slave+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50392__mato_seihei_no_slave.txt) | 78 | 16828 | 2024-03-21 15:10 | | 49981 | [](https://myanimelist.net/anime/49981/Kimi_to_Boku_no_Saigo_no_Senjou_Aruiwa_Sekai_ga_Hajimaru_Seisen_Season_II) | [Kimi to Boku no Saigo no Senjou, Arui wa Sekai ga Hajimaru Seisen S2](https://subsplease.org/shows/kimi-to-boku-no-saigo-no-senjou-arui-wa-sekai-ga-hajimaru-seisen-s2) | TV | 4 / 12 | Currently Airing | 6.68 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kimi+to+Boku+no+Saigo+no+Senjou+Arui+wa+Sekai+ga+Hajimaru+Seisen+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49981__kimi_to_boku_no_saigo_no_senjou_aruiwa_sekai_ga_hajimaru_seisen_season_ii.txt) | 78 | 7302 | 2024-07-31 14:32 | | 56662 | [](https://myanimelist.net/anime/56662/Trillion_Game) | [Trillion Game](https://subsplease.org/shows/trillion-game) | TV | 15 / 26 | Currently Airing | 7.08 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Trillion+Game+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56662__trillion_game.txt) | 76 | 4307 | 2025-01-16 18:53 | | 57947 | [](https://myanimelist.net/anime/57947/Mayonaka_Punch) | [Mayonaka Punch](https://subsplease.org/shows/mayonaka-punch) | TV | 12 / 12 | **Finished Airing** | 7.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mayonaka+Punch+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57947__mayonaka_punch.txt) | 76 | 6745 | 2024-09-23 14:02 | | 44511 | [](https://myanimelist.net/anime/44511/Chainsaw_Man) | [Chainsaw Man](https://subsplease.org/shows/chainsaw-man) | TV | 12 / 12 | **Finished Airing** | 8.47 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Chainsaw+Man+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44511__chainsaw_man.txt) | 76 | 32075 | 2022-12-27 17:02 | | 56348 | [](https://myanimelist.net/anime/56348/Dungeon_no_Naka_no_Hito) | [Dungeon no Naka no Hito](https://subsplease.org/shows/dungeon-no-naka-no-hito) | TV | 12 / 12 | **Finished Airing** | 7.11 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dungeon+no+Naka+no+Hito+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56348__dungeon_no_naka_no_hito.txt) | 76 | 8381 | 2024-09-27 17:33 | | 40357 | [](https://myanimelist.net/anime/40357/Tate_no_Yuusha_no_Nariagari_Season_3) | [Tate no Yuusha no Nariagari S3](https://subsplease.org/shows/tate-no-yuusha-no-nariagari-s3) | TV | 12 / 12 | **Finished Airing** | 7.1 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tate+no+Yuusha+no+Nariagari+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40357__tate_no_yuusha_no_nariagari_season_3.txt) | 75 | 20497 | 2023-12-22 13:36 | | 21 | [](https://myanimelist.net/anime/21/One_Piece) | [One Piece](https://subsplease.org/shows/one-piece) | TV | 52 / ? | Currently Airing | 8.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+One+Piece+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/21__one_piece.txt) | 74 | 18233 | 2024-10-13 02:01 | | 58173 | [](https://myanimelist.net/anime/58173/Mahoutsukai_ni_Narenakatta_Onnanoko_no_Hanashi) | [Mahoutsukai ni Narenakatta Onnanoko no Hanashi](https://subsplease.org/shows/mahoutsukai-ni-narenakatta-onnanoko-no-hanashi) | TV | 12 / 12 | **Finished Airing** | 6.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahoutsukai+ni+Narenakatta+Onnanoko+no+Hanashi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58173__mahoutsukai_ni_narenakatta_onnanoko_no_hanashi.txt) | 74 | 4106 | 2024-12-20 19:32 | | 56449 | [](https://myanimelist.net/anime/56449/Madougushi_Dahliya_wa_Utsumukanai) | [Madougushi Dahliya wa Utsumukanai](https://subsplease.org/shows/madougushi-dahliya-wa-utsumukanai) | TV | 12 / 12 | **Finished Airing** | 6.8 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Madougushi+Dahliya+wa+Utsumukanai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56449__madougushi_dahliya_wa_utsumukanai.txt) | 74 | 8020 | 2024-09-21 12:32 | | 52211 | [](https://myanimelist.net/anime/52211/Mashle) | [Mashle](https://subsplease.org/shows/mashle) | TV | 25 / 12 | **Finished Airing** | 7.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mashle+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52211__mashle.txt) | 73 | 26317 | 2024-03-30 16:01 | | 57100 | [](https://myanimelist.net/anime/57100/The_New_Gate) | [The New Gate](https://subsplease.org/shows/the-new-gate) | TV | 12 / 12 | **Finished Airing** | 6.44 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+New+Gate+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57100__the_new_gate.txt) | 71 | 14245 | 2024-06-29 17:31 | | 54866 | [](https://myanimelist.net/anime/54866/Blue_Lock__Episode_Nagi) | [Blue lock - Episode Nagi](https://subsplease.org/shows/blue-lock-episode-nagi) | Movie | 1 / 1 | **Finished Airing** | 7.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Blue+lock+Episode+Nagi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54866__blue_lock_episode_nagi.txt) | 71 | 4944 | 2024-10-20 17:24 | | 56690 | [](https://myanimelist.net/anime/56690/Re_Monster) | [Re Monster](https://subsplease.org/shows/re-monster) | TV | 12 / 12 | **Finished Airing** | 6.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Re+Monster+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56690__re_monster.txt) | 71 | 14394 | 2024-06-17 15:33 | | 48418 | [](https://myanimelist.net/anime/48418/Maou_Gakuin_no_Futekigousha_II__Shijou_Saikyou_no_Maou_no_Shiso_Tensei_shite_Shison-tachi_no_Gakkou_e_Kayou_Part_2) | [Maou Gakuin no Futekigousha S2](https://subsplease.org/shows/maou-gakuin-no-futekigousha-s2) | TV | 24 / 12 | **Finished Airing** | 6.42 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Maou+Gakuin+no+Futekigousha+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48418__maou_gakuin_no_futekigousha_ii_shijou_saikyou_no_maou_no_shiso_tensei_shite_shison_tachi_no_gakkou_e_kayou_part_2.txt) | 68 | 12380 | 2024-07-24 18:34 | | 55877 | [](https://myanimelist.net/anime/55877/Henjin_no_Salad_Bowl) | [Henjin no Salad Bowl](https://subsplease.org/shows/henjin-no-salad-bowl) | TV | 12 / 12 | **Finished Airing** | 7.28 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Henjin+no+Salad+Bowl+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55877__henjin_no_salad_bowl.txt) | 68 | 8479 | 2024-06-20 18:47 | | 39894 | [](https://myanimelist.net/anime/39894/Hibike_Euphonium_3) | [Hibike! Euphonium S3](https://subsplease.org/shows/hibike-euphonium-s3) | TV | 13 / 13 | **Finished Airing** | 8.72 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hibike+Euphonium+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39894__hibike_euphonium_3.txt) | 67 | 9492 | 2024-06-30 10:32 | | 58272 | [](https://myanimelist.net/anime/58272/Boku_no_Tsuma_wa_Kanjou_ga_Nai) | [Boku no Tsuma wa Kanjou ga Nai](https://subsplease.org/shows/boku-no-tsuma-wa-kanjou-ga-nai) | TV | 12 / 12 | **Finished Airing** | 6.77 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Boku+no+Tsuma+wa+Kanjou+ga+Nai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58272__boku_no_tsuma_wa_kanjou_ga_nai.txt) | 66 | 6188 | 2024-09-14 15:02 | | 47917 | [](https://myanimelist.net/anime/47917/Bocchi_the_Rock) | [Bocchi the Rock!](https://subsplease.org/shows/bocchi-the-rock) | TV | 12 / 12 | **Finished Airing** | 8.76 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bocchi+the+Rock+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47917__bocchi_the_rock.txt) | 66 | 17828 | 2022-12-24 17:31 | | 53434 | [](https://myanimelist.net/anime/53434/Maou_no_Ore_ga_Dorei_Elf_wo_Yome_ni_Shitanda_ga_Dou_Medereba_Ii) | [Madome](https://subsplease.org/shows/madome) | TV | 12 / 12 | **Finished Airing** | 7.29 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Madome+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53434__maou_no_ore_ga_dorei_elf_wo_yome_ni_shitanda_ga_dou_medereba_ii.txt) | 66 | 12673 | 2024-06-13 16:04 | | 46569 | [](https://myanimelist.net/anime/46569/Jigokuraku) | [Jigokuraku](https://subsplease.org/shows/jigokuraku) | TV | 13 / 13 | **Finished Airing** | 8.09 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jigokuraku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/46569__jigokuraku.txt) | 65 | 29823 | 2023-07-01 15:31 | | 48549 | [](https://myanimelist.net/anime/48549/Dr_Stone__New_World) | [Dr. Stone S3](https://subsplease.org/shows/dr-stone-s3) | TV | 22 / 11 | **Finished Airing** | 8.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dr+Stone+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48549__dr_stone_new_world.txt) | 64 | 20134 | 2023-12-21 15:35 | | 54794 | [](https://myanimelist.net/anime/54794/Metallic_Rouge) | [Metallic Rouge](https://subsplease.org/shows/metallic-rouge) | TV | 13 / 13 | **Finished Airing** | 6.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Metallic+Rouge+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54794__metallic_rouge.txt) | 64 | 11992 | 2024-04-03 17:26 | | 55996 | [](https://myanimelist.net/anime/55996/Koi_wa_Futago_de_Warikirenai) | [Koi wa Futago de Warikirenai](https://subsplease.org/shows/koi-wa-futago-de-warikirenai) | TV | 12 / 12 | **Finished Airing** | 6.29 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Koi+wa+Futago+de+Warikirenai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55996__koi_wa_futago_de_warikirenai.txt) | 63 | 5682 | 2024-09-25 15:17 | | 55823 | [](https://myanimelist.net/anime/55823/Natsume_Yuujinchou_Shichi) | [Natsume Yuujinchou S7](https://subsplease.org/shows/natsume-yuujinchou-s7) | TV | 12 / 12 | **Finished Airing** | 8.59 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Natsume+Yuujinchou+S7+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55823__natsume_yuujinchou_shichi.txt) | 62 | 4523 | 2024-12-23 18:22 | | 56647 | [](https://myanimelist.net/anime/56647/Ao_no_Miburo) | [Ao no Miburo](https://subsplease.org/shows/ao-no-miburo) | TV | 14 / 24 | Currently Airing | 6.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ao+no+Miburo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56647__ao_no_miburo.txt) | 62 | 3196 | 2025-01-18 11:02 | | 55690 | [](https://myanimelist.net/anime/55690/Boku_no_Kokoro_no_Yabai_Yatsu_2nd_Season) | [Boku no Kokoro no Yabai Yatsu](https://subsplease.org/shows/boku-no-kokoro-no-yabai-yatsu) | TV | 26 / 13 | **Finished Airing** | 8.77 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Boku+no+Kokoro+no+Yabai+Yatsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55690__boku_no_kokoro_no_yabai_yatsu_2nd_season.txt) | 62 | 15386 | 2024-03-30 18:03 | | 55528 | [](https://myanimelist.net/anime/55528/Yuuki_Bakuhatsu_Bang_Bravern) | [Yuuki Bakuhatsu Bang Bravern](https://subsplease.org/shows/yuuki-bakuhatsu-bang-bravern) | TV | 12 / 12 | **Finished Airing** | 7.57 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yuuki+Bakuhatsu+Bang+Bravern+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55528__yuuki_bakuhatsu_bang_bravern.txt) | 62 | 8337 | 2024-03-28 16:31 | | 54233 | [](https://myanimelist.net/anime/54233/Sasayaku_You_ni_Koi_wo_Utau) | [Sasayaku You ni Koi wo Utau](https://subsplease.org/shows/sasayaku-you-ni-koi-wo-utau) | TV | 12 / 12 | **Finished Airing** | 6.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sasayaku+You+ni+Koi+wo+Utau+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54233__sasayaku_you_ni_koi_wo_utau.txt) | 62 | 6291 | 2024-12-29 11:04 | | 48316 | [](https://myanimelist.net/anime/48316/Kage_no_Jitsuryokusha_ni_Naritakute) | [Kage no Jitsuryokusha ni Naritakute!](https://subsplease.org/shows/kage-no-jitsuryokusha-ni-naritakute) | TV | 20 / 20 | **Finished Airing** | 8.27 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kage+no+Jitsuryokusha+ni+Naritakute+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48316__kage_no_jitsuryokusha_ni_naritakute.txt) | 61 | 29188 | 2023-02-15 14:32 | | 57380 | [](https://myanimelist.net/anime/57380/Isekai_Yururi_Kikou__Kosodateshinagara_Boukensha_Shimasu) | [Isekai Yururi Kikou](https://subsplease.org/shows/isekai-yururi-kikou) | TV | 12 / 12 | **Finished Airing** | 6.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+Yururi+Kikou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57380__isekai_yururi_kikou_kosodateshinagara_boukensha_shimasu.txt) | 61 | 7976 | 2024-09-15 17:32 | | 55866 | [](https://myanimelist.net/anime/55866/Yubisaki_to_Renren) | [Yubisaki to Renren](https://subsplease.org/shows/yubisaki-to-renren) | TV | 12 / 12 | **Finished Airing** | 8.22 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yubisaki+to+Renren+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55866__yubisaki_to_renren.txt) | 60 | 9796 | 2024-03-23 14:01 | | 50664 | [](https://myanimelist.net/anime/50664/Saihate_no_Paladin__Tetsusabi_no_Yama_no_Ou) | [Saihate no Paladin S2](https://subsplease.org/shows/saihate-no-paladin-s2) | TV | 12 / 12 | **Finished Airing** | 7.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Saihate+no+Paladin+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50664__saihate_no_paladin_tetsusabi_no_yama_no_ou.txt) | 60 | 11257 | 2023-12-23 14:34 | | 54714 | [](https://myanimelist.net/anime/54714/Kimi_no_Koto_ga_Daidaidaidaidaisuki_na_100-nin_no_Kanojo) | [Hyakkano](https://subsplease.org/shows/hyakkano) | TV | 14 / 12 | **Finished Airing** | 7.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hyakkano+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54714__kimi_no_koto_ga_daidaidaidaidaisuki_na_100_nin_no_kanojo.txt) | 60 | 11406 | 2025-01-19 15:01 | | 50695 | [](https://myanimelist.net/anime/50695/MF_Ghost) | [MF Ghost](https://subsplease.org/shows/mf-ghost) | TV | 24 / 12 | **Finished Airing** | 7.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+MF+Ghost+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50695__mf_ghost.txt) | 60 | 10012 | 2024-12-22 17:02 | | 50593 | [](https://myanimelist.net/anime/50593/Natsu_e_no_Tunnel_Sayonara_no_Deguchi) | [Natsu e no Tunnel, Sayonara no Deguchi](https://subsplease.org/shows/natsu-e-no-tunnel-sayonara-no-deguchi) | Movie | 1 / 1 | **Finished Airing** | 7.92 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Natsu+e+no+Tunnel+Sayonara+no+Deguchi+1080p+mkv) | 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[Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49785__fairy_tail_100_nen_quest.txt) | 59 | 7255 | 2025-01-05 10:01 | | 53626 | [](https://myanimelist.net/anime/53626/Bye_Bye_Earth) | [Bye Bye, Earth](https://subsplease.org/shows/bye-bye-earth) | TV | 10 / 10 | **Finished Airing** | 6.06 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bye+Bye+Earth+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53626__bye_bye_earth.txt) | 58 | 7788 | 2024-09-13 15:02 | | 38475 | [](https://myanimelist.net/anime/38475/Yuru_Camp△_Movie) | [Yuru Camp Movie](https://subsplease.org/shows/yuru-camp-movie) | Movie | 1 / 1 | **Finished Airing** | 8.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yuru+Camp+Movie+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38475__yuru_camp_movie.txt) | 57 | 5761 | 2022-11-28 17:03 | | 53488 | [](https://myanimelist.net/anime/53488/Shin_no_Nakama_ja_Nai_to_Yuusha_no_Party_wo_Oidasareta_node_Henkyou_de_Slow_Life_suru_Koto_ni_Shimashita_2nd) | [Shin no Nakama S2](https://subsplease.org/shows/shin-no-nakama-s2) | TV | 12 / 12 | **Finished Airing** | 6.76 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shin+no+Nakama+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53488__shin_no_nakama_ja_nai_to_yuusha_no_party_wo_oidasareta_node_henkyou_de_slow_life_suru_koto_ni_shimashita_2nd.txt) | 57 | 11622 | 2024-03-24 14:31 | | 52196 | [](https://myanimelist.net/anime/52196/Date_A_Live_V) | [Date a Live V](https://subsplease.org/shows/date-a-live-v) | TV | 12 / 12 | **Finished Airing** | 7.69 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Date+a+Live+V+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52196__date_a_live_v.txt) | 57 | 7234 | 2024-06-26 14:32 | | 56843 | [](https://myanimelist.net/anime/56843/Goukon_ni_Ittara_Onna_ga_Inakatta_Hanashi) | [Goukon ni Ittara Onna ga Inakatta Hanashi](https://subsplease.org/shows/goukon-ni-ittara-onna-ga-inakatta-hanashi) | TV | 12 / 12 | **Finished Airing** | 7.42 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Goukon+ni+Ittara+Onna+ga+Inakatta+Hanashi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56843__goukon_ni_ittara_onna_ga_inakatta_hanashi.txt) | 56 | 3762 | 2024-12-20 18:01 | | 53590 | [](https://myanimelist.net/anime/53590/Saijaku_Tamer_wa_Gomi_Hiroi_no_Tabi_wo_Hajimemashita) | [Saijaku Tamer wa Gomi Hiroi no Tabi wo Hajimemashita](https://subsplease.org/shows/saijaku-tamer-wa-gomi-hiroi-no-tabi-wo-hajimemashita) | TV | 12 / 12 | **Finished Airing** | 7.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Saijaku+Tamer+wa+Gomi+Hiroi+no+Tabi+wo+Hajimemashita+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53590__saijaku_tamer_wa_gomi_hiroi_no_tabi_wo_hajimemashita.txt) | 55 | 11902 | 2024-03-29 14:31 | | 50172 | [](https://myanimelist.net/anime/50172/Mob_Psycho_100_III) | [Mob Psycho 100 S3](https://subsplease.org/shows/mob-psycho-100-s3) | TV | 12 / 12 | **Finished Airing** | 8.71 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mob+Psycho+100+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50172__mob_psycho_100_iii.txt) | 55 | 16712 | 2022-12-21 17:01 | | 56135 | [](https://myanimelist.net/anime/56135/UniteUp_Uni_Birth) | [UniteUp! S2](https://subsplease.org/shows/uniteup-s2) | TV | 2 / 12 | Currently Airing | 6.96 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+UniteUp+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56135__uniteup_uni_birth.txt) | 54 | 1250 | 2025-01-18 17:02 | | 53912 | [](https://myanimelist.net/anime/53912/Seiyuu_Radio_no_Uraomote) | [Seiyuu Radio no Uraomote](https://subsplease.org/shows/seiyuu-radio-no-uraomote) | TV | 12 / 12 | **Finished Airing** | 6.96 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Seiyuu+Radio+no+Uraomote+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53912__seiyuu_radio_no_uraomote.txt) | 54 | 5841 | 2024-06-26 12:33 | | 49877 | [](https://myanimelist.net/anime/49877/Tensei_shitara_Slime_Datta_Ken_Movie__Guren_no_Kizuna-hen) | [Tensei shitara Slime Datta Ken Movie - Guren no Kizuna-hen](https://subsplease.org/shows/tensei-shitara-slime-datta-ken-movie-guren-no-kizuna-hen) | Movie | 1 / 1 | **Finished Airing** | 7.63 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tensei+shitara+Slime+Datta+Ken+Movie+Guren+no+Kizuna+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49877__tensei_shitara_slime_datta_ken_movie_guren_no_kizuna_hen.txt) | 54 | 12730 | 2023-04-21 05:54 | | 57533 | [](https://myanimelist.net/anime/57533/Youkai_Gakkou_no_Sensei_Hajimemashita) | [Youkai Gakkou no Sensei Hajimemashita](https://subsplease.org/shows/youkai-gakkou-no-sensei-hajimemashita) | TV | 14 / 24 | Currently Airing | 6.59 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Youkai+Gakkou+no+Sensei+Hajimemashita+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57533__youkai_gakkou_no_sensei_hajimemashita.txt) | 52 | 3230 | 2025-01-14 15:32 | | 57099 | [](https://myanimelist.net/anime/57099/Na_Nare_Hana_Nare) | [Na Nare Hana Nare](https://subsplease.org/shows/na-nare-hana-nare) | TV | 12 / 12 | **Finished Airing** | 6.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Na+Nare+Hana+Nare+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57099__na_nare_hana_nare.txt) | 52 | 4327 | 2024-09-22 16:32 | | 56242 | [](https://myanimelist.net/anime/56242/Sengoku_Youko__Yonaoshi_Kyoudai-hen) | [Sengoku Youko](https://subsplease.org/shows/sengoku-youko) | TV | 37 / 13 | **Finished Airing** | 6.99 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sengoku+Youko+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56242__sengoku_youko_yonaoshi_kyoudai_hen.txt) | 52 | 5838 | 2024-12-25 16:48 | | 54835 | [](https://myanimelist.net/anime/54835/Kono_Sekai_wa_Fukanzen_Sugiru) | [Kono Sekai wa Fukanzen Sugiru](https://subsplease.org/shows/kono-sekai-wa-fukanzen-sugiru) | TV | 13 / 13 | **Finished Airing** | 6.55 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kono+Sekai+wa+Fukanzen+Sugiru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54835__kono_sekai_wa_fukanzen_sugiru.txt) | 52 | 6180 | 2024-09-27 18:32 | | 48561 | [](https://myanimelist.net/anime/48561/Jujutsu_Kaisen_0_Movie) | [Jujutsu Kaisen 0](https://subsplease.org/shows/jujutsu-kaisen-0) | Movie | 1 / 1 | **Finished Airing** | 8.42 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jujutsu+Kaisen+0+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48561__jujutsu_kaisen_0_movie.txt) | 52 | 15346 | 2022-09-22 00:23 | | 54112 | [](https://myanimelist.net/anime/54112/Zom_100__Zombie_ni_Naru_made_ni_Shitai_100_no_Koto) | [Zom 100 - Zombie ni Naru made ni Shitai 100 no Koto](https://subsplease.org/shows/zom-100-zombie-ni-naru-made-ni-shitai-100-no-koto) | TV | 12 / 12 | **Finished Airing** | 7.76 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Zom+100+Zombie+ni+Naru+made+ni+Shitai+100+no+Koto+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54112__zom_100_zombie_ni_naru_made_ni_shitai_100_no_koto.txt) | 51 | 24530 | 2023-12-27 14:14 | | 54103 | [](https://myanimelist.net/anime/54103/Hikikomari_Kyuuketsuki_no_Monmon) | [Hikikomari Kyuuketsuki no Monmon](https://subsplease.org/shows/hikikomari-kyuuketsuki-no-monmon) | TV | 12 / 12 | **Finished Airing** | 7.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hikikomari+Kyuuketsuki+no+Monmon+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54103__hikikomari_kyuuketsuki_no_monmon.txt) | 51 | 11822 | 2023-12-30 14:01 | | 50739 | [](https://myanimelist.net/anime/50739/Otonari_no_Tenshi-sama_ni_Itsunomanika_Dame_Ningen_ni_Sareteita_Ken) | [Otonari no Tenshi-sama ni Itsunomanika Dame Ningen ni Sareteita Ken](https://subsplease.org/shows/otonari-no-tenshi-sama-ni-itsunomanika-dame-ningen-ni-sareteita-ken) | TV | 12 / 12 | **Finished Airing** | 7.83 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Otonari+no+Tenshi+sama+ni+Itsunomanika+Dame+Ningen+ni+Sareteita+Ken+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50739__otonari_no_tenshi_sama_ni_itsunomanika_dame_ningen_ni_sareteita_ken.txt) | 51 | 9278 | 2023-03-25 15:02 | | 49828 | [](https://myanimelist.net/anime/49828/Kidou_Senshi_Gundam__Suisei_no_Majo) | [Mobile Suit Gundam - The Witch from Mercury](https://subsplease.org/shows/mobile-suit-gundam-the-witch-from-mercury) | TV | 25 / 12 | **Finished Airing** | 7.86 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mobile+Suit+Gundam+The+Witch+from+Mercury+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49828__kidou_senshi_gundam_suisei_no_majo.txt) | 51 | 19450 | 2023-07-02 09:31 | | 52736 | [](https://myanimelist.net/anime/52736/Tensei_Oujo_to_Tensai_Reijou_no_Mahou_Kakumei) | [Tensei Oujo to Tensai Reijou no Mahou Kakumei](https://subsplease.org/shows/tensei-oujo-to-tensai-reijou-no-mahou-kakumei) | TV | 12 / 12 | **Finished Airing** | 7.47 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tensei+Oujo+to+Tensai+Reijou+no+Mahou+Kakumei+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52736__tensei_oujo_to_tensai_reijou_no_mahou_kakumei.txt) | 51 | 11623 | 2023-03-22 13:01 | | 52619 | [](https://myanimelist.net/anime/52619/Jidou_Hanbaiki_ni_Umarekawatta_Ore_wa_Meikyuu_wo_Samayou) | [Jidou Hanbaiki ni Umarekawatta Ore wa Meikyuu wo Samayou](https://subsplease.org/shows/jidou-hanbaiki-ni-umarekawatta-ore-wa-meikyuu-wo-samayou) | TV | 12 / 12 | **Finished Airing** | 6.46 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jidou+Hanbaiki+ni+Umarekawatta+Ore+wa+Meikyuu+wo+Samayou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52619__jidou_hanbaiki_ni_umarekawatta_ore_wa_meikyuu_wo_samayou.txt) | 50 | 11708 | 2023-09-20 14:01 | | 53407 | [](https://myanimelist.net/anime/53407/Bartender__Kami_no_Glass) | [Bartender - Kami no Glass](https://subsplease.org/shows/bartender-kami-no-glass) | TV | 12 / 12 | **Finished Airing** | 7.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bartender+Kami+no+Glass+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53407__bartender_kami_no_glass.txt) | 50 | 8605 | 2024-06-19 16:32 | | 50709 | [](https://myanimelist.net/anime/50709/Lycoris_Recoil) | [Lycoris Recoil](https://subsplease.org/shows/lycoris-recoil) | TV | 13 / 13 | **Finished Airing** | 8.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Lycoris+Recoil+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50709__lycoris_recoil.txt) | 48 | 15394 | 2022-09-24 16:03 | | 52482 | [](https://myanimelist.net/anime/52482/Sasaki_to_Pii-chan) | [Sasaki to Pii-chan](https://subsplease.org/shows/sasaki-to-pii-chan) | TV | 12 / 12 | **Finished Airing** | 6.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sasaki+to+Pii+chan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52482__sasaki_to_pii_chan.txt) | 48 | 12953 | 2024-03-22 12:31 | | 53730 | [](https://myanimelist.net/anime/53730/Sokushi_Cheat_ga_Saikyou_sugite_Isekai_no_Yatsura_ga_Marude_Aite_ni_Naranai_n_desu_ga) | [Sokushi Cheat](https://subsplease.org/shows/sokushi-cheat) | TV | 12 / 12 | **Finished Airing** | 6.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sokushi+Cheat+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53730__sokushi_cheat_ga_saikyou_sugite_isekai_no_yatsura_ga_marude_aite_ni_naranai_n_desu_ga.txt) | 48 | 13892 | 2024-03-21 16:31 | | 52969 | [](https://myanimelist.net/anime/52969/Jitsu_wa_Ore_Saikyou_deshita) | [Jitsu wa Ore, Saikyou deshita](https://subsplease.org/shows/jitsu-wa-ore-saikyou-deshita) | TV | 12 / 12 | **Finished Airing** | 6.49 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jitsu+wa+Ore+Saikyou+deshita+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52969__jitsu_wa_ore_saikyou_deshita.txt) | 47 | 13326 | 2023-09-30 18:46 | | 56838 | [](https://myanimelist.net/anime/56838/One_Room_Hiatari_Futsuu_Tenshi-tsuki) | [One Room, Hiatari Futsuu, Tenshi-tsuki](https://subsplease.org/shows/one-room-hiatari-futsuu-tenshi-tsuki) | TV | 12 / 12 | **Finished Airing** | 7.04 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+One+Room+Hiatari+Futsuu+Tenshi+tsuki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56838__one_room_hiatari_futsuu_tenshi_tsuki.txt) | 46 | 6791 | 2024-06-15 14:04 | | 55717 | [](https://myanimelist.net/anime/55717/Dekisokonai_to_Yobareta_Motoeiyuu_wa_Jikka_kara_Tsuihou_sareta_node_Sukikatte_ni_Ikiru_Koto_ni_Shita) | [Dekisoko](https://subsplease.org/shows/dekisoko) | TV | 12 / 12 | **Finished Airing** | 5.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dekisoko+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55717__dekisokonai_to_yobareta_motoeiyuu_wa_jikka_kara_tsuihou_sareta_node_sukikatte_ni_ikiru_koto_ni_shita.txt) | 46 | 8833 | 2024-06-10 18:33 | | 54856 | [](https://myanimelist.net/anime/54856/Horimiya__Piece) | [Horimiya - Piece](https://subsplease.org/shows/horimiya-piece) | TV | 13 / 13 | **Finished Airing** | 8.16 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Horimiya+Piece+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54856__horimiya_piece.txt) | 46 | 10904 | 2023-09-23 16:01 | | 53833 | [](https://myanimelist.net/anime/53833/Watashi_no_Oshi_wa_Akuyaku_Reijou) | [Watashi no Oshi wa Akuyaku Reijou](https://subsplease.org/shows/watashi-no-oshi-wa-akuyaku-reijou) | TV | 12 / 12 | **Finished Airing** | 7.34 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Watashi+no+Oshi+wa+Akuyaku+Reijou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53833__watashi_no_oshi_wa_akuyaku_reijou.txt) | 45 | 9899 | 2023-12-18 18:37 | | 53421 | [](https://myanimelist.net/anime/53421/Dosanko_Gal_wa_Namara_Menkoi) | [Dosanko Gal wa Namara Menkoi](https://subsplease.org/shows/dosanko-gal-wa-namara-menkoi) | TV | 12 / 12 | **Finished Airing** | 7.03 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dosanko+Gal+wa+Namara+Menkoi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53421__dosanko_gal_wa_namara_menkoi.txt) | 45 | 11172 | 2024-03-25 16:46 | | 52747 | [](https://myanimelist.net/anime/52747/Psycho-Pass_Movie__Providence) | [Psycho-Pass Movie - Providence](https://subsplease.org/shows/psycho-pass-movie-providence) | Movie | 1 / 1 | **Finished Airing** | 7.64 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Psycho+Pass+Movie+Providence+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52747__psycho_pass_movie_providence.txt) | 45 | 11255 | 2023-12-19 01:41 | | 53287 | [](https://myanimelist.net/anime/53287/Love_Live_Superstar_3rd_Season) | [Love Live! Superstar!! S3](https://subsplease.org/shows/love-live-superstar-s3) | TV | 12 / 12 | **Finished Airing** | 7.89 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Love+Live+Superstar+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53287__love_live_superstar_3rd_season.txt) | 45 | 2547 | 2024-12-24 02:02 | | 52962 | [](https://myanimelist.net/anime/52962/Tearmoon_Teikoku_Monogatari__Dantoudai_kara_Hajimaru_Hime_no_Tensei_Gyakuten_Story) | [Tearmoon Teikoku Monogatari](https://subsplease.org/shows/tearmoon-teikoku-monogatari) | TV | 12 / 12 | **Finished Airing** | 7.21 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tearmoon+Teikoku+Monogatari+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52962__tearmoon_teikoku_monogatari_dantoudai_kara_hajimaru_hime_no_tensei_gyakuten_story.txt) | 44 | 9413 | 2023-12-23 17:20 | | 50587 | [](https://myanimelist.net/anime/50587/Gridman_Universe) | [Gridman Universe](https://subsplease.org/shows/gridman-universe) | Movie | 1 / 1 | **Finished Airing** | 8.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gridman+Universe+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50587__gridman_universe.txt) | 44 | 3516 | 2024-10-25 04:41 | | 49387 | [](https://myanimelist.net/anime/49387/Vinland_Saga_Season_2) | [Vinland Saga S2](https://subsplease.org/shows/vinland-saga-s2) | TV | 24 / 24 | **Finished Airing** | 8.81 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Vinland+Saga+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49387__vinland_saga_season_2.txt) | 44 | 17379 | 2023-06-19 16:32 | | 55129 | [](https://myanimelist.net/anime/55129/Oroka_na_Tenshi_wa_Akuma_to_Odoru) | [Oroka na Tenshi wa Akuma to Odoru](https://subsplease.org/shows/oroka-na-tenshi-wa-akuma-to-odoru) | TV | 12 / 12 | **Finished Airing** | 6.91 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Oroka+na+Tenshi+wa+Akuma+to+Odoru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55129__oroka_na_tenshi_wa_akuma_to_odoru.txt) | 44 | 7876 | 2024-03-25 17:01 | | 54362 | [](https://myanimelist.net/anime/54362/Hametsu_no_Oukoku) | [Hametsu no Oukoku](https://subsplease.org/shows/hametsu-no-oukoku) | TV | 12 / 12 | **Finished Airing** | 6.22 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hametsu+no+Oukoku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54362__hametsu_no_oukoku.txt) | 44 | 14239 | 2023-12-22 18:35 | | 52816 | [](https://myanimelist.net/anime/52816/Majo_to_Yajuu) | [Majo to Yajuu](https://subsplease.org/shows/majo-to-yajuu) | TV | 12 / 12 | **Finished Airing** | 7.42 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Majo+to+Yajuu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52816__majo_to_yajuu.txt) | 44 | 11718 | 2024-04-04 18:16 | | 41514 | [](https://myanimelist.net/anime/41514/Itai_no_wa_Iya_nanode_Bougyoryoku_ni_Kyokufuri_Shitai_to_Omoimasu_2) | [Bofuri S2](https://subsplease.org/shows/bofuri-s2) | TV | 12 / 12 | **Finished Airing** | 7.21 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bofuri+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41514__itai_no_wa_iya_nanode_bougyoryoku_ni_kyokufuri_shitai_to_omoimasu_2.txt) | 44 | 10414 | 2023-04-19 16:18 | | 60410 | [](https://myanimelist.net/anime/60410/Yami_Shibai_14) | [Yami Shibai 14](https://subsplease.org/shows/yami-shibai-14) | TV | 3 / ? | Currently Airing | 6.08 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yami+Shibai+14+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/60410__yami_shibai_14.txt) | 44 | 1153 | 2025-01-19 20:45 | | 54265 | [](https://myanimelist.net/anime/54265/Kekkon_Yubiwa_Monogatari) | [Kekkon Yubiwa Monogatari](https://subsplease.org/shows/kekkon-yubiwa-monogatari) | TV | 12 / 12 | **Finished Airing** | 6.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kekkon+Yubiwa+Monogatari+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54265__kekkon_yubiwa_monogatari.txt) | 43 | 9345 | 2024-03-23 13:01 | | 48895 | [](https://myanimelist.net/anime/48895/Overlord_IV) | [Overlord IV](https://subsplease.org/shows/overlord-iv) | TV | 13 / 13 | **Finished Airing** | 8.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Overlord+IV+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48895__overlord_iv.txt) | 43 | 17402 | 2022-09-27 14:01 | | 52955 | [](https://myanimelist.net/anime/52955/Mahoutsukai_no_Yome_Season_2) | [Mahoutsukai no Yome S2](https://subsplease.org/shows/mahoutsukai-no-yome-s2) | TV | 24 / 12 | **Finished Airing** | 7.76 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahoutsukai+no+Yome+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52955__mahoutsukai_no_yome_season_2.txt) | 43 | 11500 | 2023-12-21 16:06 | | 51297 | [](https://myanimelist.net/anime/51297/Ragna_Crimson) | [Ragna Crimson](https://subsplease.org/shows/ragna-crimson) | TV | 24 / 24 | **Finished Airing** | 7.54 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ragna+Crimson+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51297__ragna_crimson.txt) | 43 | 15085 | 2024-03-30 16:02 | | 50613 | [](https://myanimelist.net/anime/50613/Rurouni_Kenshin__Meiji_Kenkaku_Romantan_2023) | [Rurouni Kenshin (2023)](https://subsplease.org/shows/rurouni-kenshin-2023) | TV | 38 / 24 | **Finished Airing** | 7.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Rurouni+Kenshin+2023+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50613__rurouni_kenshin_meiji_kenkaku_romantan_2023.txt) | 42 | 12636 | 2025-01-16 19:01 | | 54199 | [](https://myanimelist.net/anime/54199/Kaii_to_Otome_to_Kamikakushi) | [Kaii to Otome to Kamikakushi](https://subsplease.org/shows/kaii-to-otome-to-kamikakushi) | TV | 12 / 12 | **Finished Airing** | 6.77 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kaii+to+Otome+to+Kamikakushi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54199__kaii_to_otome_to_kamikakushi.txt) | 42 | 8177 | 2024-06-26 14:02 | | 53439 | [](https://myanimelist.net/anime/53439/Boushoku_no_Berserk) | [Boushoku no Berserk](https://subsplease.org/shows/boushoku-no-berserk) | TV | 12 / 12 | **Finished Airing** | 6.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Boushoku+no+Berserk+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53439__boushoku_no_berserk.txt) | 42 | 15234 | 2023-12-17 16:35 | | 50205 | [](https://myanimelist.net/anime/50205/Arknights__Reimei_Zensou) | [Arknights - Fuyukomori Kaerimichi](https://subsplease.org/shows/arknights-reimei-zensou) | TV | 8 / 8 | **Finished Airing** | 7.08 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Arknights+Fuyukomori+Kaerimichi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50205__arknights_reimei_zensou.txt) | 42 | 6667 | 2023-11-24 18:01 | | 56845 | [](https://myanimelist.net/anime/56845/Saikyou_Tank_no_Meikyuu_Kouryaku__Tairyoku_9999_no_Rare_Skill-mochi_Tank_Yuusha_Party_wo_Tsuihou_sareru) | [Saikyou Tank no Meikyuu Kouryaku](https://subsplease.org/shows/saikyou-tank-no-meikyuu-kouryaku) | TV | 12 / 12 | **Finished Airing** | 6.12 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Saikyou+Tank+no+Meikyuu+Kouryaku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56845__saikyou_tank_no_meikyuu_kouryaku_tairyoku_9999_no_rare_skill_mochi_tank_yuusha_party_wo_tsuihou_sareru.txt) | 42 | 11189 | 2024-03-23 18:01 | | 41084 | [](https://myanimelist.net/anime/41084/Made_in_Abyss__Retsujitsu_no_Ougonkyou) | [Made in Abyss - Retsujitsu no Ougonkyou](https://subsplease.org/shows/made-in-abyss-retsujitsu-no-ougonkyou) | TV | 12 / 12 | **Finished Airing** | 8.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Made+in+Abyss+Retsujitsu+no+Ougonkyou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41084__made_in_abyss_retsujitsu_no_ougonkyou.txt) | 41 | 17900 | 2022-09-28 14:32 | | 53126 | [](https://myanimelist.net/anime/53126/Yamada-kun_to_Lv999_no_Koi_wo_Suru) | [Yamada-kun to Lv999 no Koi wo Suru](https://subsplease.org/shows/yamada-kun-to-lv999-no-koi-wo-suru) | TV | 13 / 13 | **Finished Airing** | 7.78 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yamada+kun+to+Lv999+no+Koi+wo+Suru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53126__yamada_kun_to_lv999_no_koi_wo_suru.txt) | 41 | 15580 | 2023-06-24 17:01 | | 50612 | [](https://myanimelist.net/anime/50612/Dr_Stone__Ryuusui) | [Dr. Stone - Ryuusui](https://subsplease.org/shows/dr-stone-ryuusui) | TV Special | 1 / 1 | **Finished Airing** | 8.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dr+Stone+Ryuusui+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50612__dr_stone_ryuusui.txt) | 41 | 9486 | 2022-07-10 18:25 | | 58779 | [](https://myanimelist.net/anime/58779/Shi_Cao_Lao_Long_Bei_Guan_Yi_E_Long_Zhi_Ming_2nd_Season) | [A Herbivorous Dragon of 5000 Years Gets Unfairly Villainized S2](https://subsplease.org/shows/a-herbivorous-dragon-of-5000-years-gets-unfairly-villainized-s2) | ONA | 12 / 12 | **Finished Airing** | 6.77 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+A+Herbivorous+Dragon+of+5000+Years+Gets+Unfairly+Villainized+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58779__shi_cao_lao_long_bei_guan_yi_e_long_zhi_ming_2nd_season.txt) | 41 | 3013 | 2024-12-18 04:02 | | 54852 | [](https://myanimelist.net/anime/54852/Kikansha_no_Mahou_wa_Tokubetsu_desu) | [Kikansha no Mahou wa Tokubetsu desu](https://subsplease.org/shows/kikansha-no-mahou-wa-tokubetsu-desu) | TV | 12 / 12 | **Finished Airing** | 7.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kikansha+no+Mahou+wa+Tokubetsu+desu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54852__kikansha_no_mahou_wa_tokubetsu_desu.txt) | 41 | 11900 | 2023-12-23 17:37 | | 56230 | [](https://myanimelist.net/anime/56230/Jiisan_Baasan_Wakagaeru) | [Jiisan Baasan Wakagaeru](https://subsplease.org/shows/jiisan-baasan-wakagaeru) | TV | 11 / 11 | **Finished Airing** | 7.47 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jiisan+Baasan+Wakagaeru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56230__jiisan_baasan_wakagaeru.txt) | 40 | 8236 | 2024-06-16 15:02 | | 54790 | [](https://myanimelist.net/anime/54790/Undead_Girl_Murder_Farce) | [Undead Girl Murder Farce](https://subsplease.org/shows/undead-girl-murder-farce) | TV | 13 / 13 | **Finished Airing** | 7.86 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Undead+Girl+Murder+Farce+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54790__undead_girl_murder_farce.txt) | 40 | 13533 | 2023-09-27 17:26 | | 54041 | [](https://myanimelist.net/anime/54041/16bit_Sensation__Another_Layer) | [16bit Sensation - Another Layer](https://subsplease.org/shows/16bit-sensation-another-layer) | TV | 13 / 13 | **Finished Airing** | 6.9 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+16bit+Sensation+Another+Layer+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54041__16bit_sensation_another_layer.txt) | 40 | 8726 | 2023-12-27 18:03 | | 50796 | [](https://myanimelist.net/anime/50796/Kimi_wa_Houkago_Insomnia) | [Kimi wa Houkago Insomnia](https://subsplease.org/shows/kimi-wa-houkago-insomnia) | TV | 13 / 13 | **Finished Airing** | 8.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kimi+wa+Houkago+Insomnia+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50796__kimi_wa_houkago_insomnia.txt) | 40 | 12116 | 2023-07-03 16:00 | | 50582 | [](https://myanimelist.net/anime/50582/Nanatsu_no_Maken_ga_Shihai_suru) | [Nanatsu no Maken ga Shihai suru](https://subsplease.org/shows/nanatsu-no-maken-ga-shihai-suru) | TV | 15 / 15 | **Finished Airing** | 6.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Nanatsu+no+Maken+ga+Shihai+suru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50582__nanatsu_no_maken_ga_shihai_suru.txt) | 40 | 11834 | 2023-10-13 16:32 | | 56179 | [](https://myanimelist.net/anime/56179/Delicos_Nursery) | [Delico's Nursery](https://subsplease.org/shows/delicos-nursery) | TV | 14 / 13 | **Finished Airing** | 6.81 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Delico+s+Nursery+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56179__delico_s_nursery.txt) | 39 | 3170 | 2024-11-27 18:03 | | 51020 | [](https://myanimelist.net/anime/51020/Helck) | [Helck](https://subsplease.org/shows/helck) | TV | 24 / 24 | **Finished Airing** | 7.28 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Helck+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51020__helck.txt) | 39 | 15428 | 2023-12-19 18:31 | | 50346 | [](https://myanimelist.net/anime/50346/Yofukashi_no_Uta) | [Yofukashi no Uta](https://subsplease.org/shows/yofukashi-no-uta) | TV | 13 / 13 | **Finished Airing** | 7.96 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yofukashi+no+Uta+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50346__yofukashi_no_uta.txt) | 39 | 18093 | 2022-09-29 17:31 | | 49834 | [](https://myanimelist.net/anime/49834/Boku_ga_Aishita_Subete_no_Kimi_e) | [Boku ga Aishita Subete no Kimi e](https://subsplease.org/shows/boku-ga-aishita-subete-no-kimi-e) | Movie | 1 / 1 | **Finished Airing** | 7.49 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Boku+ga+Aishita+Subete+no+Kimi+e+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49834__boku_ga_aishita_subete_no_kimi_e.txt) | 39 | 5731 | 2023-04-21 06:02 | | 53889 | [](https://myanimelist.net/anime/53889/Ao_no_Exorcist__Shimane_Illuminati-hen) | [Ao no Exorcist - Shimane Illuminati-hen](https://subsplease.org/shows/ao-no-exorcist-shimane-illuminati-hen) | TV | 12 / 12 | **Finished Airing** | 7.05 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ao+no+Exorcist+Shimane+Illuminati+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53889__ao_no_exorcist_shimane_illuminati_hen.txt) | 39 | 10171 | 2024-03-23 17:01 | | 52359 | [](https://myanimelist.net/anime/52359/Isekai_de_Mofumofu_Nadenade_suru_Tame_ni_Ganbattemasu) | [Isekai de Mofumofu Nadenade suru Tame ni Ganbattemasu](https://subsplease.org/shows/isekai-de-mofumofu-nadenade-suru-tame-ni-ganbattemasu) | TV | 12 / 12 | **Finished Airing** | 6.77 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+de+Mofumofu+Nadenade+suru+Tame+ni+Ganbattemasu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52359__isekai_de_mofumofu_nadenade_suru_tame_ni_ganbattemasu.txt) | 38 | 9107 | 2024-03-17 14:01 | | 58357 | [](https://myanimelist.net/anime/58357/Tensui_no_Sakuna-hime) | [Tensui no Sakuna-hime](https://subsplease.org/shows/tensui-no-sakuna-hime) | TV | 13 / 13 | **Finished Airing** | 6.74 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tensui+no+Sakuna+hime+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58357__tensui_no_sakuna_hime.txt) | 38 | 4964 | 2024-09-28 15:32 | | 53111 | [](https://myanimelist.net/anime/53111/Dungeon_ni_Deai_wo_Motomeru_no_wa_Machigatteiru_Darou_ka_IV__Shin_Shou_-_Yakusai-hen) | [Dungeon ni Deai wo Motomeru no wa Machigatteiru Darou ka S4](https://subsplease.org/shows/dungeon-ni-deai-wo-motomeru-no-wa-machigatteiru-darou-ka-s4) | TV | 23 / 11 | **Finished Airing** | 8.25 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dungeon+ni+Deai+wo+Motomeru+no+wa+Machigatteiru+Darou+ka+S4+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53111__dungeon_ni_deai_wo_motomeru_no_wa_machigatteiru_darou_ka_iv_shin_shou_yakusai_hen.txt) | 38 | 13876 | 2023-03-16 14:01 | | 50869 | [](https://myanimelist.net/anime/50869/Kami_wa_Game_ni_Ueteiru) | [Kami wa Game ni Ueteiru](https://subsplease.org/shows/kami-wa-game-ni-ueteiru) | TV | 13 / 13 | **Finished Airing** | 6.27 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kami+wa+Game+ni+Ueteiru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50869__kami_wa_game_ni_ueteiru.txt) | 38 | 6308 | 2024-06-24 13:32 | | 50184 | [](https://myanimelist.net/anime/50184/Seiken_Gakuin_no_Makentsukai) | [Seiken Gakuin no Makentsukai](https://subsplease.org/shows/seiken-gakuin-no-makentsukai) | TV | 12 / 12 | **Finished Airing** | 6.19 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Seiken+Gakuin+no+Makentsukai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50184__seiken_gakuin_no_makentsukai.txt) | 38 | 12213 | 2023-12-18 17:02 | | 56980 | [](https://myanimelist.net/anime/56980/Karasu_wa_Aruji_wo_Erabanai) | [Karasu wa Aruji wo Erabanai](https://subsplease.org/shows/karasu-wa-aruji-wo-erabanai) | TV | 20 / 20 | **Finished Airing** | 8.06 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Karasu+wa+Aruji+wo+Erabanai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56980__karasu_wa_aruji_wo_erabanai.txt) | 38 | 4742 | 2024-09-21 18:30 | | 51815 | [](https://myanimelist.net/anime/51815/Kubo-san_wa_Mob_wo_Yurusanai) | [Kubo-san wa Mob wo Yurusanai](https://subsplease.org/shows/kubo-san-wa-mob-wo-yurusanai) | TV | 12 / 12 | **Finished Airing** | 7.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kubo+san+wa+Mob+wo+Yurusanai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51815__kubo_san_wa_mob_wo_yurusanai.txt) | 38 | 9737 | 2023-06-20 15:31 | | 50197 | [](https://myanimelist.net/anime/50197/Ijiranaide_Nagatoro-san_2nd_Attack) | [Ijiranaide, Nagatoro-san S2](https://subsplease.org/shows/ijiranaide-nagatoro-san-s2) | TV | 12 / 12 | **Finished Airing** | 7.47 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ijiranaide+Nagatoro+san+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50197__ijiranaide_nagatoro_san_2nd_attack.txt) | 38 | 7737 | 2023-03-18 17:31 | | 40028 | [](https://myanimelist.net/anime/40028/Shingeki_no_Kyojin__The_Final_Season) | [Shingeki no Kyojin (The Final Season)](https://subsplease.org/shows/shingeki-no-kyojin) | TV | 28 / 16 | **Finished Airing** | 8.78 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shingeki+no+Kyojin+The+Final+Season+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40028__shingeki_no_kyojin_the_final_season.txt) | 37 | 16087 | 2022-04-03 20:46 | | 53262 | [](https://myanimelist.net/anime/53262/Hoshikuzu_Telepath) | [Hoshikuzu Telepath](https://subsplease.org/shows/hoshikuzu-telepath) | TV | 12 / 12 | **Finished Airing** | 7.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hoshikuzu+Telepath+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53262__hoshikuzu_telepath.txt) | 37 | 5399 | 2023-12-25 13:32 | | 54632 | [](https://myanimelist.net/anime/54632/Gekai_Elise) | [Gekai Elise](https://subsplease.org/shows/gekai-elise) | TV | 12 / 12 | **Finished Airing** | 7.25 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gekai+Elise+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54632__gekai_elise.txt) | 36 | 7697 | 2024-03-27 13:31 | | 55774 | [](https://myanimelist.net/anime/55774/Himesama_Goumon_no_Jikan_desu) | [Hime-sama Goumon no Jikan desu](https://subsplease.org/shows/hime-sama-goumon-no-jikan-desu) | TV | 12 / 12 | **Finished Airing** | 7.25 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hime+sama+Goumon+no+Jikan+desu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55774__himesama_goumon_no_jikan_desu.txt) | 36 | 8165 | 2024-03-25 16:31 | | 53446 | [](https://myanimelist.net/anime/53446/Tondemo_Skill_de_Isekai_Hourou_Meshi) | [Tondemo Skill de Isekai Hourou Meshi](https://subsplease.org/shows/tondemo-skill-de-isekai-hourou-meshi) | TV | 12 / 12 | **Finished Airing** | 7.65 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tondemo+Skill+de+Isekai+Hourou+Meshi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53446__tondemo_skill_de_isekai_hourou_meshi.txt) | 36 | 11039 | 2023-03-28 16:31 | | 52305 | [](https://myanimelist.net/anime/52305/Tomo-chan_wa_Onnanoko) | [Tomo-chan wa Onnanoko!](https://subsplease.org/shows/tomo-chan-wa-onnanoko) | TV | 13 / 13 | **Finished Airing** | 7.75 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tomo+chan+wa+Onnanoko+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52305__tomo_chan_wa_onnanoko.txt) | 36 | 11590 | 2023-03-29 17:01 | | 51764 | [](https://myanimelist.net/anime/51764/Level_1_dakedo_Unique_Skill_de_Saikyou_desu) | [Level 1 dakedo Unique Skill de Saikyou desu](https://subsplease.org/shows/level-1-dakedo-unique-skill-de-saikyou-desu) | TV | 12 / 12 | **Finished Airing** | 6.22 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Level+1+dakedo+Unique+Skill+de+Saikyou+desu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51764__level_1_dakedo_unique_skill_de_saikyou_desu.txt) | 36 | 10251 | 2023-09-23 14:06 | | 54789 | [](https://myanimelist.net/anime/54789/Boku_no_Hero_Academia_7th_Season) | [Boku no Hero Academia](https://subsplease.org/shows/boku-no-hero-academia) | TV | 52 / 21 | **Finished Airing** | 8.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Boku+no+Hero+Academia+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54789__boku_no_hero_academia_7th_season.txt) | 36 | 16555 | 2024-10-12 09:32 | | 51215 | [](https://myanimelist.net/anime/51215/Seijo_no_Maryoku_wa_Bannou_desu_Season_2) | [Seijo no Maryoku wa Bannou Desu S2](https://subsplease.org/shows/seijo-no-maryoku-wa-bannou-desu-s2) | TV | 12 / 12 | **Finished Airing** | 7.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Seijo+no+Maryoku+wa+Bannou+Desu+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51215__seijo_no_maryoku_wa_bannou_desu_season_2.txt) | 36 | 9425 | 2023-12-19 16:41 | | 48736 | [](https://myanimelist.net/anime/48736/Sono_Bisque_Doll_wa_Koi_wo_Suru) | [Sono Bisque Doll wa Koi wo Suru](https://subsplease.org/shows/sono-bisque-doll-wa-koi-wo-suru) | TV | 12 / 12 | **Finished Airing** | 8.16 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sono+Bisque+Doll+wa+Koi+wo+Suru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48736__sono_bisque_doll_wa_koi_wo_suru.txt) | 36 | 17044 | 2022-03-26 16:31 | | 53879 | [](https://myanimelist.net/anime/53879/Kamonohashi_Ron_no_Kindan_Suiri) | [Kamonohashi Ron no Kindan Suiri](https://subsplease.org/shows/kamonohashi-ron-no-kindan-suiri) | TV | 26 / 13 | **Finished Airing** | 7.5 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kamonohashi+Ron+no+Kindan+Suiri+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53879__kamonohashi_ron_no_kindan_suiri.txt) | 35 | 5032 | 2024-12-30 15:32 | | 51693 | [](https://myanimelist.net/anime/51693/Kaminaki_Sekai_no_Kamisama_Katsudou) | [Kaminaki Sekai no Kamisama Katsudou](https://subsplease.org/shows/kaminaki-sekai-no-kamisama-katsudou) | TV | 12 / 12 | **Finished Airing** | 6.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kaminaki+Sekai+no+Kamisama+Katsudou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51693__kaminaki_sekai_no_kamisama_katsudou.txt) | 35 | 13377 | 2023-07-05 16:31 | | 49596 | [](https://myanimelist.net/anime/49596/Blue_Lock) | [Blue Lock](https://subsplease.org/shows/blue-lock) | TV | 38 / 24 | **Finished Airing** | 8.21 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Blue+Lock+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49596__blue_lock.txt) | 35 | 11178 | 2024-12-28 17:16 | | 54431 | [](https://myanimelist.net/anime/54431/Toaru_Ossan_no_VRMMO_Katsudouki) | [Toaru Ossan no VRMMO Katsudouki](https://subsplease.org/shows/toaru-ossan-no-vrmmo-katsudouki) | TV | 12 / 12 | **Finished Airing** | 6.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Toaru+Ossan+no+VRMMO+Katsudouki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54431__toaru_ossan_no_vrmmo_katsudouki.txt) | 34 | 9360 | 2023-12-18 18:05 | | 53237 | [](https://myanimelist.net/anime/53237/Shy) | [SHY](https://subsplease.org/shows/shy) | TV | 24 / 12 | **Finished Airing** | 6.72 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+SHY+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53237__shy.txt) | 34 | 6114 | 2024-09-23 16:02 | | 53450 | [](https://myanimelist.net/anime/53450/Xian_Wang_de_Richang_Shenghuo_4) | [The Daily Life of the Immortal King S4](https://subsplease.org/shows/the-daily-life-of-the-immortal-king-s4) | ONA | 12 / 12 | **Finished Airing** | 7.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+Daily+Life+of+the+Immortal+King+S4+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53450__xian_wang_de_richang_shenghuo_4.txt) | 34 | 4908 | 2024-02-25 04:01 | | 51461 | [](https://myanimelist.net/anime/51461/Tonari_no_Youkai-san) | [Tonari no Youkai-san](https://subsplease.org/shows/tonari-no-youkai-san) | TV | 13 / 13 | **Finished Airing** | 7.29 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tonari+no+Youkai+san+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51461__tonari_no_youkai_san.txt) | 34 | 3985 | 2024-06-29 18:47 | | 50710 | [](https://myanimelist.net/anime/50710/Urusei_Yatsura_2022) | [Urusei Yatsura (2022)](https://subsplease.org/shows/urusei-yatsura-2022) | TV | 46 / 23 | **Finished Airing** | 7.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Urusei+Yatsura+2022+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50710__urusei_yatsura_2022.txt) | 34 | 7051 | 2024-06-21 15:34 | | 50416 | [](https://myanimelist.net/anime/50416/Skip_to_Loafer) | [Skip to Loafer](https://subsplease.org/shows/skip-to-loafer) | TV | 12 / 12 | **Finished Airing** | 8.12 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Skip+to+Loafer+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50416__skip_to_loafer.txt) | 34 | 11555 | 2023-06-20 15:31 | | 50307 | [](https://myanimelist.net/anime/50307/Tonikaku_Kawaii_2nd_Season) | [Tonikaku Kawaii S2](https://subsplease.org/shows/tonikaku-kawaii-s2) | TV | 12 / 12 | **Finished Airing** | 7.61 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tonikaku+Kawaii+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50307__tonikaku_kawaii_2nd_season.txt) | 34 | 7719 | 2023-06-23 17:16 | | 49835 | [](https://myanimelist.net/anime/49835/Kimi_wo_Aishita_Hitori_no_Boku_e) | [Kimi wo Aishita Hitori no Boku e](https://subsplease.org/shows/kimi-wo-aishita-hitori-no-boku-e) | Movie | 1 / 1 | **Finished Airing** | 7.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kimi+wo+Aishita+Hitori+no+Boku+e+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49835__kimi_wo_aishita_hitori_no_boku_e.txt) | 34 | 5448 | 2023-04-21 06:03 | | 50481 | [](https://myanimelist.net/anime/50481/Eiyuuou_Bu_wo_Kiwameru_Tame_Tenseisu__Soshite_Sekai_Saikyou_no_Minarai_Kishi♀) | [Eiyuuou, Bu wo Kiwameru Tame Tenseisu](https://subsplease.org/shows/eiyuuou-bu-wo-kiwameru-tame-tenseisu) | TV | 12 / 12 | **Finished Airing** | 6.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Eiyuuou+Bu+wo+Kiwameru+Tame+Tenseisu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50481__eiyuuou_bu_wo_kiwameru_tame_tenseisu_soshite_sekai_saikyou_no_minarai_kishi.txt) | 34 | 8505 | 2023-03-27 18:16 | | 51252 | [](https://myanimelist.net/anime/51252/Spy_Kyoushitsu) | [Spy Kyoushitsu](https://subsplease.org/shows/spy-kyoushitsu) | TV | 24 / 12 | **Finished Airing** | 6.4 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Spy+Kyoushitsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51252__spy_kyoushitsu.txt) | 33 | 7314 | 2023-09-28 17:32 | | 50854 | [](https://myanimelist.net/anime/50854/Benriya_Saitou-san_Isekai_ni_Iku) | [Benriya Saitou-san, Isekai ni Iku](https://subsplease.org/shows/benriya-saitou-san-isekai-ni-iku) | TV | 12 / 12 | **Finished Airing** | 7.4 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Benriya+Saitou+san+Isekai+ni+Iku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50854__benriya_saitou_san_isekai_ni_iku.txt) | 33 | 9683 | 2023-03-26 14:32 | | 50583 | [](https://myanimelist.net/anime/50583/Buta_no_Liver_wa_Kanetsu_Shiro) | [Buta no Liver wa Kanetsu Shiro](https://subsplease.org/shows/buta-no-liver-wa-kanetsu-shiro) | TV | 12 / 12 | **Finished Airing** | 6.3 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Buta+no+Liver+wa+Kanetsu+Shiro+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50583__buta_no_liver_wa_kanetsu_shiro.txt) | 33 | 6316 | 2024-02-06 05:24 | | 49891 | [](https://myanimelist.net/anime/49891/Tensei_shitara_Ken_deshita) | [Tensei Shitara Ken Deshita](https://subsplease.org/shows/tensei-shitara-ken-deshita) | TV | 12 / 12 | **Finished Airing** | 7.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tensei+Shitara+Ken+Deshita+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49891__tensei_shitara_ken_deshita.txt) | 33 | 14002 | 2022-12-21 14:51 | | 49709 | [](https://myanimelist.net/anime/49709/Fumetsu_no_Anata_e_Season_2) | [Fumetsu no Anata e S2](https://subsplease.org/shows/fumetsu-no-anata-e-s2) | TV | 20 / 20 | **Finished Airing** | 8.09 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fumetsu+no+Anata+e+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49709__fumetsu_no_anata_e_season_2.txt) | 33 | 7265 | 2023-03-12 12:31 | | 57325 | [](https://myanimelist.net/anime/57325/Ramen_Akaneko) | [Ramen Akaneko](https://subsplease.org/shows/ramen-akaneko) | TV | 12 / 12 | **Finished Airing** | 7.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ramen+Akaneko+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57325__ramen_akaneko.txt) | 32 | 3463 | 2024-09-19 16:01 | | 54743 | [](https://myanimelist.net/anime/54743/Dead_Mount_Death_Play_Part_2) | [Dead Mount Death Play](https://subsplease.org/shows/dead-mount-death-play) | TV | 24 / 12 | **Finished Airing** | 7.45 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dead+Mount+Death+Play+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54743__dead_mount_death_play_part_2.txt) | 32 | 13135 | 2023-12-25 16:31 | | 54301 | [](https://myanimelist.net/anime/54301/Overtake) | [Overtake!](https://subsplease.org/shows/overtake) | TV | 12 / 12 | **Finished Airing** | 7.49 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Overtake+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54301__overtake.txt) | 32 | 6668 | 2023-12-17 15:37 | | 53494 | [](https://myanimelist.net/anime/53494/Boukensha_ni_Naritai_to_Miyako_ni_Deteitta_Musume_ga_S-Rank_ni_Natteta) | [S-Rank Musume](https://subsplease.org/shows/s-rank-musume) | TV | 13 / 13 | **Finished Airing** | 6.76 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+S+Rank+Musume+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53494__boukensha_ni_naritai_to_miyako_ni_deteitta_musume_ga_s_rank_ni_natteta.txt) | 32 | 10546 | 2023-12-21 15:05 | | 50803 | [](https://myanimelist.net/anime/50803/Jaku-Chara_Tomozaki-kun_2nd_Stage) | [Jaku-Chara Tomozaki-kun S2](https://subsplease.org/shows/jaku-chara-tomozaki-kun-s2) | TV | 13 / 13 | **Finished Airing** | 7.04 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jaku+Chara+Tomozaki+kun+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50803__jaku_chara_tomozaki_kun_2nd_stage.txt) | 32 | 6767 | 2024-03-27 12:32 | | 48633 | [](https://myanimelist.net/anime/48633/Liar_Liar) | [Liar Liar](https://subsplease.org/shows/liar-liar) | TV | 12 / 12 | **Finished Airing** | 6.3 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Liar+Liar+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48633__liar_liar.txt) | 32 | 8920 | 2023-09-16 15:01 | | 53040 | [](https://myanimelist.net/anime/53040/Kanojo_mo_Kanojo_Season_2) | [Kanojo mo Kanojo S2](https://subsplease.org/shows/kanojo-mo-kanojo-s2) | TV | 12 / 12 | **Finished Airing** | 7.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kanojo+mo+Kanojo+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53040__kanojo_mo_kanojo_season_2.txt) | 32 | 7303 | 2023-12-22 20:01 | | 51678 | [](https://myanimelist.net/anime/51678/Oniichan_wa_Oshimai) | [Oniichan wa Oshimai!](https://subsplease.org/shows/oniichan-wa-oshimai) | TV | 12 / 12 | **Finished Airing** | 7.66 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Oniichan+wa+Oshimai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51678__oniichan_wa_oshimai.txt) | 32 | 5753 | 2023-03-23 15:31 | | 51462 | [](https://myanimelist.net/anime/51462/Isekai_Nonbiri_Nouka) | [Isekai Nonbiri Nouka](https://subsplease.org/shows/isekai-nonbiri-nouka) | TV | 12 / 12 | **Finished Airing** | 7.55 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+Nonbiri+Nouka+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51462__isekai_nonbiri_nouka.txt) | 32 | 13546 | 2023-03-24 13:02 | | 58302 | [](https://myanimelist.net/anime/58302/The_iDOLMSTER_Shiny_Colors_2nd_Season) | [The iDOLM@STER Shiny Colors S2](https://subsplease.org/shows/the-idolmster-shiny-colors-s2) | TV | 12 / 12 | **Finished Airing** | 6.29 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+iDOLM+STER+Shiny+Colors+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/58302__the_idolm_ster_shiny_colors_2nd_season.txt) | 31 | 1814 | 2024-12-20 18:32 | | 57184 | [](https://myanimelist.net/anime/57184/Great_Pretender__Razbliuto) | [Great Pretender - Razbliuto](https://subsplease.org/shows/great-pretender-razbliuto) | ONA | 1 / 4 | **Finished Airing** | 6.8 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Great+Pretender+Razbliuto+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57184__great_pretender_razbliuto.txt) | 31 | 5128 | 2024-02-24 05:07 | | 54234 | [](https://myanimelist.net/anime/54234/Suki_na_Ko_ga_Megane_wo_Wasureta) | [Suki na Ko ga Megane wo Wasureta](https://subsplease.org/shows/suki-na-ko-ga-megane-wo-wasureta) | TV | 13 / 13 | **Finished Airing** | 7.14 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Suki+na+Ko+ga+Megane+wo+Wasureta+1080p+mkv) | 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[Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52093__trigun_stampede.txt) | 30 | 10461 | 2023-03-25 15:32 | | 41497 | [](https://myanimelist.net/anime/41497/Fate_Grand_Order__Shuukyoku_Tokuiten_-_Kani_Jikan_Shinden_Solomon) | [Fate Grand Order - Final Singularity - The Grand Temple of Time Solomon](https://subsplease.org/shows/fate-grand-order-final-singularity-the-grand-temple-of-time-solomon-2) | Movie | 1 / 1 | **Finished Airing** | 7.74 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fate+Grand+Order+Final+Singularity+The+Grand+Temple+of+Time+Solomon+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41497__fate_grand_order_shuukyoku_tokuiten_kani_jikan_shinden_solomon.txt) | 30 | 5106 | 2022-02-18 21:46 | | 54760 | [](https://myanimelist.net/anime/54760/Ryza_no_Atelier__Tokoyami_no_Joou_to_Himitsu_no_Kakurega) | [Ryza no Atelier](https://subsplease.org/shows/ryza-no-atelier) | TV | 12 / 12 | **Finished Airing** | 6.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ryza+no+Atelier+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54760__ryza_no_atelier_tokoyami_no_joou_to_himitsu_no_kakurega.txt) | 29 | 8375 | 2023-09-16 16:31 | | 52505 | [](https://myanimelist.net/anime/52505/Dark_Gathering) | [Dark Gathering](https://subsplease.org/shows/dark-gathering) | TV | 25 / 25 | **Finished Airing** | 7.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dark+Gathering+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52505__dark_gathering.txt) | 29 | 10525 | 2023-12-24 17:00 | | 51916 | [](https://myanimelist.net/anime/51916/Dekiru_Neko_wa_Kyou_mo_Yuuutsu) | [Dekiru Neko wa Kyou mo Yuuutsu](https://subsplease.org/shows/dekiru-neko-wa-kyou-mo-yuuutsu) | TV | 13 / 13 | **Finished Airing** | 7.65 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dekiru+Neko+wa+Kyou+mo+Yuuutsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51916__dekiru_neko_wa_kyou_mo_yuuutsu.txt) | 29 | 8177 | 2023-09-29 18:46 | | 43608 | [](https://myanimelist.net/anime/43608/Kaguya-sama_wa_Kokurasetai__Ultra_Romantic) | [Kaguya-sama wa Kokurasetai S3](https://subsplease.org/shows/kaguya-sama-wa-kokurasetai-s3) | TV | 13 / 13 | **Finished Airing** | 8.99 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kaguya+sama+wa+Kokurasetai+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43608__kaguya_sama_wa_kokurasetai_ultra_romantic.txt) | 29 | 11041 | 2022-06-26 02:10 | | 35678 | [](https://myanimelist.net/anime/35678/Hibike_Euphonium_Movie_3__Chikai_no_Finale) | [Hibike! Euphonium - Chikai no Finale](https://subsplease.org/shows/hibike-euphonium-chikai-no-finale) | Movie | 1 / 1 | **Finished Airing** | 7.76 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hibike+Euphonium+Chikai+no+Finale+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/35678__hibike_euphonium_movie_3_chikai_no_finale.txt) | 29 | 3584 | 2024-04-17 03:03 | | 52608 | [](https://myanimelist.net/anime/52608/Tensei_Kizoku_no_Isekai_Boukenroku__Jichou_wo_Shiranai_Kamigami_no_Shito) | [Tensei Kizoku no Isekai Boukenroku](https://subsplease.org/shows/tensei-kizoku-no-isekai-boukenroku) | TV | 12 / 12 | **Finished Airing** | 6.68 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tensei+Kizoku+no+Isekai+Boukenroku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52608__tensei_kizoku_no_isekai_boukenroku_jichou_wo_shiranai_kamigami_no_shito.txt) | 29 | 10582 | 2023-06-18 13:01 | | 52081 | [](https://myanimelist.net/anime/52081/Edomae_Elf) | [Edomae Elf](https://subsplease.org/shows/edomae-elf) | TV | 12 / 12 | **Finished Airing** | 7.26 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Edomae+Elf+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52081__edomae_elf.txt) | 29 | 8609 | 2023-06-23 18:26 | | 49766 | [](https://myanimelist.net/anime/49766/Under_Ninja) | [Under Ninja](https://subsplease.org/shows/under-ninja) | TV | 12 / 12 | **Finished Airing** | 6.9 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Under+Ninja+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49766__under_ninja.txt) | 29 | 8120 | 2023-12-21 18:51 | | 52308 | [](https://myanimelist.net/anime/52308/Kanojo_ga_Koushaku-tei_ni_Itta_Riyuu) | [Kanojo ga Koushaku-tei ni Itta Riyuu](https://subsplease.org/shows/kanojo-ga-koushaku-tei-ni-itta-riyuu) | TV | 12 / 12 | **Finished Airing** | 7.53 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kanojo+ga+Koushaku+tei+ni+Itta+Riyuu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52308__kanojo_ga_koushaku_tei_ni_itta_riyuu.txt) | 28 | 5401 | 2023-06-26 13:01 | | 53632 | [](https://myanimelist.net/anime/53632/Yumemiru_Danshi_wa_Genjitsushugisha) | [Yumemiru Danshi wa Genjitsushugisha](https://subsplease.org/shows/yumemiru-danshi-wa-genjitsushugisha) | TV | 12 / 12 | **Finished Airing** | 6.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yumemiru+Danshi+wa+Genjitsushugisha+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53632__yumemiru_danshi_wa_genjitsushugisha.txt) | 28 | 8799 | 2023-09-18 18:01 | | 52082 | [](https://myanimelist.net/anime/52082/Shiro_Seijo_to_Kuro_Bokushi) | [Shiro Seijo to Kuro Bokushi](https://subsplease.org/shows/shiro-seijo-to-kuro-bokushi) | TV | 12 / 12 | **Finished Airing** | 7.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shiro+Seijo+to+Kuro+Bokushi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52082__shiro_seijo_to_kuro_bokushi.txt) | 28 | 6139 | 2023-09-27 17:02 | | 49413 | [](https://myanimelist.net/anime/49413/Shiguang_Dailiren_II) | [Link Click S2](https://subsplease.org/shows/link-click-s2) | ONA | 12 / 12 | **Finished Airing** | 8.64 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Link+Click+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49413__shiguang_dailiren_ii.txt) | 28 | 5578 | 2023-09-22 04:01 | | 52985 | [](https://myanimelist.net/anime/52985/Dekoboko_Majo_no_Oyako_Jijou) | [Dekoboko Majo no Oyako Jijou](https://subsplease.org/shows/dekoboko-majo-no-oyako-jijou) | TV | 12 / 12 | **Finished Airing** | 6.77 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dekoboko+Majo+no+Oyako+Jijou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52985__dekoboko_majo_no_oyako_jijou.txt) | 28 | 6854 | 2023-12-10 15:21 | | 52934 | [](https://myanimelist.net/anime/52934/Konyaku_Haki_sareta_Reijou_wo_Hirotta_Ore_ga_Ikenai_Koto_wo_Oshiekomu) | [Ikenaikyo](https://subsplease.org/shows/ikenaikyo) | TV | 12 / 12 | **Finished Airing** | 7.16 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ikenaikyo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52934__konyaku_haki_sareta_reijou_wo_hirotta_ore_ga_ikenai_koto_wo_oshiekomu.txt) | 28 | 7713 | 2023-12-20 14:25 | | 50220 | [](https://myanimelist.net/anime/50220/Isekai_Shoukan_wa_Nidome_desu) | [Isekai Shoukan wa Nidome desu](https://subsplease.org/shows/isekai-shoukan-wa-nidome-desu) | TV | 12 / 12 | **Finished Airing** | 5.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+Shoukan+wa+Nidome+desu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50220__isekai_shoukan_wa_nidome_desu.txt) | 28 | 10339 | 2023-06-24 18:46 | | 55651 | [](https://myanimelist.net/anime/55651/Tonikaku_Kawaii__Joshikou-hen) | [Tonikaku Kawaii - Joshikou-hen](https://subsplease.org/shows/tonikaku-kawaii-joshikou-hen) | ONA | 4 / 4 | **Finished Airing** | 7.76 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tonikaku+Kawaii+Joshikou+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55651__tonikaku_kawaii_joshikou_hen.txt) | 27 | 5180 | 2023-08-23 03:31 | | 53526 | [](https://myanimelist.net/anime/53526/Uma_Musume__Pretty_Derby_Season_3) | [Uma Musume - Pretty Derby S3](https://subsplease.org/shows/uma-musume-pretty-derby-s3) | TV | 13 / 13 | **Finished Airing** | 7.59 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Uma+Musume+Pretty+Derby+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53526__uma_musume_pretty_derby_season_3.txt) | 27 | 5149 | 2023-12-27 17:01 | | 53263 | [](https://myanimelist.net/anime/53263/Seija_Musou__Salaryman_Isekai_de_Ikinokoru_Tame_ni_Ayumu_Michi) | [Seija Musou](https://subsplease.org/shows/seija-musou) | TV | 12 / 12 | **Finished Airing** | 7.11 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Seija+Musou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53263__seija_musou_salaryman_isekai_de_ikinokoru_tame_ni_ayumu_michi.txt) | 27 | 9861 | 2023-09-21 17:58 | | 51706 | [](https://myanimelist.net/anime/51706/Yuusha_ga_Shinda) | [Yuusha ga Shinda!](https://subsplease.org/shows/yuusha-ga-shinda) | TV | 12 / 12 | **Finished Airing** | 6.61 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yuusha+ga+Shinda+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51706__yuusha_ga_shinda.txt) | 27 | 9399 | 2023-06-22 16:31 | | 49894 | [](https://myanimelist.net/anime/49894/Eiyuu_Kyoushitsu) | [Eiyuu Kyoushitsu](https://subsplease.org/shows/eiyuu-kyoushitsu) | TV | 12 / 12 | **Finished Airing** | 6.05 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Eiyuu+Kyoushitsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49894__eiyuu_kyoushitsu.txt) | 27 | 8942 | 2023-09-24 14:31 | | 45486 | [](https://myanimelist.net/anime/45486/Kuma_Kuma_Kuma_Bear_Punch) | [Kuma Kuma Kuma Bear S2](https://subsplease.org/shows/kuma-kuma-kuma-bear-s2) | TV | 12 / 12 | **Finished Airing** | 7.28 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kuma+Kuma+Kuma+Bear+S2+1080p+mkv) | 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[Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kuroshitsuji+Kishuku+Gakkou+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55855__kuroshitsuji_kishuku_gakkou_hen.txt) | 26 | 4880 | 2024-06-22 16:02 | | 54857 | [](https://myanimelist.net/anime/54857/Re_Zero_kara_Hajimeru_Isekai_Seikatsu_3rd_Season) | [Re Zero kara Hajimeru Isekai Seikatsu](https://subsplease.org/shows/re-zero-kara-hajimeru-isekai-seikatsu) | TV | 20 / 16 | Currently Airing | 8.69 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Re+Zero+kara+Hajimeru+Isekai+Seikatsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54857__re_zero_kara_hajimeru_isekai_seikatsu_3rd_season.txt) | 26 | 17652 | 2024-11-20 15:30 | | 54616 | [](https://myanimelist.net/anime/54616/Potion-danomi_de_Ikinobimasu) | [Potion-danomi de Ikinobimasu!](https://subsplease.org/shows/potion-danomi-de-ikinobimasu) | TV | 12 / 12 | **Finished Airing** | 6.55 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Potion+danomi+de+Ikinobimasu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54616__potion_danomi_de_ikinobimasu.txt) | 26 | 7069 | 2023-12-24 02:24 | | 53200 | [](https://myanimelist.net/anime/53200/Hataraku_Maou-sama_2nd_Season) | [Hataraku Maou-sama S2](https://subsplease.org/shows/hataraku-maou-sama-s2) | TV | 25 / 12 | **Finished Airing** | 6.66 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hataraku+Maou+sama+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53200__hataraku_maou_sama_2nd_season.txt) | 26 | 8686 | 2023-09-28 14:31 | | 52611 | [](https://myanimelist.net/anime/52611/Okashi_na_Tensei) | [Okashi na Tensei](https://subsplease.org/shows/okashi-na-tensei) | TV | 12 / 12 | **Finished Airing** | 6.7 | 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[Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Akuyaku+Reijou+nanode+Last+Boss+wo+Kattemimashita+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49979__akuyaku_reijou_nanode_last_boss_wo_kattemimashita.txt) | 26 | 5416 | 2022-12-10 15:01 | | 48981 | [](https://myanimelist.net/anime/48981/Mahou_Shoujo_Magical_Destroyers) | [Mahou Shoujo Magical Destroyers](https://subsplease.org/shows/mahou-shoujo-magical-destroyers) | TV | 12 / 12 | **Finished Airing** | 6.27 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahou+Shoujo+Magical+Destroyers+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48981__mahou_shoujo_magical_destroyers.txt) | 26 | 6936 | 2023-06-23 18:46 | | 53438 | [](https://myanimelist.net/anime/53438/Higeki_no_Genkyou_to_Naru_Saikyou_Gedou_Last_Boss_Joou_wa_Tami_no_Tame_ni_Tsukushimasu) | [LasTame](https://subsplease.org/shows/lastame) | TV | 12 / 12 | **Finished Airing** | 7.11 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+LasTame+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53438__higeki_no_genkyou_to_naru_saikyou_gedou_last_boss_joou_wa_tami_no_tame_ni_tsukushimasu.txt) | 26 | 8950 | 2023-09-21 15:31 | | 53379 | [](https://myanimelist.net/anime/53379/Uchi_no_Kaisha_no_Chiisai_Senpai_no_Hanashi) | [Uchi no Kaisha no Chiisai Senpai no Hanashi](https://subsplease.org/shows/uchi-no-kaisha-no-chiisai-senpai-no-hanashi) | TV | 12 / 12 | **Finished Airing** | 6.9 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Uchi+no+Kaisha+no+Chiisai+Senpai+no+Hanashi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53379__uchi_no_kaisha_no_chiisai_senpai_no_hanashi.txt) | 25 | 6933 | 2023-09-30 18:16 | | 50932 | [](https://myanimelist.net/anime/50932/Saikyou_Onmyouji_no_Isekai_Tenseiki) | [Saikyou Onmyouji no Isekai Tenseiki](https://subsplease.org/shows/saikyou-onmyouji-no-isekai-tenseiki) | TV | 13 / 13 | **Finished Airing** | 7.13 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Saikyou+Onmyouji+no+Isekai+Tenseiki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50932__saikyou_onmyouji_no_isekai_tenseiki.txt) | 25 | 9777 | 2023-04-01 17:16 | | 48926 | [](https://myanimelist.net/anime/48926/Komi-san_wa_Comyushou_desu) | [Komi-san wa, Comyushou desu.](https://subsplease.org/shows/komi-san-wa-comyushou-desu) | TV | 12 / 12 | **Finished Airing** | 7.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Komi+san+wa+Comyushou+desu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48926__komi_san_wa_comyushou_desu.txt) | 25 | 9945 | 2022-01-06 23:43 | | 52446 | [](https://myanimelist.net/anime/52446/Kaiko_sareta_Ankoku_Heishi_30-dai_no_Slow_na_Second_Life) | [Kaiko sareta Ankoku Heishi (30-dai) no Slow na Second Life](https://subsplease.org/shows/kaiko-sareta-ankoku-heishi-30-dai-no-slow-na-second-life) | TV | 12 / 12 | **Finished Airing** | 7.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kaiko+sareta+Ankoku+Heishi+30+dai+no+Slow+na+Second+Life+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52446__kaiko_sareta_ankoku_heishi_30_dai_no_slow_na_second_life.txt) | 25 | 7738 | 2023-03-25 14:01 | | 52405 | [](https://myanimelist.net/anime/52405/Highspeed_Etoile) | [Highspeed Etoile](https://subsplease.org/shows/highspeed-etoile) | TV | 12 / 12 | **Finished Airing** | 6.16 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Highspeed+Etoile+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52405__highspeed_etoile.txt) | 25 | 3855 | 2024-06-21 18:01 | | 51632 | [](https://myanimelist.net/anime/51632/Isekai_wa_Smartphone_to_Tomo_ni_2) | [Isekai wa Smartphone to Tomo ni S2](https://subsplease.org/shows/isekai-wa-smartphone-to-tomo-ni-s2) | TV | 12 / 12 | **Finished Airing** | 6.42 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+wa+Smartphone+to+Tomo+ni+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51632__isekai_wa_smartphone_to_tomo_ni_2.txt) | 25 | 7218 | 2023-06-19 16:01 | | 57945 | [](https://myanimelist.net/anime/57945/Tasuuketsu) | [Tasuuketsu](https://subsplease.org/shows/tasuuketsu) | TV | 24 / 24 | **Finished Airing** | 5.54 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tasuuketsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57945__tasuuketsu.txt) | 24 | 2890 | 2024-12-24 20:02 | | 52830 | [](https://myanimelist.net/anime/52830/Isekai_de_Cheat_Skill_wo_Te_ni_Shita_Ore_wa_Genjitsu_Sekai_wo_mo_Musou_Suru__Level_Up_wa_Jinsei_wo_Kaeta) | [Iseleve](https://subsplease.org/shows/iseleve) | TV | 13 / 13 | **Finished Airing** | 6.34 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Iseleve+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52830__isekai_de_cheat_skill_wo_te_ni_shita_ore_wa_genjitsu_sekai_wo_mo_musou_suru_level_up_wa_jinsei_wo_kaeta.txt) | 24 | 15233 | 2023-06-28 17:05 | | 49109 | [](https://myanimelist.net/anime/49109/Kami-tachi_ni_Hirowareta_Otoko_2nd_Season) | [Kami-tachi ni Hirowareta Otoko S2](https://subsplease.org/shows/kami-tachi-ni-hirowareta-otoko-s2) | TV | 12 / 12 | **Finished Airing** | 6.8 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kami+tachi+ni+Hirowareta+Otoko+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49109__kami_tachi_ni_hirowareta_otoko_2nd_season.txt) | 24 | 5145 | 2023-03-26 13:04 | | 44204 | [](https://myanimelist.net/anime/44204/Kyokou_Suiri_Season_2) | [Kyokou Suiri](https://subsplease.org/shows/kyokou-suiri) | TV | 12 / 12 | **Finished Airing** | 7.36 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kyokou+Suiri+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44204__kyokou_suiri_season_2.txt) | 24 | 4680 | 2023-03-26 15:32 | | 52657 | [](https://myanimelist.net/anime/52657/Ousama_Ranking__Yuuki_no_Takarabako) | [Ousama Ranking - Yuuki no Takarabako](https://subsplease.org/shows/ousama-ranking-yuuki-no-takarabako) | TV | 10 / 10 | **Finished Airing** | 7.34 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ousama+Ranking+Yuuki+no+Takarabako+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52657__ousama_ranking_yuuki_no_takarabako.txt) | 23 | 7732 | 2023-06-15 18:02 | | 57391 | [](https://myanimelist.net/anime/57391/Astro_Note) | [Astro Note](https://subsplease.org/shows/astro-note) | TV | 12 / 12 | **Finished Airing** | 6.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Astro+Note+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57391__astro_note.txt) | 23 | 4812 | 2024-06-21 14:02 | | 53050 | [](https://myanimelist.net/anime/53050/Kanojo_Okarishimasu_3rd_Season) | [Kanojo, Okarishimasu](https://subsplease.org/shows/kanojo-okarishimasu) | TV | 24 / 12 | **Finished Airing** | 7.03 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kanojo+Okarishimasu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53050__kanojo_okarishimasu_3rd_season.txt) | 23 | 6381 | 2023-09-29 18:31 | | 52990 | [](https://myanimelist.net/anime/52990/Keikenzumi_na_Kimi_to_Keiken_Zero_na_Ore_ga_Otsukiai_suru_Hanashi) | [Kimizero](https://subsplease.org/shows/kimizero) | TV | 12 / 12 | **Finished Airing** | 6.75 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kimizero+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52990__keikenzumi_na_kimi_to_keiken_zero_na_ore_ga_otsukiai_suru_hanashi.txt) | 23 | 7678 | 2023-12-22 15:35 | | 51498 | [](https://myanimelist.net/anime/51498/Masamune-kun_no_Revenge_R) | [Masamune-kun no Revenge S2](https://subsplease.org/shows/masamune-kun-no-revenge-s2) | TV | 12 / 12 | **Finished Airing** | 7.23 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Masamune+kun+no+Revenge+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51498__masamune_kun_no_revenge_r.txt) | 23 | 7187 | 2023-09-18 13:01 | | 51219 | [](https://myanimelist.net/anime/51219/Isekai_One_Turn_Kill_Neesan__Ane_Douhan_no_Isekai_Seikatsu_Hajimemashita) | [Isekai One Turn Kill Neesan](https://subsplease.org/shows/isekai-one-turn-kill-neesan) | TV | 12 / 12 | **Finished Airing** | 6.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+One+Turn+Kill+Neesan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51219__isekai_one_turn_kill_neesan_ane_douhan_no_isekai_seikatsu_hajimemashita.txt) | 23 | 7983 | 2023-06-23 14:31 | | 41457 | [](https://myanimelist.net/anime/41457/86) | [86 - Eighty Six](https://subsplease.org/shows/86-eighty-six) | TV | 27 / 11 | **Finished Airing** | 8.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+86+Eighty+Six+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41457__86.txt) | 23 | 12549 | 2022-03-19 16:31 | | 58854 | [](https://myanimelist.net/anime/58854/Kinoko_Inu) | [Kinoko Inu](https://subsplease.org/shows/kinoko-inu) | TV | 12 / 12 | **Finished Airing** | 6.89 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kinoko+Inu+1080p+mkv) | 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[Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55358__bucchigiri.txt) | 22 | 5929 | 2024-04-06 15:31 | | 52973 | [](https://myanimelist.net/anime/52973/Megami_no_Café_Terrace) | [Megami no Cafe Terrace](https://subsplease.org/shows/megami-no-cafe-terrace) | TV | 24 / 12 | **Finished Airing** | 7.31 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Megami+no+Cafe+Terrace+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52973__megami_no_caf%C3%A9_terrace.txt) | 22 | 6878 | 2024-09-19 17:32 | | 52461 | [](https://myanimelist.net/anime/52461/Rougo_ni_Sonaete_Isekai_de_8-manmai_no_Kinka_wo_Tamemasu) | [Rougo ni Sonaete Isekai de 8-manmai no Kinka wo Tamemasu](https://subsplease.org/shows/rougo-ni-sonaete-isekai-de-8-manmai-no-kinka-wo-tamemasu) | TV | 12 / 12 | **Finished Airing** | 6.94 | 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[Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57031__vampire_dormitory.txt) | 22 | 2985 | 2024-06-23 14:47 | | 55844 | [](https://myanimelist.net/anime/55844/Tasogare_Out_Focus) | [Tasogare Out Focus](https://subsplease.org/shows/tasogare-out-focus) | TV | 12 / 12 | **Finished Airing** | 7.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tasogare+Out+Focus+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55844__tasogare_out_focus.txt) | 22 | 2178 | 2024-09-19 15:02 | | 51495 | [](https://myanimelist.net/anime/51495/Shin_Shinka_no_Mi__Shiranai_Uchi_ni_Kachigumi_Jinsei) | [Shinka no Mi S2](https://subsplease.org/shows/shinka-no-mi-s2) | TV | 12 / 12 | **Finished Airing** | 5.46 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shinka+no+Mi+S2+1080p+mkv) | 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[Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kimetsu+no+Yaiba+Mugen+Ressha+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49926__kimetsu_no_yaiba_mugen_ressha_hen.txt) | 22 | 13509 | 2021-11-28 15:48 | | 55570 | [](https://myanimelist.net/anime/55570/Shin_Tennis_no_Oujisama__U-17_World_Cup_Semifinal) | [The Prince of Tennis II - U-17 World Cup Semifinal](https://subsplease.org/shows/the-prince-of-tennis-ii-u-17-world-cup-semifinal) | TV | 13 / 13 | **Finished Airing** | 6.64 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+Prince+of+Tennis+II+U+17+World+Cup+Semifinal+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55570__shin_tennis_no_oujisama_u_17_world_cup_semifinal.txt) | 21 | 1917 | 2024-12-25 17:02 | | 52214 | [](https://myanimelist.net/anime/52214/Genjitsu_no_Yohane__Sunshine_in_the_Mirror) | [Genjitsu no Yohane - Sunshine in the Mirror](https://subsplease.org/shows/genjitsu-no-yohane-sunshine-in-the-mirror) | TV | 13 / 13 | **Finished Airing** | 6.87 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Genjitsu+no+Yohane+Sunshine+in+the+Mirror+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52214__genjitsu_no_yohane_sunshine_in_the_mirror.txt) | 21 | 5412 | 2023-09-17 14:46 | | 51711 | [](https://myanimelist.net/anime/51711/Hyouken_no_Majutsushi_ga_Sekai_wo_Suberu) | [Hyouken no Majutsushi ga Sekai wo Suberu](https://subsplease.org/shows/hyouken-no-majutsushi-ga-sekai-wo-suberu) | TV | 12 / 12 | **Finished Airing** | 6.35 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hyouken+no+Majutsushi+ga+Sekai+wo+Suberu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51711__hyouken_no_majutsushi_ga_sekai_wo_suberu.txt) | 21 | 7137 | 2023-03-23 18:01 | | 51096 | [](https://myanimelist.net/anime/51096/Youkoso_Jitsuryoku_Shijou_Shugi_no_Kyoushitsu_e_2nd_Season) | [Youkoso Jitsuryoku Shijou Shugi no Kyoushitsu e S2](https://subsplease.org/shows/youkoso-jitsuryoku-shijou-shugi-no-kyoushitsu-e-s2) | TV | 13 / 13 | **Finished Airing** | 8.09 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Youkoso+Jitsuryoku+Shijou+Shugi+no+Kyoushitsu+e+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51096__youkoso_jitsuryoku_shijou_shugi_no_kyoushitsu_e_2nd_season.txt) | 21 | 7979 | 2022-09-26 13:31 | | 49827 | [](https://myanimelist.net/anime/49827/Kidou_Senshi_Gundam__Cucuruz_Doan_no_Shima) | [Mobile Suit Gundam - Cucuruz Doan's Island](https://subsplease.org/shows/mobile-suit-gundam-cucuruz-doans-island) | Movie | 1 / 1 | **Finished Airing** | 7.35 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mobile+Suit+Gundam+Cucuruz+Doan+s+Island+1080p+mkv) | 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[Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50652__tsundere_akuyaku_reijou_liselotte_to_jikkyou_no_endou_kun_to_kaisetsu_no_kobayashi_san.txt) | 21 | 4470 | 2023-03-24 17:56 | | 51458 | [](https://myanimelist.net/anime/51458/Lv1_Maou_to_One_Room_Yuusha) | [Lv1 Maou to One Room Yuusha](https://subsplease.org/shows/lv1-maou-to-one-room-yuusha) | TV | 12 / 12 | **Finished Airing** | 7.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Lv1+Maou+to+One+Room+Yuusha+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51458__lv1_maou_to_one_room_yuusha.txt) | 20 | 9518 | 2023-09-18 13:31 | | 42745 | [](https://myanimelist.net/anime/42745/Machikado_Mazoku__2-choume) | [Machikado Mazoku S2](https://subsplease.org/shows/machikado-mazoku-s2) | TV | 12 / 12 | **Finished Airing** | 7.85 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Machikado+Mazoku+S2+1080p+mkv) | 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[Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kawaii+dake+ja+Nai+Shikimori+san+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/45613__kawaii_dake_ja_nai_shikimori_san.txt) | 19 | 6262 | 2022-07-09 18:16 | | 54898 | [](https://myanimelist.net/anime/54898/Bungou_Stray_Dogs_5th_Season) | [Bungou Stray Dogs](https://subsplease.org/shows/bungou-stray-dogs) | TV | 24 / 11 | **Finished Airing** | 8.63 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bungou+Stray+Dogs+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54898__bungou_stray_dogs_5th_season.txt) | 19 | 6604 | 2023-09-20 14:31 | | 49722 | [](https://myanimelist.net/anime/49722/Karakai_Jouzu_no_Takagi-san_Movie) | [Karakai Jouzu no Takagi-san Movie](https://subsplease.org/shows/karakai-jouzu-no-takagi-san-movie) | Movie | 1 / 1 | **Finished Airing** | 8.44 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Karakai+Jouzu+no+Takagi+san+Movie+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49722__karakai_jouzu_no_takagi_san_movie.txt) | 19 | 3268 | 2023-06-19 04:28 | | 41468 | [](https://myanimelist.net/anime/41468/Burn_the_Witch) | [Burn the Witch](https://subsplease.org/shows/burn-the-witch) | ONA | 4 / 3 | **Finished Airing** | 7.14 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Burn+the+Witch+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41468__burn_the_witch.txt) | 19 | 5265 | 2024-01-01 15:33 | | 39576 | [](https://myanimelist.net/anime/39576/Goblin_Slayer__Goblins_Crown) | [Goblin Slayer - Goblin's Crown](https://subsplease.org/shows/goblin-slayer-goblins-crown) | Movie | 1 / 1 | **Finished Airing** | 7.26 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Goblin+Slayer+Goblin+s+Crown+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39576__goblin_slayer_goblin_s_crown.txt) | 19 | 4495 | 2020-11-10 18:58 | | 57390 | [](https://myanimelist.net/anime/57390/Wonderful_Precure) | [Wonderful Precure!](https://subsplease.org/shows/wonderful-precure) | TV | 49 / 50 | Currently Airing | 7.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Wonderful+Precure+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57390__wonderful_precure.txt) | 18 | 1508 | 2025-01-19 01:32 | | 57192 | [](https://myanimelist.net/anime/57192/Yeosin_Gangnim) | [True Beauty](https://subsplease.org/shows/true-beauty) | ONA | 13 / 13 | **Finished Airing** | 6.59 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+True+Beauty+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57192__yeosin_gangnim.txt) | 18 | 2072 | 2024-10-30 16:32 | | 56768 | [](https://myanimelist.net/anime/56768/Tadaima_Okaeri) | [Tadaima, Okaeri](https://subsplease.org/shows/tadaima-okaeri) | TV | 12 / 12 | **Finished Airing** | 7.75 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tadaima+Okaeri+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56768__tadaima_okaeri.txt) | 18 | 2314 | 2024-06-24 16:02 | | 53411 | [](https://myanimelist.net/anime/53411/Buddy_Daddies) | [Buddy Daddies](https://subsplease.org/shows/buddy-daddies) | TV | 13 / 12 | **Finished Airing** | 8.11 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Buddy+Daddies+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53411__buddy_daddies.txt) | 18 | 6864 | 2023-03-31 16:31 | | 53163 | [](https://myanimelist.net/anime/53163/Kawaisugi_Crisis) | [Kawaisugi Crisis](https://subsplease.org/shows/kawaisugi-crisis) | TV | 12 / 12 | **Finished Airing** | 6.81 | 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[Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shinmai+Renkinjutsushi+no+Tenpo+Keiei+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49849__shinmai_renkinjutsushi_no_tenpo_keiei.txt) | 18 | 5930 | 2022-12-19 13:00 | | 47163 | [](https://myanimelist.net/anime/47163/Tensei_Kenja_no_Isekai_Life__Dai-2_no_Shokugyou_wo_Ete_Sekai_Saikyou_ni_Narimashita) | [Tensei Kenja no Isekai Life](https://subsplease.org/shows/tensei-kenja-no-isekai-life) | TV | 12 / 12 | **Finished Airing** | 6.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tensei+Kenja+no+Isekai+Life+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47163__tensei_kenja_no_isekai_life_dai_2_no_shokugyou_wo_ete_sekai_saikyou_ni_narimashita.txt) | 18 | 10359 | 2022-09-12 12:01 | | 55237 | [](https://myanimelist.net/anime/55237/Jashin-chan_Dropkick_Seikimatsu-hen) | [Jashin-chan Dropkick - Seikimatsu-hen](https://subsplease.org/shows/jashin-chan-dropkick-seikimatsu-hen) | TV Special | 1 / 1 | **Finished Airing** | 6.89 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jashin+chan+Dropkick+Seikimatsu+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55237__jashin_chan_dropkick_seikimatsu_hen.txt) | 17 | 3641 | 2023-12-28 03:58 | | 54275 | [](https://myanimelist.net/anime/54275/Temple) | [TenPuru](https://subsplease.org/shows/tenpuru) | TV | 13 / 12 | **Finished Airing** | 6.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+TenPuru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54275__temple.txt) | 17 | 6422 | 2023-11-22 09:56 | | 53621 | [](https://myanimelist.net/anime/53621/Jijou_wo_Shiranai_Tenkousei_ga_Guigui_Kuru) | [Jijou wo Shiranai Tenkousei ga Guigui Kuru](https://subsplease.org/shows/jijou-wo-shiranai-tenkousei-ga-guigui-kuru) | TV | 13 / 13 | **Finished Airing** | 7.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jijou+wo+Shiranai+Tenkousei+ga+Guigui+Kuru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53621__jijou_wo_shiranai_tenkousei_ga_guigui_kuru.txt) | 17 | 4258 | 2023-06-25 13:31 | | 50586 | [](https://myanimelist.net/anime/50586/Migi_to_Dali) | [Migi to Dali](https://subsplease.org/shows/migi-to-dali) | TV | 13 / 13 | **Finished Airing** | 7.72 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Migi+to+Dali+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50586__migi_to_dali.txt) | 17 | 4443 | 2023-12-25 14:31 | | 49784 | [](https://myanimelist.net/anime/49784/Mairimashita_Iruma-kun_3rd_Season) | [Mairimashita! Iruma-kun S3](https://subsplease.org/shows/mairimashita-iruma-kun-s3) | TV | 21 / 21 | **Finished Airing** | 7.82 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mairimashita+Iruma+kun+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49784__mairimashita_iruma_kun_3rd_season.txt) | 17 | 4940 | 2023-03-04 13:01 | | 47162 | [](https://myanimelist.net/anime/47162/Shokei_Shoujo_no_Virgin_Road) | [Shokei Shoujo no Virgin Road](https://subsplease.org/shows/shokei-shoujo-no-virgin-road) | TV | 12 / 12 | **Finished Airing** | 6.76 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shokei+Shoujo+no+Virgin+Road+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47162__shokei_shoujo_no_virgin_road.txt) | 17 | 8796 | 2022-06-17 16:31 | | 56165 | [](https://myanimelist.net/anime/56165/Boukyaku_Battery_TV) | [Boukyaku Battery](https://subsplease.org/shows/boukyaku-battery) | TV | 12 / 12 | **Finished Airing** | 7.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Boukyaku+Battery+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56165__boukyaku_battery_tv.txt) | 17 | 3864 | 2024-07-02 18:08 | | 52173 | [](https://myanimelist.net/anime/52173/Koori_Zokusei_Danshi_to_Cool_na_Douryou_Joshi) | [Koori Zokusei Danshi to Cool na Douryou Joshi](https://subsplease.org/shows/koori-zokusei-danshi-to-cool-na-douryou-joshi) | TV | 12 / 12 | **Finished Airing** | 7.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Koori+Zokusei+Danshi+to+Cool+na+Douryou+Joshi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52173__koori_zokusei_danshi_to_cool_na_douryou_joshi.txt) | 17 | 4552 | 2023-03-21 14:31 | | 54798 | [](https://myanimelist.net/anime/54798/Kamierabi) | [KamiErabi GOD.app](https://subsplease.org/shows/kamierabi-god-app) | TV | 24 / 12 | **Finished Airing** | 5.81 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+KamiErabi+GOD+app+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54798__kamierabi.txt) | 16 | 3224 | 2024-12-18 18:27 | | 52046 | [](https://myanimelist.net/anime/52046/Yuusha_Party_wo_Tsuihou_sareta_Beast_Tamer_Saikyoushu_no_Nekomimi_Shoujo_to_Deau) | [Beast Tamer](https://subsplease.org/shows/beast-tamer) | TV | 13 / 13 | **Finished Airing** | 6.92 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Beast+Tamer+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52046__yuusha_party_wo_tsuihou_sareta_beast_tamer_saikyoushu_no_nekomimi_shoujo_to_deau.txt) | 16 | 6639 | 2022-12-24 16:01 | | 48548 | [](https://myanimelist.net/anime/48548/5-toubun_no_Hanayome_Movie) | [Gotoubun no Hanayome Movie](https://subsplease.org/shows/gotoubun-no-hanayome-movie) | Movie | 1 / 1 | **Finished Airing** | 7.83 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gotoubun+no+Hanayome+Movie+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48548__5_toubun_no_hanayome_movie.txt) | 16 | 3080 | 2023-04-28 20:06 | | 42962 | [](https://myanimelist.net/anime/42962/Uzaki-chan_wa_Asobitai_Double) | [Uzaki-chan wa Asobitai! S2](https://subsplease.org/shows/uzaki-chan-wa-asobitai-s2) | TV | 13 / 13 | **Finished Airing** | 7.25 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Uzaki+chan+wa+Asobitai+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42962__uzaki_chan_wa_asobitai_double.txt) | 16 | 4933 | 2022-12-24 15:31 | | 40211 | [](https://myanimelist.net/anime/40211/Luo_Xiao_Hei_Zhan_Ji_Movie) | [The Legend of Hei](https://subsplease.org/shows/the-legend-of-hei) | Movie | 1 / 1 | **Finished Airing** | 8.3 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+Legend+of+Hei+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40211__luo_xiao_hei_zhan_ji_movie.txt) | 16 | 2332 | 2023-06-25 05:56 | | 39535 | [](https://myanimelist.net/anime/39535/Mushoku_Tensei__Isekai_Ittara_Honki_Dasu) | [Mushoku Tensei](https://subsplease.org/shows/mushoku-tensei) | TV | 24 / 11 | **Finished Airing** | 8.36 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mushoku+Tensei+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39535__mushoku_tensei_isekai_ittara_honki_dasu.txt) | 16 | 18654 | 2022-03-16 02:03 | | 38474 | [](https://myanimelist.net/anime/38474/Yuru_Camp△_Season_2) | [Yuru Camp S2](https://subsplease.org/shows/yuru-camp-s2) | TV | 13 / 13 | **Finished Airing** | 8.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yuru+Camp+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38474__yuru_camp_season_2.txt) | 16 | 5453 | 2021-04-01 15:03 | | 51064 | [](https://myanimelist.net/anime/51064/Kuro_no_Shoukanshi) | [Kuro no Shoukanshi](https://subsplease.org/shows/kuro-no-shoukanshi) | TV | 12 / 12 | **Finished Airing** | 7.04 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kuro+no+Shoukanshi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51064__kuro_no_shoukanshi.txt) | 16 | 6431 | 2022-09-24 14:31 | | 53179 | [](https://myanimelist.net/anime/53179/Ars_no_Kyojuu) | [Ars no Kyojuu](https://subsplease.org/shows/ars-no-kyojuu) | TV | 12 / 12 | **Finished Airing** | 6.04 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ars+no+Kyojuu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53179__ars_no_kyojuu.txt) | 15 | 5603 | 2023-03-24 17:01 | | 52092 | [](https://myanimelist.net/anime/52092/My_Home_Hero) | [My Home Hero](https://subsplease.org/shows/my-home-hero) | TV | 12 / 12 | **Finished Airing** | 7.08 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+My+Home+Hero+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52092__my_home_hero.txt) | 15 | 6687 | 2023-06-18 15:01 | | 50663 | [](https://myanimelist.net/anime/50663/Poputepipikku_2nd_Season) | [Pop Team Epic S2](https://subsplease.org/shows/pop-team-epic-s2) | TV | 12 / 11 | **Finished Airing** | 7.52 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Pop+Team+Epic+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50663__poputepipikku_2nd_season.txt) | 15 | 2972 | 2022-12-17 18:31 | | 50175 | [](https://myanimelist.net/anime/50175/Yuusha_Yamemasu) | [Yuusha, Yamemasu](https://subsplease.org/shows/yuusha-yamemasu) | TV | 14 / 12 | **Finished Airing** | 7.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yuusha+Yamemasu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50175__yuusha_yamemasu.txt) | 15 | 10738 | 2022-08-25 04:17 | | 49980 | [](https://myanimelist.net/anime/49980/Sugar_Apple_Fairy_Tale) | [Sugar Apple Fairy Tale](https://subsplease.org/shows/sugar-apple-fairy-tale) | TV | 24 / 12 | **Finished Airing** | 7.47 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sugar+Apple+Fairy+Tale+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49980__sugar_apple_fairy_tale.txt) | 15 | 4365 | 2023-09-22 13:01 | | 49776 | [](https://myanimelist.net/anime/49776/Kumichou_Musume_to_Sewagakari) | [Kumichou Musume to Sewagakari](https://subsplease.org/shows/kumichou-musume-to-sewagakari) | TV | 12 / 12 | **Finished Airing** | 7.75 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kumichou+Musume+to+Sewagakari+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49776__kumichou_musume_to_sewagakari.txt) | 15 | 4044 | 2022-09-22 15:31 | | 42385 | [](https://myanimelist.net/anime/42385/The_iDOLMSTER_Million_Live) | [The iDOLM@STER Million Live!](https://subsplease.org/shows/the-idolmster-million-live) | TV | 12 / 12 | **Finished Airing** | 6.92 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+iDOLM+STER+Million+Live+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42385__the_idolm_ster_million_live.txt) | 15 | 3051 | 2023-12-24 02:46 | | 40938 | [](https://myanimelist.net/anime/40938/Hige_wo_Soru_Soshite_Joshikousei_wo_Hirou) | [Hige wo Soru. Soshite Joshikousei wo Hirou.](https://subsplease.org/shows/hige-wo-soru-soshite-joshikousei-wo-hirou) | TV | 13 / 13 | **Finished Airing** | 7.27 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hige+wo+Soru+Soshite+Joshikousei+wo+Hirou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40938__hige_wo_soru_soshite_joshikousei_wo_hirou.txt) | 15 | 6514 | 2021-06-28 15:02 | | 40787 | [](https://myanimelist.net/anime/40787/Josee_to_Tora_to_Sakana-tachi) | [Josee to Tora to Sakana-tachi](https://subsplease.org/shows/josee-to-tora-to-sakana-tachi) | Movie | 1 / 1 | **Finished Airing** | 8.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Josee+to+Tora+to+Sakana+tachi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40787__josee_to_tora_to_sakana_tachi.txt) | 15 | 2929 | 2022-08-13 06:37 | | 39247 | [](https://myanimelist.net/anime/39247/Kobayashi-san_Chi_no_Maid_Dragon_S) | [Kobayashi-san Chi no Maid Dragon S2](https://subsplease.org/shows/kobayashi-san-chi-no-maid-dragon-s2) | TV | 13 / 12 | **Finished Airing** | 8.23 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kobayashi+san+Chi+no+Maid+Dragon+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39247__kobayashi_san_chi_no_maid_dragon_s.txt) | 15 | 9781 | 2022-04-27 16:13 | | 235 | [](https://myanimelist.net/anime/235/Meitantei_Conan) | [Detective Conan](https://subsplease.org/shows/detective-conan) | TV | 52 / ? | Currently Airing | 8.17 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Detective+Conan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/235__meitantei_conan.txt) | 15 | 2006 | 2025-01-18 12:31 | | 53428 | [](https://myanimelist.net/anime/53428/Ayaka) | [Ayaka](https://subsplease.org/shows/ayaka) | TV | 12 / 12 | **Finished Airing** | 6.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ayaka+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53428__ayaka.txt) | 15 | 4210 | 2023-09-16 17:31 | | 59499 | [](https://myanimelist.net/anime/59499/Asatir_2__Mirai_no_Mukashi_Banashi) | [Asatir 2 - Mirai no Mukashi Banashi](https://subsplease.org/shows/asatir-2-mirai-no-mukashi-banashi) | TV | 11 / 13 | Currently Airing | N/A | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Asatir+2+Mirai+no+Mukashi+Banashi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59499__asatir_2_mirai_no_mukashi_banashi.txt) | ~14~ | 941 | 2025-01-20 15:01 | | 54959 | [](https://myanimelist.net/anime/54959/BanG_Dream_Its_MyGO) | [BanG Dream! It's MyGO!!!!!](https://subsplease.org/shows/bang-dream-its-mygo) | TV | 13 / 13 | **Finished Airing** | 8.23 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+BanG+Dream+It+s+MyGO+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54959__bang_dream_it_s_mygo.txt) | ~14~ | 3054 | 2023-09-14 14:01 | | 51837 | [](https://myanimelist.net/anime/51837/Saikin_Yatotta_Maid_ga_Ayashii) | [Saikin Yatotta Maid ga Ayashii](https://subsplease.org/shows/saikin-yatotta-maid-ga-ayashii) | TV | 11 / 11 | **Finished Airing** | 6.57 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Saikin+Yatotta+Maid+ga+Ayashii+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51837__saikin_yatotta_maid_ga_ayashii.txt) | ~14~ | 3500 | 2022-10-08 18:46 | | 50606 | [](https://myanimelist.net/anime/50606/Ayakashi_Triangle) | [Ayakashi Triangle](https://subsplease.org/shows/ayakashi-triangle) | TV | 13 / 12 | **Finished Airing** | 6.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ayakashi+Triangle+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50606__ayakashi_triangle.txt) | ~14~ | 4154 | 2023-09-25 17:37 | | 50590 | [](https://myanimelist.net/anime/50590/Koukyuu_no_Karasu) | [Koukyuu no Karasu](https://subsplease.org/shows/koukyuu-no-karasu) | TV | 13 / 13 | **Finished Airing** | 7.54 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Koukyuu+no+Karasu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50590__koukyuu_no_karasu.txt) | ~14~ | 2894 | 2022-12-24 17:01 | | 48842 | [](https://myanimelist.net/anime/48842/Mahoutsukai_Reimeiki) | [Mahoutsukai Reimeiki](https://subsplease.org/shows/mahoutsukai-reimeiki) | TV | 12 / 12 | **Finished Airing** | 6.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahoutsukai+Reimeiki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48842__mahoutsukai_reimeiki.txt) | ~14~ | 5163 | 2022-06-30 18:46 | | 48760 | [](https://myanimelist.net/anime/48760/Gaikotsu_Kishi-sama_Tadaima_Isekai_e_Odekakechuu) | [Gaikotsu Kishi-sama, Tadaima Isekai e Odekakechuu](https://subsplease.org/shows/gaikotsu-kishi-sama-tadaima-isekai-e-odekakechuu) | TV | 12 / 12 | **Finished Airing** | 7.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gaikotsu+Kishi+sama+Tadaima+Isekai+e+Odekakechuu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48760__gaikotsu_kishi_sama_tadaima_isekai_e_odekakechuu.txt) | ~14~ | 8359 | 2022-06-23 14:31 | | 48624 | [](https://myanimelist.net/anime/48624/Re_cycle_of_the_Penguindrum) | [Re-cycle of the Penguindrum](https://subsplease.org/shows/re-cycle-of-the-penguindrum) | Movie | 2 / 2 | **Finished Airing** | 7.19 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Re+cycle+of+the+Penguindrum+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48624__re_cycle_of_the_penguindrum.txt) | ~14~ | 2294 | 2023-04-16 22:13 | | 48415 | [](https://myanimelist.net/anime/48415/Shijou_Saikyou_no_Daimaou_Murabito_A_ni_Tensei_suru) | [Shijou Saikyou no Daimaou, Murabito A ni Tensei suru](https://subsplease.org/shows/shijou-saikyou-no-daimaou-murabito-a-ni-tensei-suru) | TV | 12 / 12 | **Finished Airing** | 6.29 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shijou+Saikyou+no+Daimaou+Murabito+A+ni+Tensei+suru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48415__shijou_saikyou_no_daimaou_murabito_a_ni_tensei_suru.txt) | ~14~ | 6663 | 2022-06-22 12:01 | | 47159 | [](https://myanimelist.net/anime/47159/Tensai_Ouji_no_Akaji_Kokka_Saisei_Jutsu) | [Tensai Ouji no Akaji Kokka Saisei Jutsu](https://subsplease.org/shows/tensai-ouji-no-akaji-kokka-saisei-jutsu) | TV | 12 / 12 | **Finished Airing** | 7.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tensai+Ouji+no+Akaji+Kokka+Saisei+Jutsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47159__tensai_ouji_no_akaji_kokka_saisei_jutsu.txt) | ~14~ | 7534 | 2022-03-29 14:33 | | 43760 | [](https://myanimelist.net/anime/43760/Hikari_no_Ou) | [Hikari no Ou](https://subsplease.org/shows/hikari-no-ou) | TV | 20 / 10 | **Finished Airing** | 6.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hikari+no+Ou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43760__hikari_no_ou.txt) | ~14~ | 3845 | 2024-03-17 15:31 | | 42361 | [](https://myanimelist.net/anime/42361/Ijiranaide_Nagatoro-san) | [Ijiranaide, Nagatoro-san](https://subsplease.org/shows/ijiranaide-nagatoro-san) | TV | 12 / 12 | **Finished Airing** | 7.22 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ijiranaide+Nagatoro+san+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42361__ijiranaide_nagatoro_san.txt) | ~14~ | 8040 | 2021-06-26 16:02 | | 50399 | [](https://myanimelist.net/anime/50399/Tian_Guan_Cifu_Er) | [Heaven Official's Blessing S2](https://subsplease.org/shows/heaven-officials-blessing-s2) | ONA | 12 / 12 | **Finished Airing** | 8.66 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Heaven+Official+s+Blessing+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50399__tian_guan_cifu_er.txt) | ~14~ | 2988 | 2024-01-17 13:01 | | 49438 | [](https://myanimelist.net/anime/49438/Isekai_Yakkyoku) | [Isekai Yakkyoku](https://subsplease.org/shows/isekai-yakkyoku) | TV | 12 / 12 | **Finished Airing** | 7.28 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+Yakkyoku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49438__isekai_yakkyoku.txt) | ~14~ | 6635 | 2022-09-25 13:33 | | 48483 | [](https://myanimelist.net/anime/48483/Mieruko-chan) | [Mieruko-chan](https://subsplease.org/shows/mieruko-chan) | TV | 12 / 12 | **Finished Airing** | 7.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mieruko+chan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48483__mieruko_chan.txt) | ~14~ | 8981 | 2021-12-19 14:02 | | 42429 | [](https://myanimelist.net/anime/42429/Honzuki_no_Gekokujou__Shisho_ni_Naru_Tame_ni_wa_Shudan_wo_Erandeiraremasen_3rd_Season) | [Honzuki no Gekokujou](https://subsplease.org/shows/honzuki-no-gekokujou) | TV | 12 / 10 | **Finished Airing** | 8.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Honzuki+no+Gekokujou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42429__honzuki_no_gekokujou_shisho_ni_naru_tame_ni_wa_shudan_wo_erandeiraremasen_3rd_season.txt) | ~14~ | 4255 | 2022-06-13 18:32 | | 55998 | [](https://myanimelist.net/anime/55998/Momochi-san_Chi_no_Ayakashi_Ouji) | [Momochi-san Chi no Ayakashi Ouji](https://subsplease.org/shows/momochi-san-chi-no-ayakashi-ouji) | TV | 12 / 12 | **Finished Airing** | 6.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Momochi+san+Chi+no+Ayakashi+Ouji+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55998__momochi_san_chi_no_ayakashi_ouji.txt) | ~13~ | 3564 | 2024-03-22 16:31 | | 55973 | [](https://myanimelist.net/anime/55973/30-sai_made_Doutei_dato_Mahoutsukai_ni_Nareru_Rashii) | [30-sai made Doutei dato Mahoutsukai ni Nareru Rashii](https://subsplease.org/shows/30-sai-made-doutei-dato-mahoutsukai-ni-nareru-rashii) | TV | 12 / 12 | **Finished Airing** | 7.64 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+30+sai+made+Doutei+dato+Mahoutsukai+ni+Nareru+Rashii+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55973__30_sai_made_doutei_dato_mahoutsukai_ni_nareru_rashii.txt) | ~13~ | 3404 | 2024-03-27 16:31 | | 51586 | [](https://myanimelist.net/anime/51586/D4DJ_All_Mix) | [D4DJ Double Mix](https://subsplease.org/shows/d4dj-all-mix) | TV | 1 / 12 | **Finished Airing** | 7.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+D4DJ+Double+Mix+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51586__d4dj_all_mix.txt) | ~13~ | 1401 | 2023-03-26 16:31 | | 51536 | [](https://myanimelist.net/anime/51536/The_iDOLMSTER_Cinderella_Girls__U149) | [The IDOLM@STER Cinderella Girls - U149](https://subsplease.org/shows/the-idolmster-cinderella-girls-u149) | TV | 12 / 12 | **Finished Airing** | 7.69 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+IDOLM+STER+Cinderella+Girls+U149+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51536__the_idolm_ster_cinderella_girls_u149.txt) | ~13~ | 3364 | 2023-06-29 10:02 | | 51265 | [](https://myanimelist.net/anime/51265/Inu_ni_Nattara_Suki_na_Hito_ni_Hirowareta) | [Inu ni Nattara Suki na Hito ni Hirowareta](https://subsplease.org/shows/inu-ni-nattara-suki-na-hito-ni-hirowareta) | TV | 14 / 12 | **Finished Airing** | 5.05 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Inu+ni+Nattara+Suki+na+Hito+ni+Hirowareta+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51265__inu_ni_nattara_suki_na_hito_ni_hirowareta.txt) | ~13~ | 3964 | 2023-04-26 18:50 | | 49757 | [](https://myanimelist.net/anime/49757/Ji_Yao_Lu__Qicheng_Pian) | [Another Journey to the West](https://subsplease.org/shows/another-journey-to-the-west) | ONA | 12 / 12 | **Finished Airing** | 6.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Another+Journey+to+the+West+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49757__ji_yao_lu_qicheng_pian.txt) | ~13~ | 1826 | 2024-11-15 14:33 | | 49053 | [](https://myanimelist.net/anime/49053/Given__Uragawa_no_Sonzai) | [Given](https://subsplease.org/shows/given) | OVA | 1 / 1 | **Finished Airing** | 8.13 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Given+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49053__given_uragawa_no_sonzai.txt) | ~13~ | 1771 | 2022-06-15 05:15 | | 48375 | [](https://myanimelist.net/anime/48375/Mahouka_Koukou_no_Rettousei__Tsuioku-hen) | [Mahouka Koukou no Rettousei - Tsuioku-hen](https://subsplease.org/shows/mahouka-koukou-no-rettousei-tsuioku-hen) | TV Special | 1 / 1 | **Finished Airing** | 7.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahouka+Koukou+no+Rettousei+Tsuioku+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48375__mahouka_koukou_no_rettousei_tsuioku_hen.txt) | ~13~ | 4391 | 2021-12-31 18:33 | | 46095 | [](https://myanimelist.net/anime/46095/Vivy__Fluorite_Eyes_Song) | [Vivy - Fluorite Eye's Song](https://subsplease.org/shows/vivy-fluorite-eyes-song) | TV | 14 / 13 | **Finished Airing** | 8.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Vivy+Fluorite+Eye+s+Song+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/46095__vivy_fluorite_eye_s_song.txt) | ~13~ | 10840 | 2021-06-26 17:24 | | 41461 | [](https://myanimelist.net/anime/41461/Date_A_Live_IV) | [Date a Live IV](https://subsplease.org/shows/date-a-live-iv) | TV | 12 / 12 | **Finished Airing** | 7.75 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Date+a+Live+IV+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41461__date_a_live_iv.txt) | ~13~ | 3534 | 2022-06-24 13:33 | | 55310 | [](https://myanimelist.net/anime/55310/Atarashii_Joushi_wa_Do_Tennen) | [Atarashii Joushi wa Do Tennen](https://subsplease.org/shows/atarashii-joushi-wa-do-tennen) | TV | 12 / 12 | **Finished Airing** | 7.93 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Atarashii+Joushi+wa+Do+Tennen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55310__atarashii_joushi_wa_do_tennen.txt) | ~13~ | 3295 | 2023-12-23 17:05 | | 51128 | [](https://myanimelist.net/anime/51128/Noumin_Kanren_no_Skill_bakka_Agetetara_Nazeka_Tsuyoku_Natta) | [Noumin Kanren no Skill bakka Agetetara Nazeka Tsuyoku Natta](https://subsplease.org/shows/noumin-kanren-no-skill-bakka-agetetara-nazeka-tsuyoku-natta) | TV | 12 / 12 | **Finished Airing** | 5.96 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Noumin+Kanren+no+Skill+bakka+Agetetara+Nazeka+Tsuyoku+Natta+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51128__noumin_kanren_no_skill_bakka_agetetara_nazeka_tsuyoku_natta.txt) | ~13~ | 6910 | 2022-12-17 13:30 | | 49782 | [](https://myanimelist.net/anime/49782/Shadows_House_2nd_Season) | [Shadows House S2](https://subsplease.org/shows/shadows-house-s2) | TV | 12 / 12 | **Finished Airing** | 8.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shadows+House+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49782__shadows_house_2nd_season.txt) | ~13~ | 3741 | 2022-09-23 16:33 | | 48438 | [](https://myanimelist.net/anime/48438/Mahoutsukai_no_Yome__Nishi_no_Shounen_to_Seiran_no_Kishi) | [Mahoutsukai no Yome - Nishi no Shounen to Seiran no Kishi](https://subsplease.org/shows/mahoutsukai-no-yome-nishi-no-shounen-to-seiran-no-kishi) | OVA | 3 / 3 | **Finished Airing** | 7.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahoutsukai+no+Yome+Nishi+no+Shounen+to+Seiran+no+Kishi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48438__mahoutsukai_no_yome_nishi_no_shounen_to_seiran_no_kishi.txt) | ~12~ | 4210 | 2022-12-14 07:38 | | 57502 | [](https://myanimelist.net/anime/57502/Meiji_Gekken__1874) | [Meiji Gekken 1874](https://subsplease.org/shows/meiji-gekken-1874) | TV | 10 / 10 | **Finished Airing** | 6.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Meiji+Gekken+1874+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57502__meiji_gekken_1874.txt) | ~12~ | 3470 | 2024-03-17 15:02 | | 53848 | [](https://myanimelist.net/anime/53848/Megumi_no_Daigo__Kyuukoku_no_Orange) | [Megumi no Daigo - Kyuukoku no Orange](https://subsplease.org/shows/megumi-no-daigo-kyuukoku-no-orange) | TV | 25 / 23 | **Finished Airing** | 6.69 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Megumi+no+Daigo+Kyuukoku+no+Orange+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53848__megumi_no_daigo_kyuukoku_no_orange.txt) | ~12~ | 3181 | 2024-03-23 09:01 | | 53223 | [](https://myanimelist.net/anime/53223/Kingdom_5th_Season) | [Kingdom S5](https://subsplease.org/shows/kingdom-s5) | TV | 13 / 13 | **Finished Airing** | 8.72 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kingdom+S5+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53223__kingdom_5th_season.txt) | ~12~ | 6111 | 2024-03-31 00:07 | | 50461 | [](https://myanimelist.net/anime/50461/Otome_Game_Sekai_wa_Mob_ni_Kibishii_Sekai_desu) | [Otome Game Sekai wa Mob ni Kibishii Sekai desu](https://subsplease.org/shows/otome-game-sekai-wa-mob-ni-kibishii-sekai-desu) | TV | 12 / 12 | **Finished Airing** | 7.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Otome+Game+Sekai+wa+Mob+ni+Kibishii+Sekai+desu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50461__otome_game_sekai_wa_mob_ni_kibishii_sekai_desu.txt) | ~12~ | 6134 | 2022-06-19 13:33 | | 50384 | [](https://myanimelist.net/anime/50384/Mononogatari) | [Mononogatari](https://subsplease.org/shows/mononogatari) | TV | 24 / 12 | **Finished Airing** | 7.25 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mononogatari+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50384__mononogatari.txt) | ~12~ | 3918 | 2023-09-18 17:01 | | 49236 | [](https://myanimelist.net/anime/49236/Youjo_Senki__Sabaku_no_Pasta_Daisakusen) | [Youjo Senki](https://subsplease.org/shows/youjo-senki) | ONA | 1 / 1 | **Finished Airing** | 7.46 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Youjo+Senki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49236__youjo_senki_sabaku_no_pasta_daisakusen.txt) | ~12~ | 4028 | 2021-06-19 17:53 | | 49236 | [](https://myanimelist.net/anime/49236/Youjo_Senki__Sabaku_no_Pasta_Daisakusen) | [Youjo Senki - Sabaku no Pasta Dai Sakusen](https://subsplease.org/shows/youjo-senki) | ONA | 1 / 1 | **Finished Airing** | 7.46 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Youjo+Senki+Sabaku+no+Pasta+Dai+Sakusen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49236__youjo_senki_sabaku_no_pasta_daisakusen.txt) | ~12~ | 4028 | 2021-06-19 17:53 | | 46102 | [](https://myanimelist.net/anime/46102/Odd_Taxi) | [Odd Taxi](https://subsplease.org/shows/odd-taxi) | TV | 14 / 13 | **Finished Airing** | 8.65 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Odd+Taxi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/46102__odd_taxi.txt) | ~12~ | 3980 | 2022-09-10 08:46 | | 42282 | [](https://myanimelist.net/anime/42282/Otome_Game_no_Hametsu_Flag_shika_Nai_Akuyaku_Reijou_ni_Tensei_shiteshimatta_X) | [Hamefura S2](https://subsplease.org/shows/hamefura-s2) | TV | 13 / 12 | **Finished Airing** | 7.26 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hamefura+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42282__otome_game_no_hametsu_flag_shika_nai_akuyaku_reijou_ni_tensei_shiteshimatta_x.txt) | ~12~ | 4126 | 2022-01-01 01:24 | | 53787 | [](https://myanimelist.net/anime/53787/AI_no_Idenshi) | [AI no Idenshi](https://subsplease.org/shows/ai-no-idenshi) | TV | 12 / 12 | **Finished Airing** | 6.53 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+AI+no+Idenshi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53787__ai_no_idenshi.txt) | ~12~ | 5330 | 2023-09-29 19:01 | | 51859 | [](https://myanimelist.net/anime/51859/Touken_Ranbu_Kai__Kyoden_Moyuru_Honnouji) | [Touken Ranbu Kai - Kyoden Moyuru Honnouji](https://subsplease.org/shows/touken-ranbu-kai-kyoden-moyuru-honnouji) | TV | 8 / 8 | **Finished Airing** | 6.11 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Touken+Ranbu+Kai+Kyoden+Moyuru+Honnouji+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51859__touken_ranbu_kai_kyoden_moyuru_honnouji.txt) | ~12~ | 2264 | 2024-05-21 15:31 | | 40586 | [](https://myanimelist.net/anime/40586/Slime_Taoshite_300-nen_Shiranai_Uchi_ni_Level_Max_ni_Nattemashita) | [Slime Taoshite 300-nen, Shiranai Uchi ni Level Max ni Nattemashita](https://subsplease.org/shows/slime-taoshite-300-nen-shiranai-uchi-ni-level-max-ni-nattemashita) | TV | 12 / 12 | **Finished Airing** | 6.91 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Slime+Taoshite+300+nen+Shiranai+Uchi+ni+Level+Max+ni+Nattemashita+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40586__slime_taoshite_300_nen_shiranai_uchi_ni_level_max_ni_nattemashita.txt) | ~12~ | 6353 | 2021-06-26 13:31 | | 40834 | [](https://myanimelist.net/anime/40834/Ousama_Ranking) | [Ousama Ranking](https://subsplease.org/shows/ousama-ranking) | TV | 23 / 23 | **Finished Airing** | 8.5 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ousama+Ranking+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40834__ousama_ranking.txt) | ~12~ | 11832 | 2022-03-24 17:48 | | 33970 | [](https://myanimelist.net/anime/33970/Girls___Panzer__Saishuushou_Part_1) | [Girls und Panzer das Finale](https://subsplease.org/shows/girls-und-panzer-das-finale) | Movie | 3 / 1 | **Finished Airing** | 7.95 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Girls+und+Panzer+das+Finale+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/33970__girls_panzer_saishuushou_part_1.txt) | ~12~ | 3568 | 2023-01-04 05:31 | | 59010 | [](https://myanimelist.net/anime/59010/Yami_Shibai_13) | [Yami Shibai 13](https://subsplease.org/shows/yami-shibai-13) | TV | 13 / 13 | **Finished Airing** | 6.04 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yami+Shibai+13+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/59010__yami_shibai_13.txt) | ~11~ | 1307 | 2024-10-06 19:46 | | 51417 | [](https://myanimelist.net/anime/51417/Engage_Kiss) | [Engage Kiss](https://subsplease.org/shows/engage-kiss) | TV | 13 / 13 | **Finished Airing** | 6.79 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Engage+Kiss+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51417__engage_kiss.txt) | ~11~ | 5847 | 2022-09-24 17:01 | | 51381 | [](https://myanimelist.net/anime/51381/RWBY__Hyousetsu_Teikoku) | [RWBY - Hyousetsu Teikoku](https://subsplease.org/shows/rwby-hyousetsu-teikoku) | TV | 13 / 12 | **Finished Airing** | 6.34 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+RWBY+Hyousetsu+Teikoku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51381__rwby_hyousetsu_teikoku.txt) | ~11~ | 3405 | 2022-09-18 15:01 | | 50425 | [](https://myanimelist.net/anime/50425/Fuufu_Ijou_Koibito_Miman) | [Fuufu Ijou, Koibito Miman](https://subsplease.org/shows/fuufu-ijou-koibito-miman) | TV | 12 / 12 | **Finished Airing** | 7.58 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fuufu+Ijou+Koibito+Miman+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50425__fuufu_ijou_koibito_miman.txt) | ~11~ | 4699 | 2022-12-25 15:01 | | 49342 | [](https://myanimelist.net/anime/49342/Shin_Ikkitousen) | [Shin Ikkitousen](https://subsplease.org/shows/shin-ikkitousen) | TV | 3 / 3 | **Finished Airing** | 5.83 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shin+Ikkitousen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49342__shin_ikkitousen.txt) | ~11~ | 2984 | 2022-05-31 13:01 | | 48491 | [](https://myanimelist.net/anime/48491/Yama_no_Susume__Next_Summit) | [Yama no Susume - Next Summit](https://subsplease.org/shows/yama-no-susume-next-summit) | TV | 12 / 12 | **Finished Airing** | 7.63 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yama+no+Susume+Next+Summit+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48491__yama_no_susume_next_summit.txt) | ~11~ | 2571 | 2022-12-20 16:00 | | 47790 | [](https://myanimelist.net/anime/47790/Sekai_Saikou_no_Ansatsusha_Isekai_Kizoku_ni_Tensei_suru) | [Sekai Saikou no Ansatsusha, Isekai Kizoku ni Tensei suru](https://subsplease.org/shows/sekai-saikou-no-ansatsusha-isekai-kizoku-ni-tensei-suru) | TV | 12 / 12 | **Finished Airing** | 7.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sekai+Saikou+no+Ansatsusha+Isekai+Kizoku+ni+Tensei+suru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47790__sekai_saikou_no_ansatsusha_isekai_kizoku_ni_tensei_suru.txt) | ~11~ | 11171 | 2021-12-22 15:31 | | 42351 | [](https://myanimelist.net/anime/42351/Senpai_ga_Uzai_Kouhai_no_Hanashi) | [Senpai ga Uzai Kouhai no Hanashi](https://subsplease.org/shows/senpai-ga-uzai-kouhai-no-hanashi) | TV | 12 / 12 | **Finished Airing** | 7.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Senpai+ga+Uzai+Kouhai+no+Hanashi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42351__senpai_ga_uzai_kouhai_no_hanashi.txt) | ~11~ | 6713 | 2021-12-25 17:02 | | 55894 | [](https://myanimelist.net/anime/55894/Bokura_no_Ame-iro_Protocol) | [Bokura no Ameiro Protocol](https://subsplease.org/shows/bokura-no-ameiro-protocol) | TV | 12 / 12 | **Finished Airing** | 6.1 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bokura+no+Ameiro+Protocol+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55894__bokura_no_ame_iro_protocol.txt) | ~11~ | 4061 | 2023-12-23 19:05 | | 50864 | [](https://myanimelist.net/anime/50864/Ooyukiumi_no_Kaina) | [Ooyukiumi no Kaina](https://subsplease.org/shows/ooyukiumi-no-kaina) | TV | 12 / 11 | **Finished Airing** | 6.68 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ooyukiumi+no+Kaina+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50864__ooyukiumi_no_kaina.txt) | ~11~ | 4906 | 2023-12-28 04:13 | | 41488 | [](https://myanimelist.net/anime/41488/Tensura_Nikki__Tensei_shitara_Slime_Datta_Ken) | [Tensura Nikki - Tensei Shitara Slime Datta Ken](https://subsplease.org/shows/tensura-nikki-tensei-shitara-slime-datta-ken) | TV | 12 / 12 | **Finished Airing** | 7.59 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tensura+Nikki+Tensei+Shitara+Slime+Datta+Ken+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41488__tensura_nikki_tensei_shitara_slime_datta_ken.txt) | ~11~ | 4657 | 2021-06-22 15:01 | | 41456 | [](https://myanimelist.net/anime/41456/Sentouin_Hakenshimasu) | [Sentouin, Hakenshimasu!](https://subsplease.org/shows/sentouin-hakenshimasu) | TV | 12 / 12 | **Finished Airing** | 7.14 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sentouin+Hakenshimasu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41456__sentouin_hakenshimasu.txt) | ~11~ | 6342 | 2021-06-20 12:02 | | 30455 | [](https://myanimelist.net/anime/30455/KanColle__Itsuka_Ano_Umi_de) | [KanColle S2](https://subsplease.org/shows/kancolle-s2) | TV | 8 / 8 | **Finished Airing** | 6.26 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+KanColle+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/30455__kancolle_itsuka_ano_umi_de.txt) | ~11~ | 2236 | 2023-03-25 16:31 | | 53300 | [](https://myanimelist.net/anime/53300/Ojou_to_Banken-kun) | [Ojou to Banken-kun](https://subsplease.org/shows/ojou-to-banken-kun) | TV | 13 / 13 | **Finished Airing** | 5.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ojou+to+Banken+kun+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53300__ojou_to_banken_kun.txt) | ~10~ | 3790 | 2023-12-21 17:21 | | 51680 | [](https://myanimelist.net/anime/51680/Cool_Doji_Danshi) | [Cool Doji Danshi](https://subsplease.org/shows/cool-doji-danshi) | TV | 24 / 24 | **Finished Airing** | 7.86 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cool+Doji+Danshi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51680__cool_doji_danshi.txt) | ~10~ | 2182 | 2023-03-27 18:00 | | 51464 | [](https://myanimelist.net/anime/51464/4-nin_wa_Sorezore_Uso_wo_Tsuku) | [4-nin wa Sorezore Uso wo Tsuku](https://subsplease.org/shows/4-nin-wa-sorezore-uso-wo-tsuku) | TV | 11 / 11 | **Finished Airing** | 7.14 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+4+nin+wa+Sorezore+Uso+wo+Tsuku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51464__4_nin_wa_sorezore_uso_wo_tsuku.txt) | ~10~ | 2022 | 2022-12-24 19:46 | | 51440 | [](https://myanimelist.net/anime/51440/Sasaki_to_Miyano_Movie__Sotsugyou-hen) | [Sasaki to Miyano - Sotsugyou-hen](https://subsplease.org/shows/sasaki-to-miyano-sotsugyou-hen) | Movie | 1 / 1 | **Finished Airing** | 8.3 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sasaki+to+Miyano+Sotsugyou+hen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51440__sasaki_to_miyano_movie_sotsugyou_hen.txt) | ~10~ | 2451 | 2023-09-30 06:04 | | 51098 | [](https://myanimelist.net/anime/51098/Shinobi_no_Ittoki) | [Shinobi no Ittoki](https://subsplease.org/shows/shinobi-no-ittoki) | TV | 12 / 12 | **Finished Airing** | 6.06 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shinobi+no+Ittoki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51098__shinobi_no_ittoki.txt) | ~10~ | 4328 | 2022-12-20 14:01 | | 50248 | [](https://myanimelist.net/anime/50248/Birdie_Wing__Golf_Girls_Story) | [Birdie Wing - Golf Girls' Story](https://subsplease.org/shows/birdie-wing-golf-girls-story) | TV | 25 / 13 | **Finished Airing** | 7.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Birdie+Wing+Golf+Girls+Story+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50248__birdie_wing_golf_girls_story.txt) | ~10~ | 3756 | 2023-06-23 18:31 | | 48753 | [](https://myanimelist.net/anime/48753/Jahy-sama_wa_Kujikenai) | [Jahy-sama wa Kujikenai!](https://subsplease.org/shows/jahy-sama-wa-kujikenai) | TV | 20 / 20 | **Finished Airing** | 7.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jahy+sama+wa+Kujikenai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48753__jahy_sama_wa_kujikenai.txt) | ~10~ | 5637 | 2021-12-18 19:46 | | 48675 | [](https://myanimelist.net/anime/48675/Kakkou_no_Iinazuke) | [Kakkou no Iinazuke](https://subsplease.org/shows/kakkou-no-iinazuke) | TV | 24 / 24 | **Finished Airing** | 6.87 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kakkou+no+Iinazuke+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48675__kakkou_no_iinazuke.txt) | ~10~ | 4218 | 2022-10-02 04:02 | | 48239 | [](https://myanimelist.net/anime/48239/Leadale_no_Daichi_nite) | [Leadale no Daichi nite](https://subsplease.org/shows/leadale-no-daichi-nite) | TV | 12 / 12 | **Finished Airing** | 6.95 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Leadale+no+Daichi+nite+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48239__leadale_no_daichi_nite.txt) | ~10~ | 6106 | 2022-03-23 14:17 | | 45653 | [](https://myanimelist.net/anime/45653/Soredemo_Ayumu_wa_Yosetekuru) | [Soredemo Ayumu wa Yosetekuru](https://subsplease.org/shows/soredemo-ayumu-wa-yosetekuru) | TV | 12 / 12 | **Finished Airing** | 7.02 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Soredemo+Ayumu+wa+Yosetekuru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/45653__soredemo_ayumu_wa_yosetekuru.txt) | ~10~ | 4911 | 2022-09-23 16:01 | | 43299 | [](https://myanimelist.net/anime/43299/Wonder_Egg_Priority) | [Wonder Egg Priority](https://subsplease.org/shows/wonder-egg-priority) | TV | 13 / 12 | **Finished Airing** | 7.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Wonder+Egg+Priority+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43299__wonder_egg_priority.txt) | ~10~ | 8876 | 2021-06-30 03:56 | | 42994 | [](https://myanimelist.net/anime/42994/Jashin-chan_Dropkick_X) | [Jashin-chan Dropkick X](https://subsplease.org/shows/jashin-chan-dropkick-x) | TV | 12 / 12 | **Finished Airing** | 7.42 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jashin+chan+Dropkick+X+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42994__jashin_chan_dropkick_x.txt) | ~10~ | 2520 | 2022-09-20 18:04 | | 42587 | [](https://myanimelist.net/anime/42587/Yuuki_Yuuna_wa_Yuusha_de_Aru__Dai_Mankai_no_Shou) | [Yuuki Yuuna wa Yuusha de Aru - Dai Mankai no Shou](https://subsplease.org/shows/yuuki-yuuna-wa-yuusha-de-aru-dai-mankai-no-shou) | TV | 12 / 12 | **Finished Airing** | 7.3 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yuuki+Yuuna+wa+Yuusha+de+Aru+Dai+Mankai+no+Shou+1080p+mkv) | 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[Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40852__dr_stone_stone_wars.txt) | ~10~ | 9675 | 2021-03-25 14:32 | | 37744 | [](https://myanimelist.net/anime/37744/Isekai_Cheat_Magician) | [Isekai Cheat Magician](https://subsplease.org/shows/isekai-cheat-magician) | TV | 1 / 12 | **Finished Airing** | 5.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+Cheat+Magician+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/37744__isekai_cheat_magician.txt) | ~10~ | 2009 | 2021-07-08 01:02 | | 33737 | [](https://myanimelist.net/anime/33737/Megaton-kyuu_Musashi) | [Megaton-kyuu Musashi](https://subsplease.org/shows/megaton-kyuu-musashi) | TV | 13 / 13 | **Finished Airing** | 6.29 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Megaton+kyuu+Musashi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/33737__megaton_kyuu_musashi.txt) | ~10~ | 2172 | 2024-07-18 15:34 | | 235 | [](https://myanimelist.net/anime/235/Meitantei_Conan) | [Detective Conan - Kid vs Komei - The Targeted Lips](https://subsplease.org/shows/detective-conan) | TV | 1 / ? | Currently Airing | 8.17 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Detective+Conan+Kid+vs+Komei+The+Targeted+Lips+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/235__meitantei_conan.txt) | ~10~ | 1690 | 2025-01-18 12:31 | | 54259 | [](https://myanimelist.net/anime/54259/Rokudou_no_Onna-tachi) | [Rokudou no Onna-tachi](https://subsplease.org/shows/rokudou-no-onna-tachi) | TV | 12 / 12 | **Finished Airing** | 6.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Rokudou+no+Onna+tachi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54259__rokudou_no_onna_tachi.txt) | ~10~ | 4672 | 2023-06-23 17:46 | | 48556 | [](https://myanimelist.net/anime/48556/Takt_Op_Destiny) | [Takt Op. Destiny](https://subsplease.org/shows/takt-op-destiny) | TV | 12 / 12 | **Finished Airing** | 7.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Takt+Op+Destiny+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48556__takt_op_destiny.txt) | ~10~ | 7367 | 2021-12-21 17:01 | | 40594 | [](https://myanimelist.net/anime/40594/Tatoeba_Last_Dungeon_Mae_no_Mura_no_Shounen_ga_Joban_no_Machi_de_Kurasu_Youna_Monogatari) | [Tatoeba Last Dungeon Mae no Mura no Shounen ga Joban no Machi de Kurasu Youna Monogatari](https://subsplease.org/shows/tatoeba-last-dungeon-mae-no-mura-no-shounen-ga-joban-no-machi-de-kurasu-youna-monogatari) | TV | 12 / 12 | **Finished Airing** | 6.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tatoeba+Last+Dungeon+Mae+no+Mura+no+Shounen+ga+Joban+no+Machi+de+Kurasu+Youna+Monogatari+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40594__tatoeba_last_dungeon_mae_no_mura_no_shounen_ga_joban_no_machi_de_kurasu_youna_monogatari.txt) | ~10~ | 6910 | 2021-03-22 14:31 | | 37807 | [](https://myanimelist.net/anime/37807/Princess_Principal__Crown_Handler_Movie_1) | [Princess Principal - Crown Handler](https://subsplease.org/shows/princess-principal-crown-handler) | Movie | 2 / 1 | **Finished Airing** | 7.53 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Princess+Principal+Crown+Handler+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/37807__princess_principal_crown_handler_movie_1.txt) | ~9~ | 2846 | 2023-04-16 22:26 | | 57623 | [](https://myanimelist.net/anime/57623/Nijiyon_Animation_2) | [Nijiyon Animation S2](https://subsplease.org/shows/nijiyon-animation-s2) | TV | 12 / 12 | **Finished Airing** | 6.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Nijiyon+Animation+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57623__nijiyon_animation_2.txt) | ~9~ | 1763 | 2024-06-21 13:16 | | 57180 | [](https://myanimelist.net/anime/57180/Yami_Shibai_12) | [Yami Shibai 12](https://subsplease.org/shows/yami-shibai-12) | TV | 13 / 13 | **Finished Airing** | 5.85 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yami+Shibai+12+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57180__yami_shibai_12.txt) | ~9~ | 1475 | 2024-04-07 19:45 | | 55636 | [](https://myanimelist.net/anime/55636/Snack_Basue) | [Snack Basue](https://subsplease.org/shows/snack-basue) | TV | 13 / 13 | **Finished Airing** | 6.49 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Snack+Basue+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55636__snack_basue.txt) | ~9~ | 2595 | 2024-04-05 17:17 | | 53671 | [](https://myanimelist.net/anime/53671/Love_Live_Nijigasaki_Gakuen_School_Idol_Doukoukai__Next_Sky) | [Love Live! Nijigasaki Gakuen School Idol Doukoukai - Next Sky](https://subsplease.org/shows/love-live-nijigasaki-gakuen-school-idol-doukoukai-next-sky) | OVA | 1 / 1 | **Finished Airing** | 7.29 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Love+Live+Nijigasaki+Gakuen+School+Idol+Doukoukai+Next+Sky+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53671__love_live_nijigasaki_gakuen_school_idol_doukoukai_next_sky.txt) | ~9~ | 2022 | 2023-10-28 22:33 | | 53633 | [](https://myanimelist.net/anime/53633/Bullbuster) | [Bullbuster](https://subsplease.org/shows/bullbuster) | TV | 12 / 12 | **Finished Airing** | 6.18 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bullbuster+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53633__bullbuster.txt) | ~9~ | 3544 | 2023-12-20 14:05 | | 53587 | [](https://myanimelist.net/anime/53587/The_Marginal_Service) | [The Marginal Service](https://subsplease.org/shows/the-marginal-service) | TV | 12 / 12 | **Finished Airing** | 5.49 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+Marginal+Service+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53587__the_marginal_service.txt) | ~9~ | 2836 | 2023-06-27 18:31 | | 53213 | [](https://myanimelist.net/anime/53213/Revenger) | [Revenger](https://subsplease.org/shows/revenger) | TV | 12 / 12 | **Finished Airing** | 6.66 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Revenger+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53213__revenger.txt) | ~9~ | 4594 | 2023-03-23 13:31 | | 51466 | [](https://myanimelist.net/anime/51466/Sekai_ga_Horobiru_Mae_ni_Kimi_ni_Aitai) | [Sekai ga Horobiru Mae ni Kimi ni Aitai](https://subsplease.org/shows/sekai-ga-horobiru-mae-ni-kimi-ni-aitai) | Movie | 1 / 1 | **Finished Airing** | 4.93 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sekai+ga+Horobiru+Mae+ni+Kimi+ni+Aitai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51466__sekai_ga_horobiru_mae_ni_kimi_ni_aitai.txt) | ~9~ | 2267 | 2023-04-17 16:40 | | 50923 | [](https://myanimelist.net/anime/50923/Mushikaburi-hime) | [Mushikaburi Hime](https://subsplease.org/shows/mushikaburi-hime) | TV | 12 / 12 | **Finished Airing** | 6.87 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mushikaburi+Hime+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50923__mushikaburi_hime.txt) | ~9~ | 2818 | 2022-12-22 14:30 | | 50871 | [](https://myanimelist.net/anime/50871/Alice_Gear_Aegis_Expansion) | [Alice Gear Aegis Expansion](https://subsplease.org/shows/alice-gear-aegis-expansion) | TV | 13 / 12 | **Finished Airing** | 5.72 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Alice+Gear+Aegis+Expansion+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50871__alice_gear_aegis_expansion.txt) | ~9~ | 2621 | 2023-06-19 12:00 | | 50203 | [](https://myanimelist.net/anime/50203/Love_Live_Superstar_2nd_Season) | [Love Live! Superstar!! S2](https://subsplease.org/shows/love-live-superstar-s2) | TV | 12 / 12 | **Finished Airing** | 7.68 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Love+Live+Superstar+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50203__love_live_superstar_2nd_season.txt) | ~9~ | 1431 | 2022-10-11 01:53 | | 48643 | [](https://myanimelist.net/anime/48643/Koi_wa_Sekai_Seifuku_no_Ato_de) | [Koi wa Sekai Seifuku no Ato de](https://subsplease.org/shows/koi-wa-sekai-seifuku-no-ato-de) | TV | 12 / 12 | **Finished Airing** | 7.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Koi+wa+Sekai+Seifuku+no+Ato+de+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48643__koi_wa_sekai_seifuku_no_ato_de.txt) | ~9~ | 5059 | 2022-06-24 14:01 | | 47161 | [](https://myanimelist.net/anime/47161/Shikkakumon_no_Saikyou_Kenja) | [Shikkakumon no Saikyou Kenja](https://subsplease.org/shows/shikkakumon-no-saikyou-kenja) | TV | 12 / 12 | **Finished Airing** | 6.23 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shikkakumon+no+Saikyou+Kenja+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47161__shikkakumon_no_saikyou_kenja.txt) | ~9~ | 6416 | 2022-03-26 14:01 | | 44248 | [](https://myanimelist.net/anime/44248/Fate_Grand_Carnival) | [Fate Grand Carnival](https://subsplease.org/shows/fate-grand-carnival) | OVA | 2 / 4 | **Finished Airing** | 7.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fate+Grand+Carnival+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44248__fate_grand_carnival.txt) | ~9~ | 3198 | 2022-07-04 20:59 | | 40748 | [](https://myanimelist.net/anime/40748/Jujutsu_Kaisen) | [Jujutsu Kaisen](https://subsplease.org/shows/jujutsu-kaisen) | TV | 48 / 24 | **Finished Airing** | 8.57 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jujutsu+Kaisen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40748__jujutsu_kaisen.txt) | ~9~ | 31944 | 2023-12-28 18:17 | | 40594 | [](https://myanimelist.net/anime/40594/Tatoeba_Last_Dungeon_Mae_no_Mura_no_Shounen_ga_Joban_no_Machi_de_Kurasu_Youna_Monogatari) | [Last Dungeon](https://subsplease.org/shows/tatoeba-last-dungeon-mae-no-mura-no-shounen-ga-joban-no-machi-de-kurasu-youna-monogatari) | TV | 12 / 12 | **Finished Airing** | 6.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Last+Dungeon+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40594__tatoeba_last_dungeon_mae_no_mura_no_shounen_ga_joban_no_machi_de_kurasu_youna_monogatari.txt) | ~9~ | 6910 | 2021-03-22 14:31 | | 53698 | [](https://myanimelist.net/anime/53698/World_Dai_Star) | [World Dai Star](https://subsplease.org/shows/world-dai-star) | TV | 12 / 12 | **Finished Airing** | 7.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+World+Dai+Star+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53698__world_dai_star.txt) | ~9~ | 2102 | 2023-06-25 15:31 | | 51403 | [](https://myanimelist.net/anime/51403/Renai_Flops) | [Renai Flops](https://subsplease.org/shows/renai-flops) | TV | 12 / 12 | **Finished Airing** | 6.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Renai+Flops+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51403__renai_flops.txt) | ~9~ | 5020 | 2022-12-28 16:05 | | 48441 | [](https://myanimelist.net/anime/48441/The_Legend_of_Heroes__Sen_no_Kiseki_-_Northern_War) | [The Legend of Heroes - Sen no Kiseki - Northern War](https://subsplease.org/shows/the-legend-of-heroes-sen-no-kiseki-northern-war) | TV | 12 / 12 | **Finished Airing** | 5.63 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+Legend+of+Heroes+Sen+no+Kiseki+Northern+War+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48441__the_legend_of_heroes_sen_no_kiseki_northern_war.txt) | ~9~ | 3340 | 2023-03-24 13:31 | | 41812 | [](https://myanimelist.net/anime/41812/Megami-ryou_no_Ryoubo-kun) | [Megami-ryou no Ryoubo-kun.](https://subsplease.org/shows/megami-ryou-no-ryoubo-kun) | TV | 10 / 10 | **Finished Airing** | 6.5 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Megami+ryou+no+Ryoubo+kun+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41812__megami_ryou_no_ryoubo_kun.txt) | ~8~ | 4329 | 2021-09-15 16:03 | | 56691 | [](https://myanimelist.net/anime/56691/Gekkan_Mousou_Kagaku) | [Gekkan Mousou Kagaku](https://subsplease.org/shows/gekkan-mousou-kagaku) | TV | 12 / 12 | **Finished Airing** | 5.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gekkan+Mousou+Kagaku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/56691__gekkan_mousou_kagaku.txt) | ~8~ | 2412 | 2024-03-28 15:31 | | 55153 | [](https://myanimelist.net/anime/55153/Yuzuki-san_Chi_no_Yonkyoudai) | [Yuzuki-san Chi no Yonkyoudai](https://subsplease.org/shows/yuzuki-san-chi-no-yonkyoudai) | TV | 12 / 12 | **Finished Airing** | 7.88 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yuzuki+san+Chi+no+Yonkyoudai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55153__yuzuki_san_chi_no_yonkyoudai.txt) | ~8~ | 3156 | 2023-12-21 17:05 | | 52274 | [](https://myanimelist.net/anime/52274/Nokemono-tachi_no_Yoru) | [Nokemono-tachi no Yoru](https://subsplease.org/shows/nokemono-tachi-no-yoru) | TV | 13 / 13 | **Finished Airing** | 6.61 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Nokemono+tachi+no+Yoru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52274__nokemono_tachi_no_yoru.txt) | ~8~ | 3035 | 2023-04-02 13:31 | | 51956 | [](https://myanimelist.net/anime/51956/Paradox_Live_the_Animation) | [Paradox Live](https://subsplease.org/shows/paradox-live) | TV | 12 / 12 | **Finished Airing** | 6.86 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Paradox+Live+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51956__paradox_live_the_animation.txt) | ~8~ | 1979 | 2023-12-26 19:01 | | 51139 | [](https://myanimelist.net/anime/51139/Kizuna_no_Allele) | [Kizuna no Allele](https://subsplease.org/shows/kizuna-no-allele) | TV | 24 / 12 | **Finished Airing** | 5.24 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kizuna+no+Allele+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51139__kizuna_no_allele.txt) | ~8~ | 2073 | 2023-12-20 17:50 | | 50571 | [](https://myanimelist.net/anime/50571/Zanting_Rang_Wo_Cha_Gonglue) | [Kouryaku Wanted - Isekai Sukuimasu](https://subsplease.org/shows/kouryaku-wanted-isekai-sukuimasu) | ONA | 12 / 12 | **Finished Airing** | 6.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kouryaku+Wanted+Isekai+Sukuimasu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50571__zanting_rang_wo_cha_gonglue.txt) | ~8~ | 2734 | 2023-12-22 16:35 | | 50338 | [](https://myanimelist.net/anime/50338/Kunoichi_Tsubaki_no_Mune_no_Uchi) | [Kunoichi Tsubaki no Mune no Uchi](https://subsplease.org/shows/kunoichi-tsubaki-no-mune-no-uchi) | TV | 13 / 13 | **Finished Airing** | 7.11 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kunoichi+Tsubaki+no+Mune+no+Uchi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50338__kunoichi_tsubaki_no_mune_no_uchi.txt) | ~8~ | 3004 | 2022-07-02 16:31 | | 50287 | [](https://myanimelist.net/anime/50287/Kyuuketsuki_Sugu_Shinu_2) | [Kyuuketsuki Sugu Shinu S2](https://subsplease.org/shows/kyuuketsuki-sugu-shinu-s2) | TV | 12 / 12 | **Finished Airing** | 7.45 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kyuuketsuki+Sugu+Shinu+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50287__kyuuketsuki_sugu_shinu_2.txt) | ~8~ | 1981 | 2023-03-27 14:02 | | 48414 | [](https://myanimelist.net/anime/48414/Sabikui_Bisco) | [Sabikui Bisco](https://subsplease.org/shows/sabikui-bisco) | TV | 12 / 12 | **Finished Airing** | 7.14 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sabikui+Bisco+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48414__sabikui_bisco.txt) | ~8~ | 5688 | 2022-03-28 15:31 | | 45425 | [](https://myanimelist.net/anime/45425/Slow_Loop) | [Slow Loop](https://subsplease.org/shows/slow-loop) | TV | 12 / 12 | **Finished Airing** | 7.25 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Slow+Loop+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/45425__slow_loop.txt) | ~8~ | 2821 | 2022-03-25 14:01 | | 43556 | [](https://myanimelist.net/anime/43556/Tsurune_Movie__Hajimari_no_Issha) | [Tsurune Movie - Hajimari no Issha](https://subsplease.org/shows/tsurune-movie-hajimari-no-issha) | Movie | 1 / 1 | **Finished Airing** | 7.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tsurune+Movie+Hajimari+no+Issha+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43556__tsurune_movie_hajimari_no_issha.txt) | ~8~ | 2180 | 2023-06-19 04:33 | | 43470 | [](https://myanimelist.net/anime/43470/Rikei_ga_Koi_ni_Ochita_no_de_Shoumei_shitemita_Heart) | [Rikei ga Koi ni Ochita no de Shoumei shitemita S2](https://subsplease.org/shows/rikei-ga-koi-ni-ochita-no-de-shoumei-shitemita-s2) | TV | 12 / 12 | **Finished Airing** | 7.3 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Rikei+ga+Koi+ni+Ochita+no+de+Shoumei+shitemita+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43470__rikei_ga_koi_ni_ochita_no_de_shoumei_shitemita_heart.txt) | ~8~ | 2631 | 2022-06-17 16:47 | | 41589 | [](https://myanimelist.net/anime/41589/Tokyo_Mew_Mew_New_♡) | [Tokyo Mew Mew New](https://subsplease.org/shows/tokyo-mew-mew-new) | TV | 24 / 12 | **Finished Airing** | 6.61 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tokyo+Mew+Mew+New+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41589__tokyo_mew_mew_new.txt) | ~8~ | 2281 | 2023-06-20 16:02 | | 41379 | [](https://myanimelist.net/anime/41379/Kimi_wa_Kanata) | [Kimi wa Kanata](https://subsplease.org/shows/kimi-wa-kanata) | Movie | 1 / 1 | **Finished Airing** | 5.92 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kimi+wa+Kanata+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41379__kimi_wa_kanata.txt) | ~8~ | 2079 | 2021-10-22 16:49 | | 41025 | [](https://myanimelist.net/anime/41025/Fumetsu_no_Anata_e) | [Fumetsu no Anata e](https://subsplease.org/shows/fumetsu-no-anata-e) | TV | 20 / 20 | **Finished Airing** | 8.34 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fumetsu+no+Anata+e+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41025__fumetsu_no_anata_e.txt) | ~8~ | 8831 | 2021-08-30 16:32 | | 40904 | [](https://myanimelist.net/anime/40904/Bokutachi_no_Remake) | [Bokutachi no Remake](https://subsplease.org/shows/bokutachi-no-remake) | TV | 13 / 12 | **Finished Airing** | 7.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bokutachi+no+Remake+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40904__bokutachi_no_remake.txt) | ~8~ | 5724 | 2021-09-25 14:32 | | 39808 | [](https://myanimelist.net/anime/39808/Non_Non_Biyori_Nonstop) | [Non Non Biyori Nonstop](https://subsplease.org/shows/non-non-biyori-nonstop) | TV | 12 / 12 | **Finished Airing** | 8.36 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Non+Non+Biyori+Nonstop+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39808__non_non_biyori_nonstop.txt) | ~8~ | 3222 | 2021-03-28 17:53 | | 39584 | [](https://myanimelist.net/anime/39584/Human_Lost__Ningen_Shikkaku) | [Human Lost](https://subsplease.org/shows/human-lost) | Movie | 1 / 1 | **Finished Airing** | 5.79 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Human+Lost+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39584__human_lost_ningen_shikkaku.txt) | ~8~ | 1379 | 2022-08-13 06:39 | | 49721 | [](https://myanimelist.net/anime/49721/Karakai_Jouzu_no_Takagi-san_3) | [Karakai Jouzu no Takagi-san S3](https://subsplease.org/shows/karakai-jouzu-no-takagi-san-s3) | TV | 12 / 12 | **Finished Airing** | 8.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Karakai+Jouzu+no+Takagi+san+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49721__karakai_jouzu_no_takagi_san_3.txt) | ~8~ | 5254 | 2022-03-25 17:05 | | 50917 | [](https://myanimelist.net/anime/50917/Prima_Doll) | [Prima Doll](https://subsplease.org/shows/prima-doll) | TV | 12 / 12 | **Finished Airing** | 7.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Prima+Doll+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50917__prima_doll.txt) | ~7~ | 2544 | 2022-09-23 16:31 | | 50429 | [](https://myanimelist.net/anime/50429/Aiyou_de_Mishi) | [X and Y](https://subsplease.org/shows/x-and-y) | ONA | 16 / 16 | **Finished Airing** | 7.25 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+X+and+Y+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50429__aiyou_de_mishi.txt) | ~7~ | 1796 | 2023-07-19 04:01 | | 50348 | [](https://myanimelist.net/anime/50348/Peter_Grill_to_Kenja_no_Jikan__Super_Extra) | [Peter Grill to Kenja no Jikan S2](https://subsplease.org/shows/peter-grill-to-kenja-no-jikan-s2) | TV | 12 / 12 | **Finished Airing** | 5.94 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Peter+Grill+to+Kenja+no+Jikan+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50348__peter_grill_to_kenja_no_jikan_super_extra.txt) | ~7~ | 2588 | 2022-12-25 16:30 | | 50002 | [](https://myanimelist.net/anime/50002/Edens_Zero_2nd_Season) | [Edens Zero](https://subsplease.org/shows/edens-zero) | TV | 25 / 25 | **Finished Airing** | 7.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Edens+Zero+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50002__edens_zero_2nd_season.txt) | ~7~ | 4191 | 2023-09-30 18:01 | | 49533 | [](https://myanimelist.net/anime/49533/Uchi_no_Shishou_wa_Shippo_ga_Nai) | [Uchi no Shishou wa Shippo ga Nai](https://subsplease.org/shows/uchi-no-shishou-wa-shippo-ga-nai) | TV | 13 / 13 | **Finished Airing** | 6.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Uchi+no+Shishou+wa+Shippo+ga+Nai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49533__uchi_no_shishou_wa_shippo_ga_nai.txt) | ~7~ | 2189 | 2022-12-23 14:30 | | 49376 | [](https://myanimelist.net/anime/49376/Mou_Ippon) | [Mou Ippon!](https://subsplease.org/shows/mou-ippon) | TV | 13 / 13 | **Finished Airing** | 7.09 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mou+Ippon+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49376__mou_ippon.txt) | ~7~ | 2911 | 2023-04-02 17:35 | | 48553 | [](https://myanimelist.net/anime/48553/Akebi-chan_no_Sailor-fuku) | [Akebi-chan no Sailor-fuku](https://subsplease.org/shows/akebi-chan-no-sailor-fuku) | TV | 12 / 12 | **Finished Airing** | 7.65 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Akebi+chan+no+Sailor+fuku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48553__akebi_chan_no_sailor_fuku.txt) | ~7~ | 4549 | 2022-03-26 17:01 | | 48471 | [](https://myanimelist.net/anime/48471/Tsuki_to_Laika_to_Nosferatu) | [Tsuki to Laika to Nosferatu](https://subsplease.org/shows/tsuki-to-laika-to-nosferatu) | TV | 12 / 12 | **Finished Airing** | 7.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tsuki+to+Laika+to+Nosferatu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48471__tsuki_to_laika_to_nosferatu.txt) | ~7~ | 4914 | 2021-12-19 17:07 | | 47257 | [](https://myanimelist.net/anime/47257/Shinigami_Bocchan_to_Kuro_Maid) | [Shinigami Bocchan to Kuro Maid](https://subsplease.org/shows/shinigami-bocchan-to-kuro-maid) | TV | 36 / 12 | **Finished Airing** | 7.59 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shinigami+Bocchan+to+Kuro+Maid+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47257__shinigami_bocchan_to_kuro_maid.txt) | ~7~ | 4310 | 2024-06-23 14:02 | | 46604 | [](https://myanimelist.net/anime/46604/Dolls_Frontline) | [Girls' Frontline](https://subsplease.org/shows/girls-frontline) | TV | 12 / 12 | **Finished Airing** | 5.87 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Girls+Frontline+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/46604__dolls_frontline.txt) | ~7~ | 2428 | 2022-03-25 17:02 | | 46471 | [](https://myanimelist.net/anime/46471/Tantei_wa_Mou_Shindeiru) | [Tantei wa Mou, Shindeiru.](https://subsplease.org/shows/tantei-wa-mou-shindeiru) | TV | 12 / 12 | **Finished Airing** | 6.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tantei+wa+Mou+Shindeiru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/46471__tantei_wa_mou_shindeiru.txt) | ~7~ | 5123 | 2021-09-19 13:32 | | 44586 | [](https://myanimelist.net/anime/44586/Kakushigoto_Movie) | [Kakushigoto Movie](https://subsplease.org/shows/kakushigoto-movie) | Movie | 1 / 1 | **Finished Airing** | 7.68 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kakushigoto+Movie+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44586__kakushigoto_movie.txt) | ~7~ | 2210 | 2021-08-07 03:35 | | 42897 | [](https://myanimelist.net/anime/42897/Horimiya) | [Horimiya](https://subsplease.org/shows/horimiya) | TV | 13 / 13 | **Finished Airing** | 8.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Horimiya+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42897__horimiya.txt) | ~7~ | 9376 | 2021-04-03 17:05 | | 42670 | [](https://myanimelist.net/anime/42670/Princess_Connect_Re_Dive_Season_2) | [Princess Connect! Re-Dive S2](https://subsplease.org/shows/princess-connect-re-dive-s2) | TV | 12 / 12 | **Finished Airing** | 7.75 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Princess+Connect+Re+Dive+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42670__princess_connect_re_dive_season_2.txt) | ~7~ | 3649 | 2022-03-28 16:01 | | 41782 | [](https://myanimelist.net/anime/41782/BanG_Dream_Movie__Poppin_Dream) | [BanG Dream! Movie](https://subsplease.org/shows/bang-dream-movie) | Movie | 1 / 1 | **Finished Airing** | 7.89 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+BanG+Dream+Movie+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41782__bang_dream_movie_poppin_dream.txt) | ~7~ | 953 | 2022-09-05 00:56 | | 41623 | [](https://myanimelist.net/anime/41623/Isekai_Maou_to_Shoukan_Shoujo_no_Dorei_Majutsu_Ω) | [Isekai Maou to Shoukan Shoujo no Dorei Majutsu S2](https://subsplease.org/shows/isekai-maou-to-shoukan-shoujo-no-dorei-majutsu-s2) | TV | 10 / 10 | **Finished Airing** | 6.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+Maou+to+Shoukan+Shoujo+no+Dorei+Majutsu+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41623__isekai_maou_to_shoukan_shoujo_no_dorei_majutsu_%CF%89.txt) | ~7~ | 5424 | 2021-06-10 18:46 | | 41530 | [](https://myanimelist.net/anime/41530/Magia_Record__Mahou_Shoujo_Madoka☆Magica_Gaiden_2nd_Season_-_Kakusei_Zenya) | [Magia Record S2](https://subsplease.org/shows/magia-record-s2) | TV | 9 / 8 | **Finished Airing** | 7.0 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Magia+Record+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41530__magia_record_mahou_shoujo_madoka_magica_gaiden_2nd_season_kakusei_zenya.txt) | ~7~ | 2175 | 2021-09-25 16:33 | | 41402 | [](https://myanimelist.net/anime/41402/Mairimashita_Iruma-kun_2nd_Season) | [Mairimashita! Iruma-kun S2](https://subsplease.org/shows/mairimashita-iruma-kun-s2) | TV | 21 / 21 | **Finished Airing** | 8.03 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mairimashita+Iruma+kun+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41402__mairimashita_iruma_kun_2nd_season.txt) | ~7~ | 3890 | 2021-09-11 11:16 | | 39783 | [](https://myanimelist.net/anime/39783/5-toubun_no_Hanayome_∬) | [Go-toubun no Hanayome S2](https://subsplease.org/shows/go-toubun-no-hanayome-s2) | TV | 12 / 12 | **Finished Airing** | 8.02 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Go+toubun+no+Hanayome+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39783__5_toubun_no_hanayome.txt) | ~7~ | 4264 | 2021-03-25 21:01 | | 37984 | [](https://myanimelist.net/anime/37984/Kumo_desu_ga_Nani_ka) | [Kumo desu ga, Nani ka](https://subsplease.org/shows/kumo-desu-ga-nani-ka) | TV | 24 / 24 | **Finished Airing** | 7.45 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kumo+desu+ga+Nani+ka+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/37984__kumo_desu_ga_nani_ka.txt) | ~7~ | 7801 | 2021-07-03 13:02 | | 50284 | [](https://myanimelist.net/anime/50284/Technoroid__Overmind) | [Technoroid Overmind](https://subsplease.org/shows/technoroid-overmind) | TV | 12 / 12 | **Finished Airing** | 6.53 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Technoroid+Overmind+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50284__technoroid_overmind.txt) | ~7~ | 1455 | 2023-03-29 16:31 | | 50273 | [](https://myanimelist.net/anime/50273/Tomodachi_Game) | [Tomodachi Game](https://subsplease.org/shows/tomodachi-game) | TV | 12 / 12 | **Finished Airing** | 7.72 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tomodachi+Game+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50273__tomodachi_game.txt) | ~7~ | 3966 | 2022-06-21 15:01 | | 48997 | [](https://myanimelist.net/anime/48997/Fantasy_Bishoujo_Juniku_Ojisan_to) | [Fantasy Bishoujo Juniku Ojisan to](https://subsplease.org/shows/fantasy-bishoujo-juniku-ojisan-to) | TV | 12 / 12 | **Finished Airing** | 7.13 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fantasy+Bishoujo+Juniku+Ojisan+to+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48997__fantasy_bishoujo_juniku_ojisan_to.txt) | ~7~ | 4899 | 2022-03-29 16:31 | | 48405 | [](https://myanimelist.net/anime/48405/Totsukuni_no_Shoujo_2022) | [Totsukuni no Shoujo](https://subsplease.org/shows/totsukuni-no-shoujo) | OVA | 3 / 1 | **Finished Airing** | 7.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Totsukuni+no+Shoujo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48405__totsukuni_no_shoujo_2022.txt) | ~6~ | 1705 | 2022-08-05 17:17 | | 41780 | [](https://myanimelist.net/anime/41780/BanG_Dream_Movie__Episode_of_Roselia_-_I__Yakusoku) | [BanG Dream! Movie - Episode of Roselia](https://subsplease.org/shows/bang-dream-movie-episode-of-roselia) | Movie | 2 / 1 | **Finished Airing** | 7.71 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+BanG+Dream+Movie+Episode+of+Roselia+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41780__bang_dream_movie_episode_of_roselia_i_yakusoku.txt) | ~6~ | 878 | 2022-08-06 17:28 | | 55166 | [](https://myanimelist.net/anime/55166/Yami_Shibai_11) | [Yami Shibai 11](https://subsplease.org/shows/yami-shibai-11) | TV | 13 / 13 | **Finished Airing** | 5.83 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yami+Shibai+11+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/55166__yami_shibai_11.txt) | ~6~ | 1454 | 2023-10-01 19:30 | | 54803 | [](https://myanimelist.net/anime/54803/Captain_Tsubasa_Season_2__Junior_Youth-hen) | [Captain Tsubasa S2](https://subsplease.org/shows/captain-tsubasa-s2) | TV | 39 / 39 | **Finished Airing** | 7.42 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Captain+Tsubasa+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54803__captain_tsubasa_season_2_junior_youth_hen.txt) | ~6~ | 1602 | 2024-06-30 09:02 | | 54738 | [](https://myanimelist.net/anime/54738/Majutsushi_Orphen_Hagure_Tabi__Seiiki-hen) | [Majutsushi Orphen Hagure Tabi S4](https://subsplease.org/shows/majutsushi-orphen-hagure-tabi-s4) | TV | 12 / 12 | **Finished Airing** | 6.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Majutsushi+Orphen+Hagure+Tabi+S4+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54738__majutsushi_orphen_hagure_tabi_seiiki_hen.txt) | ~6~ | 2198 | 2023-06-28 12:31 | | 53162 | [](https://myanimelist.net/anime/53162/Majutsushi_Orphen_Hagure_Tabi__Urbanrama-hen) | [Majutsushi Orphen Hagure Tabi S3](https://subsplease.org/shows/majutsushi-orphen-hagure-tabi-s3) | TV | 12 / 12 | **Finished Airing** | 6.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Majutsushi+Orphen+Hagure+Tabi+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53162__majutsushi_orphen_hagure_tabi_urbanrama_hen.txt) | ~6~ | 1879 | 2023-04-05 12:37 | | 50891 | [](https://myanimelist.net/anime/50891/Hoshi_no_Samidare) | [Hoshi no Samidare](https://subsplease.org/shows/hoshi-no-samidare) | TV | 25 / 24 | **Finished Airing** | 5.63 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hoshi+no+Samidare+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50891__hoshi_no_samidare.txt) | ~6~ | 2372 | 2022-12-23 19:46 | | 48916 | [](https://myanimelist.net/anime/48916/Love_Live_Nijigasaki_Gakuen_School_Idol_Doukoukai_2nd_Season) | [Love Live! Nijigasaki Gakuen School Idol Doukoukai S2](https://subsplease.org/shows/love-live-nijigasaki-gakuen-school-idol-doukoukai-s2) | TV | 13 / 13 | **Finished Airing** | 7.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Love+Live+Nijigasaki+Gakuen+School+Idol+Doukoukai+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48916__love_live_nijigasaki_gakuen_school_idol_doukoukai_2nd_season.txt) | ~6~ | 1551 | 2022-06-25 13:31 | | 48363 | [](https://myanimelist.net/anime/48363/RPG_Fudousan) | [RPG Fudousan](https://subsplease.org/shows/rpg-fudousan) | TV | 12 / 12 | **Finished Airing** | 6.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+RPG+Fudousan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48363__rpg_fudousan.txt) | ~6~ | 2711 | 2022-06-22 13:33 | | 46093 | [](https://myanimelist.net/anime/46093/Shiroi_Suna_no_Aquatope) | [Shiroi Suna no Aquatope](https://subsplease.org/shows/shiroi-suna-no-aquatope) | TV | 24 / 24 | **Finished Airing** | 7.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shiroi+Suna+no+Aquatope+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/46093__shiroi_suna_no_aquatope.txt) | ~6~ | 3645 | 2021-12-16 17:31 | | 44074 | [](https://myanimelist.net/anime/44074/Shiguang_Dailiren) | [Link Click](https://subsplease.org/shows/link-click) | ONA | 13 / 11 | **Finished Airing** | 8.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Link+Click+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44074__shiguang_dailiren.txt) | ~6~ | 1788 | 2021-08-28 20:28 | | 44037 | [](https://myanimelist.net/anime/44037/Shin_no_Nakama_ja_Nai_to_Yuusha_no_Party_wo_Oidasareta_node_Henkyou_de_Slow_Life_suru_Koto_ni_Shimashita) | [Shin no Nakama](https://subsplease.org/shows/shin-no-nakama) | TV | 13 / 13 | **Finished Airing** | 6.93 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shin+no+Nakama+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44037__shin_no_nakama_ja_nai_to_yuusha_no_party_wo_oidasareta_node_henkyou_de_slow_life_suru_koto_ni_shimashita.txt) | ~6~ | 7887 | 2021-12-29 14:32 | | 43969 | [](https://myanimelist.net/anime/43969/Kanojo_mo_Kanojo) | [Kanojo mo Kanojo](https://subsplease.org/shows/kanojo-mo-kanojo) | TV | 12 / 12 | **Finished Airing** | 6.52 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kanojo+mo+Kanojo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43969__kanojo_mo_kanojo.txt) | ~6~ | 4922 | 2021-09-17 19:02 | | 43762 | [](https://myanimelist.net/anime/43762/Hula_Fulla_Dance) | [Hula Fulla Dance](https://subsplease.org/shows/hula-fulla-dance) | Movie | 1 / 1 | **Finished Airing** | 6.3 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hula+Fulla+Dance+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43762__hula_fulla_dance.txt) | ~6~ | 1044 | 2023-06-23 16:37 | | 43523 | [](https://myanimelist.net/anime/43523/Tsuki_ga_Michibiku_Isekai_Douchuu) | [Tsuki ga Michibiku Isekai Douchuu](https://subsplease.org/shows/tsuki-ga-michibiku-isekai-douchuu) | TV | 12 / 12 | **Finished Airing** | 7.71 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tsuki+ga+Michibiku+Isekai+Douchuu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43523__tsuki_ga_michibiku_isekai_douchuu.txt) | ~6~ | 8280 | 2021-09-22 15:07 | | 42072 | [](https://myanimelist.net/anime/42072/Kenja_no_Deshi_wo_Nanoru_Kenja) | [Kenja no Deshi wo Nanoru Kenja](https://subsplease.org/shows/kenja-no-deshi-wo-nanoru-kenja) | TV | 12 / 12 | **Finished Airing** | 6.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kenja+no+Deshi+wo+Nanoru+Kenja+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42072__kenja_no_deshi_wo_nanoru_kenja.txt) | ~6~ | 4558 | 2022-03-30 03:12 | | 40454 | [](https://myanimelist.net/anime/40454/Dungeon_ni_Deai_wo_Motomeru_no_wa_Machigatteiru_Darou_ka_III) | [Dungeon ni Deai wo Motomeru no wa Machigatteiru Darou ka S3](https://subsplease.org/shows/dungeon-ni-deai-wo-motomeru-no-wa-machigatteiru-darou-ka-s3) | TV | 13 / 12 | **Finished Airing** | 7.46 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dungeon+ni+Deai+wo+Motomeru+no+wa+Machigatteiru+Darou+ka+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40454__dungeon_ni_deai_wo_motomeru_no_wa_machigatteiru_darou_ka_iii.txt) | ~6~ | 5835 | 2021-04-29 23:49 | | 40174 | [](https://myanimelist.net/anime/40174/Zombieland_Saga_Revenge) | [Zombieland Saga S2](https://subsplease.org/shows/zombieland-saga-s2) | TV | 12 / 12 | **Finished Airing** | 7.99 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Zombieland+Saga+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40174__zombieland_saga_revenge.txt) | ~6~ | 3448 | 2021-06-24 15:32 | | 39990 | [](https://myanimelist.net/anime/39990/Vlad_Love) | [Vlad Love](https://subsplease.org/shows/vlad-love) | ONA | 12 / 12 | **Finished Airing** | 5.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Vlad+Love+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39990__vlad_love.txt) | ~6~ | 1693 | 2021-03-13 21:31 | | 33839 | [](https://myanimelist.net/anime/33839/Alice_in_Deadly_School) | [Alice in Deadly School](https://subsplease.org/shows/alice-in-deadly-school) | OVA | 1 / 1 | **Finished Airing** | 5.18 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Alice+in+Deadly+School+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/33839__alice_in_deadly_school.txt) | ~6~ | 1808 | 2021-04-06 16:58 | | 50404 | [](https://myanimelist.net/anime/50404/Xian_Wang_de_Richang_Shenghuo_3) | [The Daily Life of the Immortal King S3](https://subsplease.org/shows/the-daily-life-of-the-immortal-king-s3) | ONA | 12 / 12 | **Finished Airing** | 7.35 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+Daily+Life+of+the+Immortal+King+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50404__xian_wang_de_richang_shenghuo_3.txt) | ~6~ | 2940 | 2022-12-11 06:01 | | 49160 | [](https://myanimelist.net/anime/49160/Shachiku-san_wa_Youjo_Yuurei_ni_Iyasaretai) | [Shachiku-san wa Youjo Yuurei ni Iyasaretai](https://subsplease.org/shows/shachiku-san-wa-youjo-yuurei-ni-iyasaretai) | TV | 12 / 12 | **Finished Airing** | 7.1 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shachiku+san+wa+Youjo+Yuurei+ni+Iyasaretai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49160__shachiku_san_wa_youjo_yuurei_ni_iyasaretai.txt) | ~6~ | 1772 | 2022-06-23 13:31 | | 48857 | [](https://myanimelist.net/anime/48857/Healer_Girl) | [Healer Girl](https://subsplease.org/shows/healer-girl) | TV | 12 / 12 | **Finished Airing** | 7.31 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Healer+Girl+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48857__healer_girl.txt) | ~6~ | 1696 | 2022-06-20 14:31 | | 48849 | [](https://myanimelist.net/anime/48849/Sonny_Boy) | [Sonny Boy](https://subsplease.org/shows/sonny-boy) | TV | 12 / 12 | **Finished Airing** | 7.82 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sonny+Boy+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48849__sonny_boy.txt) | ~6~ | 5279 | 2021-09-30 16:31 | | 45055 | [](https://myanimelist.net/anime/45055/Taishou_Otome_Otogibanashi) | [Taishou Otome Otogibanashi](https://subsplease.org/shows/taishou-otome-otogibanashi) | TV | 12 / 12 | **Finished Airing** | 7.79 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Taishou+Otome+Otogibanashi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/45055__taishou_otome_otogibanashi.txt) | ~6~ | 3007 | 2021-12-24 17:56 | | 44524 | [](https://myanimelist.net/anime/44524/Isekai_Meikyuu_de_Harem_wo) | [Isekai Meikyuu de Harem wo](https://subsplease.org/shows/isekai-meikyuu-de-harem-wo) | TV | 12 / 12 | **Finished Airing** | 6.54 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+Meikyuu+de+Harem+wo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44524__isekai_meikyuu_de_harem_wo.txt) | ~6~ | 5191 | 2022-09-22 00:12 | | 40685 | [](https://myanimelist.net/anime/40685/Super_Cub) | [Super Cub](https://subsplease.org/shows/super-cub) | TV | 12 / 12 | **Finished Airing** | 7.45 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Super+Cub+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40685__super_cub.txt) | ~6~ | 3127 | 2021-06-23 15:02 | | 39586 | [](https://myanimelist.net/anime/39586/Hataraku_Saibou) | [Hataraku Saibou S2](https://subsplease.org/shows/hataraku-saibou-s2) | TV | 8 / 8 | **Finished Airing** | 7.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hataraku+Saibou+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39586__hataraku_saibou.txt) | ~6~ | 4149 | 2021-02-25 17:01 | | 49738 | [](https://myanimelist.net/anime/49738/Heike_Monogatari) | [Heike Monogatari](https://subsplease.org/shows/heike-monogatari) | TV | 11 / 11 | **Finished Airing** | 7.78 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Heike+Monogatari+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49738__heike_monogatari.txt) | ~6~ | 3145 | 2021-11-24 15:03 | | 57995 | [](https://myanimelist.net/anime/57995/Bai_Yao_Pu_4th_Season) | [Fairies Album S4](https://subsplease.org/shows/fairies-album-s4) | ONA | 12 / 12 | **Finished Airing** | 7.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fairies+Album+S4+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/57995__bai_yao_pu_4th_season.txt) | ~5~ | 935 | 2024-05-03 03:01 | | 54118 | [](https://myanimelist.net/anime/54118/IDOLiSH7_Movie__LIVE_4bit_-_BEYOND_THE_PERiOD) | [IDOLiSH7 Movie - LIVE 4bit](https://subsplease.org/shows/idolish7-movie-live-4bit) | Movie | 2 / 2 | **Finished Airing** | 7.88 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+IDOLiSH7+Movie+LIVE+4bit+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54118__idolish7_movie_live_4bit_beyond_the_period.txt) | ~5~ | 954 | 2023-11-22 10:02 | | 53132 | [](https://myanimelist.net/anime/53132/UniteUp) | [UniteUp!](https://subsplease.org/shows/uniteup) | TV | 12 / 12 | **Finished Airing** | 7.05 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+UniteUp+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53132__uniteup.txt) | ~5~ | 930 | 2023-04-15 16:01 | | 52976 | [](https://myanimelist.net/anime/52976/Berserk__Ougon_Jidai-hen_-_Memorial_Edition) | [Berserk - The Golden Age Arc Memorial Edition](https://subsplease.org/shows/berserk-the-golden-age-arc-memorial-edition) | TV | 13 / 13 | **Finished Airing** | 7.89 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Berserk+The+Golden+Age+Arc+Memorial+Edition+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52976__berserk_ougon_jidai_hen_memorial_edition.txt) | ~5~ | 3488 | 2022-12-24 18:01 | | 52826 | [](https://myanimelist.net/anime/52826/Tsurune__Tsunagari_no_Issha) | [Tsurune S2](https://subsplease.org/shows/tsurune-s2) | TV | 13 / 13 | **Finished Airing** | 8.09 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tsurune+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52826__tsurune_tsunagari_no_issha.txt) | ~5~ | 2837 | 2023-03-29 16:01 | | 51923 | [](https://myanimelist.net/anime/51923/Warau_Arsnotoria_Sun) | [Warau Arsnotoria Sun!](https://subsplease.org/shows/warau-arsnotoria-sun) | TV | 12 / 12 | **Finished Airing** | 5.8 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Warau+Arsnotoria+Sun+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51923__warau_arsnotoria_sun.txt) | ~5~ | 1887 | 2022-09-21 13:01 | | 51586 | [](https://myanimelist.net/anime/51586/D4DJ_All_Mix) | [D4DJ All Mix](https://subsplease.org/shows/d4dj-all-mix) | TV | 12 / 12 | **Finished Airing** | 7.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+D4DJ+All+Mix+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51586__d4dj_all_mix.txt) | ~5~ | 1407 | 2023-03-26 16:31 | | 50250 | [](https://myanimelist.net/anime/50250/Chiikawa) | [Chiikawa](https://subsplease.org/shows/chiikawa) | TV | 52 / ? | Currently Airing | 7.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Chiikawa+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50250__chiikawa.txt) | ~5~ | 959 | 2024-11-28 17:57 | | 49942 | [](https://myanimelist.net/anime/49942/Tales_of_Luminaria__The_Fateful_Crossroad) | [Tales of Luminaria - The Fateful Crossroad](https://subsplease.org/shows/tales-of-luminaria-the-fateful-crossroad) | ONA | 2 / 2 | **Finished Airing** | 6.17 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tales+of+Luminaria+The+Fateful+Crossroad+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49942__tales_of_luminaria_the_fateful_crossroad.txt) | ~5~ | 2080 | 2022-01-21 04:37 | | 49605 | [](https://myanimelist.net/anime/49605/Ganbare_Douki-chan) | [Ganbare Douki-chan](https://subsplease.org/shows/ganbare-douki-chan) | ONA | 12 / 12 | **Finished Airing** | 6.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ganbare+Douki+chan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49605__ganbare_douki_chan.txt) | ~5~ | 4792 | 2021-12-05 23:15 | | 49385 | [](https://myanimelist.net/anime/49385/Kaijin_Kaihatsu-bu_no_Kuroitsu-san) | [Kaijin Kaihatsu-bu no Kuroitsu-san](https://subsplease.org/shows/kaijin-kaihatsu-bu-no-kuroitsu-san) | TV | 12 / 12 | **Finished Airing** | 6.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kaijin+Kaihatsu+bu+no+Kuroitsu+san+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49385__kaijin_kaihatsu_bu_no_kuroitsu_san.txt) | ~5~ | 2856 | 2022-04-02 18:46 | | 49283 | [](https://myanimelist.net/anime/49283/Bakuten_Movie) | [Bakuten!! Movie](https://subsplease.org/shows/bakuten-movie) | Movie | 1 / 1 | **Finished Airing** | 7.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bakuten+Movie+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49283__bakuten_movie.txt) | ~5~ | 1170 | 2023-06-24 00:34 | | 48761 | [](https://myanimelist.net/anime/48761/Saihate_no_Paladin) | [Saihate no Paladin](https://subsplease.org/shows/saihate-no-paladin) | TV | 13 / 12 | **Finished Airing** | 6.87 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Saihate+no+Paladin+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48761__saihate_no_paladin.txt) | ~5~ | 8155 | 2022-01-03 15:31 | | 48573 | [](https://myanimelist.net/anime/48573/Uta_no☆Prince-sama♪_Movie__Maji_Love_ST☆RISH_Tours) | [Uta no Prince-sama Maji Love Starish Tours](https://subsplease.org/shows/uta-no-prince-sama-maji-love-starish-tours) | Movie | 2 / 1 | **Finished Airing** | 7.13 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Uta+no+Prince+sama+Maji+Love+Starish+Tours+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48573__uta_no_prince_sama_movie_maji_love_st_rish_tours.txt) | ~5~ | 816 | 2023-04-16 22:35 | | 48573 | [](https://myanimelist.net/anime/48573/Uta_no☆Prince-sama♪_Movie__Maji_Love_ST☆RISH_Tours) | [Uta no Prince-sama Maji Love Starish Tours Movie](https://subsplease.org/shows/uta-no-prince-sama-maji-love-starish-tours) | Movie | 1 / 1 | **Finished Airing** | 7.13 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Uta+no+Prince+sama+Maji+Love+Starish+Tours+Movie+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48573__uta_no_prince_sama_movie_maji_love_st_rish_tours.txt) | ~5~ | 877 | 2023-04-16 22:35 | | 44203 | [](https://myanimelist.net/anime/44203/Seirei_Gensouki) | [Seirei Gensouki](https://subsplease.org/shows/seirei-gensouki) | TV | 12 / 12 | **Finished Airing** | 7.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Seirei+Gensouki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44203__seirei_gensouki.txt) | ~5~ | 6429 | 2021-09-20 18:57 | | 43439 | [](https://myanimelist.net/anime/43439/Shadows_House) | [Shadows House](https://subsplease.org/shows/shadows-house) | TV | 13 / 13 | **Finished Airing** | 7.8 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shadows+House+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43439__shadows_house.txt) | ~5~ | 3930 | 2021-07-03 17:02 | | 43007 | [](https://myanimelist.net/anime/43007/Osananajimi_ga_Zettai_ni_Makenai_Love_Comedy) | [Osananajimi ga Zettai ni Makenai Love Comedy](https://subsplease.org/shows/osananajimi-ga-zettai-ni-makenai-love-comedy) | TV | 12 / 12 | **Finished Airing** | 6.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Osananajimi+ga+Zettai+ni+Makenai+Love+Comedy+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43007__osananajimi_ga_zettai_ni_makenai_love_comedy.txt) | ~5~ | 2649 | 2021-06-30 13:02 | | 42249 | [](https://myanimelist.net/anime/42249/Tokyo_Revengers) | [Tokyo Revengers](https://subsplease.org/shows/tokyo-revengers) | TV | 24 / 24 | **Finished Airing** | 7.88 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tokyo+Revengers+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42249__tokyo_revengers.txt) | ~5~ | 7362 | 2021-09-18 19:02 | | 42091 | [](https://myanimelist.net/anime/42091/Shingeki_no_Kyojin__Chronicle) | [Shingeki no Kyojin - Chronicle](https://subsplease.org/shows/shingeki-no-kyojin-chronicle) | Movie | 1 / 1 | **Finished Airing** | 7.79 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shingeki+no+Kyojin+Chronicle+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42091__shingeki_no_kyojin_chronicle.txt) | ~5~ | 2826 | 2020-11-24 21:24 | | 41694 | [](https://myanimelist.net/anime/41694/Hataraku_Saibou_Black) | [Hataraku Saibou Black](https://subsplease.org/shows/hataraku-saibou-black) | TV | 13 / 13 | **Finished Airing** | 7.52 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hataraku+Saibou+Black+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41694__hataraku_saibou_black.txt) | ~5~ | 4204 | 2021-03-18 17:32 | | 41392 | [](https://myanimelist.net/anime/41392/Urasekai_Picnic) | [Urasekai Picnic](https://subsplease.org/shows/urasekai-picnic) | TV | 12 / 12 | **Finished Airing** | 6.65 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Urasekai+Picnic+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41392__urasekai_picnic.txt) | ~5~ | 3073 | 2021-03-22 15:31 | | 40730 | [](https://myanimelist.net/anime/40730/Tian_Guan_Cifu) | [Heaven Official's Blessing](https://subsplease.org/shows/heaven-officials-blessing) | ONA | 12 / 11 | **Finished Airing** | 8.42 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Heaven+Official+s+Blessing+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40730__tian_guan_cifu.txt) | ~5~ | 1819 | 2021-02-17 07:45 | | 40620 | [](https://myanimelist.net/anime/40620/Uramichi_Oniisan) | [Uramichi Oniisan](https://subsplease.org/shows/uramichi-oniisan) | TV | 13 / 13 | **Finished Airing** | 7.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Uramichi+Oniisan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40620__uramichi_oniisan.txt) | ~5~ | 2826 | 2021-09-27 16:02 | | 40615 | [](https://myanimelist.net/anime/40615/Umibe_no_Étranger) | [Umibe no Etranger](https://subsplease.org/shows/umibe-no-etranger) | Movie | 1 / 1 | **Finished Airing** | 7.83 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Umibe+no+Etranger+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40615__umibe_no_%C3%A9tranger.txt) | ~5~ | 1552 | 2021-07-10 00:22 | | 40608 | [](https://myanimelist.net/anime/40608/Muv-Luv_Alternative) | [Muv-Luv Alternative](https://subsplease.org/shows/muv-luv-alternative) | TV | 24 / 12 | **Finished Airing** | 5.85 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Muv+Luv+Alternative+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40608__muv_luv_alternative.txt) | ~5~ | 2562 | 2022-12-21 18:26 | | 40590 | [](https://myanimelist.net/anime/40590/Utawarerumono__Futari_no_Hakuoro) | [Utawarerumono - Futari no Hakuoro](https://subsplease.org/shows/utawarerumono-futari-no-hakuoro) | TV | 28 / 28 | **Finished Airing** | 7.52 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Utawarerumono+Futari+no+Hakuoro+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40590__utawarerumono_futari_no_hakuoro.txt) | ~5~ | 2743 | 2022-12-25 12:09 | | 40421 | [](https://myanimelist.net/anime/40421/Given_Movie_1) | [Given Movie](https://subsplease.org/shows/given-movie) | Movie | 1 / 1 | **Finished Airing** | 8.12 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Given+Movie+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40421__given_movie_1.txt) | ~5~ | 1542 | 2021-02-03 03:07 | | 39761 | [](https://myanimelist.net/anime/39761/Saezuru_Tori_wa_Habatakanai__The_Clouds_Gather) | [Saezuru Tori wa Habatakanai - The Clouds Gather](https://subsplease.org/shows/saezuru-tori-wa-habatakanai) | Movie | 1 / 1 | **Finished Airing** | 7.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Saezuru+Tori+wa+Habatakanai+The+Clouds+Gather+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39761__saezuru_tori_wa_habatakanai_the_clouds_gather.txt) | ~5~ | 940 | 2021-05-27 05:13 | | 39617 | [](https://myanimelist.net/anime/39617/Yakusoku_no_Neverland_2nd_Season) | [Yakusoku no Neverland S2](https://subsplease.org/shows/yakusoku-no-neverland-s2) | TV | 12 / 11 | **Finished Airing** | 5.26 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yakusoku+no+Neverland+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39617__yakusoku_no_neverland_2nd_season.txt) | ~5~ | 8219 | 2021-03-25 19:04 | | 38680 | [](https://myanimelist.net/anime/38680/Fruits_Basket_1st_Season) | [Fruits Basket (2019)](https://subsplease.org/shows/fruits-basket-2019) | TV | 13 / 25 | **Finished Airing** | 8.21 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fruits+Basket+2019+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38680__fruits_basket_1st_season.txt) | ~5~ | 2946 | 2021-06-28 17:32 | | 38680 | [](https://myanimelist.net/anime/38680/Fruits_Basket_1st_Season) | [Fruits Basket (2019) S3](https://subsplease.org/shows/fruits-basket-2019) | TV | 13 / 25 | **Finished Airing** | 8.21 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fruits+Basket+2019+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38680__fruits_basket_1st_season.txt) | ~5~ | 2946 | 2021-06-28 17:32 | | 38006 | [](https://myanimelist.net/anime/38006/Renmei_Kuugun_Koukuu_Mahou_Ongakutai_Luminous_Witches) | [Luminous Witches](https://subsplease.org/shows/luminous-witches) | TV | 12 / 12 | **Finished Airing** | 6.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Luminous+Witches+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38006__renmei_kuugun_koukuu_mahou_ongakutai_luminous_witches.txt) | ~5~ | 2171 | 2022-09-25 13:01 | | 48804 | [](https://myanimelist.net/anime/48804/Isekai_Shokudou_2) | [Isekai Shokudou S2](https://subsplease.org/shows/isekai-shokudou-s2) | TV | 12 / 12 | **Finished Airing** | 7.68 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Isekai+Shokudou+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48804__isekai_shokudou_2.txt) | ~5~ | 3839 | 2021-12-17 18:31 | | 48742 | [](https://myanimelist.net/anime/48742/Kono_Healer_Mendokusai) | [Kono Healer, Mendokusai](https://subsplease.org/shows/kono-healer-mendokusai) | TV | 12 / 12 | **Finished Airing** | 6.34 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kono+Healer+Mendokusai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48742__kono_healer_mendokusai.txt) | ~5~ | 2459 | 2022-06-26 12:01 | | 44276 | [](https://myanimelist.net/anime/44276/Kyuukyoku_Shinka_shita_Full_Dive_RPG_ga_Genjitsu_yori_mo_Kusoge_Dattara) | [Full Dive](https://subsplease.org/shows/full-dive) | TV | 12 / 12 | **Finished Airing** | 6.47 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Full+Dive+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44276__kyuukyoku_shinka_shita_full_dive_rpg_ga_genjitsu_yori_mo_kusoge_dattara.txt) | ~5~ | 4424 | 2021-06-23 14:32 | | 53077 | [](https://myanimelist.net/anime/53077/Nijiyon_Animation) | [Nijiyon Animation](https://subsplease.org/shows/nijiyon-animation) | TV | 15 / 12 | **Finished Airing** | 6.81 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Nijiyon+Animation+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53077__nijiyon_animation.txt) | ~4~ | 1560 | 2024-06-01 17:24 | | 42625 | [](https://myanimelist.net/anime/42625/Heion_Sedai_no_Idaten-tachi) | [Heion Sedai no Idaten-tachi](https://subsplease.org/shows/heion-sedai-no-idaten-tachi) | TV | 11 / 11 | **Finished Airing** | 7.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Heion+Sedai+no+Idaten+tachi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42625__heion_sedai_no_idaten_tachi.txt) | ~4~ | 4455 | 2021-09-28 04:02 | | 48830 | [](https://myanimelist.net/anime/48830/Free_Movie_5__The_Final_Stroke_-_Kouhen) | [Free! - The Final Stroke](https://subsplease.org/shows/free-the-final-stroke) | Movie | 2 / 1 | **Finished Airing** | 7.81 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Free+The+Final+Stroke+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48830__free_movie_5_the_final_stroke_kouhen.txt) | ~4~ | 1486 | 2023-11-19 19:19 | | 54858 | [](https://myanimelist.net/anime/54858/Hypnosis_Mic__Division_Rap_Battle_-_Rhyme_Anima__) | [Hypnosis Mic -Division Rap Battle- Rhyme Anima S2](https://subsplease.org/shows/hypnosis-mic-division-rap-battle-rhyme-anima-s2) | TV | 13 / 13 | **Finished Airing** | 6.86 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hypnosis+Mic+Division+Rap+Battle+Rhyme+Anima+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54858__hypnosis_mic_division_rap_battle_rhyme_anima.txt) | ~4~ | 1343 | 2023-12-29 17:32 | | 54716 | [](https://myanimelist.net/anime/54716/Kibou_no_Chikara__Otona_Precure_23) | [Kibou no Chikara - Otona Precure '23](https://subsplease.org/shows/kibou-no-chikara-otona-precure-23) | TV | 12 / 12 | **Finished Airing** | 6.6 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kibou+no+Chikara+Otona+Precure+23+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54716__kibou_no_chikara_otona_precure_23.txt) | ~4~ | 1804 | 2023-12-23 11:50 | | 53716 | [](https://myanimelist.net/anime/53716/Hirogaru_Sky_Precure) | [Hirogaru Sky! Precure](https://subsplease.org/shows/hirogaru-sky-precure) | TV | 50 / 50 | **Finished Airing** | 7.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hirogaru+Sky+Precure+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53716__hirogaru_sky_precure.txt) | ~4~ | 1575 | 2024-01-28 01:31 | | 50760 | [](https://myanimelist.net/anime/50760/Teppen) | [Teppen](https://subsplease.org/shows/teppen) | TV | 12 / 12 | **Finished Airing** | 6.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Teppen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50760__teppen.txt) | ~4~ | 1385 | 2022-09-24 14:24 | | 49854 | [](https://myanimelist.net/anime/49854/Getsuyoubi_no_Tawawa_2) | [Getsuyoubi no Tawawa S2](https://subsplease.org/shows/getsuyoubi-no-tawawa-s2) | ONA | 12 / 12 | **Finished Airing** | 6.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Getsuyoubi+no+Tawawa+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49854__getsuyoubi_no_tawawa_2.txt) | ~4~ | 3505 | 2021-12-05 23:21 | | 49519 | [](https://myanimelist.net/anime/49519/Hakozume__Kouban_Joshi_no_Gyakushuu) | [Hakozume - Kouban Joshi no Gyakushuu](https://subsplease.org/shows/hakozume-kouban-joshi-no-gyakushuu) | TV | 13 / 13 | **Finished Airing** | 7.17 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hakozume+Kouban+Joshi+no+Gyakushuu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49519__hakozume_kouban_joshi_no_gyakushuu.txt) | ~4~ | 2596 | 2022-03-30 15:32 | | 49515 | [](https://myanimelist.net/anime/49515/Digimon_Ghost_Game) | [Digimon Ghost Game](https://subsplease.org/shows/digimon-ghost-game) | TV | 52 / 67 | **Finished Airing** | 6.74 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Digimon+Ghost+Game+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49515__digimon_ghost_game.txt) | ~4~ | 1329 | 2023-03-26 02:31 | | 49052 | [](https://myanimelist.net/anime/49052/Ao_Ashi) | [Ao Ashi](https://subsplease.org/shows/ao-ashi) | TV | 24 / 24 | **Finished Airing** | 8.17 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ao+Ashi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49052__ao_ashi.txt) | ~4~ | 2940 | 2022-09-24 12:01 | | 48680 | [](https://myanimelist.net/anime/48680/Tesla_Note) | [Tesla Note](https://subsplease.org/shows/tesla-note) | TV | 13 / 13 | **Finished Airing** | 4.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tesla+Note+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48680__tesla_note.txt) | ~4~ | 1586 | 2021-12-26 14:32 | | 48580 | [](https://myanimelist.net/anime/48580/Vanitas_no_Karte) | [Vanitas no Carte](https://subsplease.org/shows/vanitas-no-carte) | TV | 25 / 12 | **Finished Airing** | 7.9 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Vanitas+no+Carte+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48580__vanitas_no_karte.txt) | ~4~ | 5265 | 2022-04-01 16:31 | | 47391 | [](https://myanimelist.net/anime/47391/Seven_Knights_Revolution__Eiyuu_no_Keishousha) | [Seven Knights Revolution - Eiyuu no Keishousha](https://subsplease.org/shows/seven-knights-revolution-eiyuu-no-keishousha) | TV | 12 / 12 | **Finished Airing** | 6.3 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Seven+Knights+Revolution+Eiyuu+no+Keishousha+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47391__seven_knights_revolution_eiyuu_no_keishousha.txt) | ~4~ | 1693 | 2021-06-20 17:03 | | 46985 | [](https://myanimelist.net/anime/46985/Shinka_no_Mi__Shiranai_Uchi_ni_Kachigumi_Jinsei) | [Shinka no Mi - Shiranai Uchi ni Kachigumi Jinsei](https://subsplease.org/shows/shinka-no-mi-shiranai-uchi-ni-kachigumi-jinsei) | TV | 12 / 12 | **Finished Airing** | 6.18 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shinka+no+Mi+Shiranai+Uchi+ni+Kachigumi+Jinsei+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/46985__shinka_no_mi_shiranai_uchi_ni_kachigumi_jinsei.txt) | ~4~ | 4390 | 2021-12-20 19:31 | | 45572 | [](https://myanimelist.net/anime/45572/Mahouka_Koukou_no_Yuutousei) | [Mahouka Koukou no Yuutousei](https://subsplease.org/shows/mahouka-koukou-no-yuutousei) | TV | 13 / 13 | **Finished Airing** | 6.68 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahouka+Koukou+no+Yuutousei+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/45572__mahouka_koukou_no_yuutousei.txt) | ~4~ | 4254 | 2021-09-25 16:02 | | 45560 | [](https://myanimelist.net/anime/45560/Orient) | [Orient](https://subsplease.org/shows/orient) | TV | 24 / 12 | **Finished Airing** | 6.61 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Orient+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/45560__orient.txt) | ~4~ | 2248 | 2022-09-26 17:03 | | 44961 | [](https://myanimelist.net/anime/44961/Platinum_End) | [Platinum End](https://subsplease.org/shows/platinum-end) | TV | 24 / 24 | **Finished Airing** | 6.03 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Platinum+End+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44961__platinum_end.txt) | ~4~ | 4537 | 2022-03-24 20:01 | | 43735 | [](https://myanimelist.net/anime/43735/Cue) | [Cue!](https://subsplease.org/shows/cue) | TV | 24 / 24 | **Finished Airing** | 6.78 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cue+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43735__cue.txt) | ~4~ | 1124 | 2022-06-24 18:31 | | 43691 | [](https://myanimelist.net/anime/43691/Kageki_Shoujo) | [Kageki Shoujo!!](https://subsplease.org/shows/kageki-shoujo) | TV | 13 / 13 | **Finished Airing** | 7.72 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kageki+Shoujo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43691__kageki_shoujo.txt) | ~4~ | 2191 | 2021-09-25 16:02 | | 42941 | [](https://myanimelist.net/anime/42941/Uma_Musume__Pretty_Derby_Season_2) | [Uma Musume - Pretty Derby S2](https://subsplease.org/shows/uma-musume-pretty-derby-s2) | TV | 13 / 13 | **Finished Airing** | 8.06 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Uma+Musume+Pretty+Derby+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42941__uma_musume_pretty_derby_season_2.txt) | ~4~ | 1740 | 2021-03-29 16:01 | | 42923 | [](https://myanimelist.net/anime/42923/SK∞) | [SK8 the Infinity](https://subsplease.org/shows/sk8-the-infinity) | TV | 13 / 12 | **Finished Airing** | 8.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+SK8+the+Infinity+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42923__sk.txt) | ~4~ | 3330 | 2021-04-03 18:32 | | 42826 | [](https://myanimelist.net/anime/42826/Seijo_no_Maryoku_wa_Bannou_desu) | [Seijo no Maryoku wa Bannou Desu](https://subsplease.org/shows/seijo-no-maryoku-wa-bannou-desu) | TV | 12 / 12 | **Finished Airing** | 7.31 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Seijo+no+Maryoku+wa+Bannou+Desu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42826__seijo_no_maryoku_wa_bannou_desu.txt) | ~4~ | 4179 | 2021-06-22 15:31 | | 42798 | [](https://myanimelist.net/anime/42798/Sayonara_Watashi_no_Cramer_Movie__First_Touch) | [Sayonara Watashi no Cramer - First Touch](https://subsplease.org/shows/sayonara-watashi-no-cramer-first-touch) | Movie | 1 / 1 | **Finished Airing** | 7.08 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sayonara+Watashi+no+Cramer+First+Touch+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42798__sayonara_watashi_no_cramer_movie_first_touch.txt) | ~4~ | 738 | 2021-06-11 22:21 | | 42774 | [](https://myanimelist.net/anime/42774/Sayonara_Watashi_no_Cramer) | [Sayonara Watashi no Cramer](https://subsplease.org/shows/sayonara-watashi-no-cramer) | TV | 13 / 13 | **Finished Airing** | 6.1 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sayonara+Watashi+no+Cramer+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42774__sayonara_watashi_no_cramer.txt) | ~4~ | 1057 | 2021-06-27 16:01 | | 42340 | [](https://myanimelist.net/anime/42340/Meikyuu_Black_Company) | [Meikyuu Black Company](https://subsplease.org/shows/meikyuu-black-company) | TV | 12 / 12 | **Finished Airing** | 7.23 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Meikyuu+Black+Company+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42340__meikyuu_black_company.txt) | ~4~ | 5840 | 2021-09-24 14:02 | | 41103 | [](https://myanimelist.net/anime/41103/Koi_to_Yobu_ni_wa_Kimochi_Warui) | [Koi to Yobu ni wa Kimochi Warui](https://subsplease.org/shows/koi-to-yobu-ni-wa-kimochi-warui) | TV | 12 / 12 | **Finished Airing** | 7.13 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Koi+to+Yobu+ni+wa+Kimochi+Warui+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41103__koi_to_yobu_ni_wa_kimochi_warui.txt) | ~4~ | 2219 | 2021-06-14 12:46 | | 40530 | [](https://myanimelist.net/anime/40530/Jaku-Chara_Tomozaki-kun) | [Jaku-Chara Tomozaki-kun](https://subsplease.org/shows/jaku-chara-tomozaki-kun) | TV | 14 / 12 | **Finished Airing** | 7.11 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jaku+Chara+Tomozaki+kun+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40530__jaku_chara_tomozaki_kun.txt) | ~4~ | 3832 | 2021-07-03 04:15 | | 40526 | [](https://myanimelist.net/anime/40526/Dragon_Ie_wo_Kau) | [Dragon, Ie wo Kau.](https://subsplease.org/shows/dragon-ie-wo-kau) | TV | 12 / 12 | **Finished Airing** | 6.42 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dragon+Ie+wo+Kau+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40526__dragon_ie_wo_kau.txt) | ~4~ | 1987 | 2021-06-20 14:02 | | 34566 | [](https://myanimelist.net/anime/34566/Boruto__Naruto_Next_Generations) | [Boruto - Naruto Next Generations](https://subsplease.org/shows/boruto-naruto-next-generations) | TV | 52 / 293 | **Finished Airing** | 6.0 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Boruto+Naruto+Next+Generations+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/34566__boruto_naruto_next_generations.txt) | ~4~ | 3032 | 2023-03-26 09:04 | | 50060 | [](https://myanimelist.net/anime/50060/Shadowverse_Flame) | [Shadowverse Flame](https://subsplease.org/shows/shadowverse-flame) | TV | 52 / 50 | **Finished Airing** | 6.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shadowverse+Flame+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50060__shadowverse_flame.txt) | ~4~ | 970 | 2024-09-28 02:32 | | 49969 | [](https://myanimelist.net/anime/49969/Tribe_Nine) | [Tribe Nine](https://subsplease.org/shows/tribe-nine) | TV | 12 / 12 | **Finished Airing** | 6.06 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tribe+Nine+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49969__tribe_nine.txt) | ~4~ | 1484 | 2022-03-28 13:31 | | 48406 | [](https://myanimelist.net/anime/48406/Re-Main) | [Re-Main](https://subsplease.org/shows/re-main) | TV | 12 / 12 | **Finished Airing** | 7.19 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Re+Main+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48406__re_main.txt) | ~4~ | 1675 | 2021-10-02 17:32 | | 44516 | [](https://myanimelist.net/anime/44516/Koroshi_Ai) | [Koroshi Ai](https://subsplease.org/shows/koroshi-ai) | TV | 12 / 12 | **Finished Airing** | 6.9 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Koroshi+Ai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44516__koroshi_ai.txt) | ~4~ | 2770 | 2022-03-30 14:46 | | 41899 | [](https://myanimelist.net/anime/41899/Ore_dake_Haireru_Kakushi_Dungeon) | [Ore dake Haireru Kakushi Dungeon](https://subsplease.org/shows/ore-dake-haireru-kakushi-dungeon) | TV | 12 / 12 | **Finished Airing** | 6.28 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ore+dake+Haireru+Kakushi+Dungeon+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41899__ore_dake_haireru_kakushi_dungeon.txt) | ~4~ | 5339 | 2021-03-26 18:27 | | 41312 | [](https://myanimelist.net/anime/41312/Kami-tachi_ni_Hirowareta_Otoko) | [Kami-tachi ni Hirowareta Otoko](https://subsplease.org/shows/kami-tachi-ni-hirowareta-otoko) | TV | 12 / 12 | **Finished Airing** | 6.97 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kami+tachi+ni+Hirowareta+Otoko+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41312__kami_tachi_ni_hirowareta_otoko.txt) | ~4~ | 2785 | 2020-12-20 15:01 | | 40960 | [](https://myanimelist.net/anime/40960/Cheat_Kusushi_no_Slow_Life__Isekai_ni_Tsukurou_Drugstore) | [Cheat Kusushi no Slow Life - Isekai ni Tsukurou Drugstore](https://subsplease.org/shows/cheat-kusushi-no-slow-life-isekai-ni-tsukurou-drugstore) | TV | 12 / 12 | **Finished Airing** | 6.45 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cheat+Kusushi+no+Slow+Life+Isekai+ni+Tsukurou+Drugstore+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40960__cheat_kusushi_no_slow_life_isekai_ni_tsukurou_drugstore.txt) | ~4~ | 3597 | 2021-09-22 14:02 | | 54638 | [](https://myanimelist.net/anime/54638/Kawagoe_Boys_Sing) | [Kawagoe Boys Sing](https://subsplease.org/shows/kawagoe-boys-sing) | TV | 12 / 12 | **Finished Airing** | 5.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kawagoe+Boys+Sing+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54638__kawagoe_boys_sing.txt) | ~3~ | 1139 | 2024-01-16 07:49 | | 54142 | [](https://myanimelist.net/anime/54142/Cardfight_Vanguard__Divinez) | [Cardfight!! Vanguard - Divinez](https://subsplease.org/shows/cardfight-vanguard-divinez) | TV | 13 / 13 | **Finished Airing** | 7.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cardfight+Vanguard+Divinez+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54142__cardfight_vanguard_divinez.txt) | ~3~ | 890 | 2024-04-19 23:42 | | 53748 | [](https://myanimelist.net/anime/53748/Saint_Seiya__Knights_of_the_Zodiac_-_Battle_Sanctuary_Part_2) | [Knights of the Zodiac - Saint Seiya S2 Part 2](https://subsplease.org/shows/knights-of-the-zodiac-saint-seiya-s2-part-2) | ONA | 12 / 12 | **Finished Airing** | 6.47 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Knights+of+the+Zodiac+Saint+Seiya+S2+Part+2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53748__saint_seiya_knights_of_the_zodiac_battle_sanctuary_part_2.txt) | ~3~ | 1190 | 2024-06-12 03:26 | | 53012 | [](https://myanimelist.net/anime/53012/Human_Bug_Daigaku) | [Human Bug Daigaku](https://subsplease.org/shows/human-bug-daigaku) | TV | 12 / 12 | **Finished Airing** | 6.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Human+Bug+Daigaku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53012__human_bug_daigaku.txt) | ~3~ | 1172 | 2022-12-21 14:31 | | 52614 | [](https://myanimelist.net/anime/52614/Mix__Meisei_Story_2nd_Season_-_Nidome_no_Natsu_Sora_no_Mukou_e) | [Mix - Meisei Story S2](https://subsplease.org/shows/mix-meisei-story-s2) | TV | 24 / 24 | **Finished Airing** | 6.97 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mix+Meisei+Story+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52614__mix_meisei_story_2nd_season_nidome_no_natsu_sora_no_mukou_e.txt) | ~3~ | 1564 | 2023-09-23 10:01 | | 51415 | [](https://myanimelist.net/anime/51415/OpusCOLORs) | [Opus.COLORs](https://subsplease.org/shows/opus-colors) | TV | 12 / 12 | **Finished Airing** | 5.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Opus+COLORs+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51415__opus_colors.txt) | ~3~ | 1080 | 2023-06-22 16:01 | | 51371 | [](https://myanimelist.net/anime/51371/Bucchigire) | [Bucchigire!](https://subsplease.org/shows/bucchigire) | TV | 12 / 12 | **Finished Airing** | 6.0 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bucchigire+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51371__bucchigire.txt) | ~3~ | 1634 | 2022-09-24 17:25 | | 51092 | [](https://myanimelist.net/anime/51092/Yuurei_Deco) | [Yurei Deco](https://subsplease.org/shows/yurei-deco) | TV | 12 / 12 | **Finished Airing** | 6.03 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yurei+Deco+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51092__yuurei_deco.txt) | ~3~ | 1429 | 2022-09-18 15:31 | | 50999 | [](https://myanimelist.net/anime/50999/Extreme_Hearts) | [Extreme Hearts](https://subsplease.org/shows/extreme-hearts) | TV | 12 / 12 | **Finished Airing** | 6.53 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Extreme+Hearts+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50999__extreme_hearts.txt) | ~3~ | 1206 | 2022-09-24 17:31 | | 50955 | [](https://myanimelist.net/anime/50955/Onipan) | [Onipan!](https://subsplease.org/shows/onipan) | TV | 12 / 60 | **Finished Airing** | 6.47 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Onipan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50955__onipan.txt) | ~3~ | 1591 | 2022-07-01 03:01 | | 50552 | [](https://myanimelist.net/anime/50552/Yowamushi_Pedal__Limit_Break) | [Yowamushi Pedal S5](https://subsplease.org/shows/yowamushi-pedal-s5) | TV | 25 / 25 | **Finished Airing** | 7.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yowamushi+Pedal+S5+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50552__yowamushi_pedal_limit_break.txt) | ~3~ | 1616 | 2023-03-25 22:09 | | 50438 | [](https://myanimelist.net/anime/50438/Yatogame-chan_Kansatsu_Nikki_Yonsatsume) | [Yatogame-chan Kansatsu Nikki S4](https://subsplease.org/shows/yatogame-chan-kansatsu-nikki-s4) | TV | 10 / 10 | **Finished Airing** | 6.3 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yatogame+chan+Kansatsu+Nikki+S4+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50438__yatogame_chan_kansatsu_nikki_yonsatsume.txt) | ~3~ | 1058 | 2022-06-11 12:30 | | 50281 | [](https://myanimelist.net/anime/50281/Delicious_Party♡Precure) | [Delicious Party Precure](https://subsplease.org/shows/delicious-party-precure) | TV | 45 / 45 | **Finished Airing** | 6.86 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Delicious+Party+Precure+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50281__delicious_party_precure.txt) | ~3~ | 741 | 2023-01-29 01:31 | | 50221 | [](https://myanimelist.net/anime/50221/Shine_Post) | [Shine Post](https://subsplease.org/shows/shine-post) | TV | 12 / 12 | **Finished Airing** | 7.49 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shine+Post+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50221__shine_post.txt) | ~3~ | 1746 | 2022-10-18 17:31 | | 50204 | [](https://myanimelist.net/anime/50204/Tokyo_24-ku) | [Tokyo 24-ku](https://subsplease.org/shows/tokyo-24-ku) | TV | 13 / 12 | **Finished Airing** | 6.34 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tokyo+24+ku+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50204__tokyo_24_ku.txt) | ~3~ | 1959 | 2022-04-06 17:01 | | 49514 | [](https://myanimelist.net/anime/49514/Gensou_Sangokushi__Tengen_Reishinki) | [Gensou Sangokushi - Tengen Reishinki](https://subsplease.org/shows/gensou-sangokushi-tengen-reishinki) | TV | 12 / 12 | **Finished Airing** | 5.31 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gensou+Sangokushi+Tengen+Reishinki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49514__gensou_sangokushi_tengen_reishinki.txt) | ~3~ | 1429 | 2022-03-28 18:02 | | 49304 | [](https://myanimelist.net/anime/49304/Seiken_Densetsu__Legend_of_Mana_-_The_Teardrop_Crystal) | [Seiken Densetsu - Legend of Mana - The Teardrop Crystal](https://subsplease.org/shows/seiken-densetsu-legend-of-mana-the-teardrop-crystal) | TV | 13 / 12 | **Finished Airing** | 5.47 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Seiken+Densetsu+Legend+of+Mana+The+Teardrop+Crystal+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49304__seiken_densetsu_legend_of_mana_the_teardrop_crystal.txt) | ~3~ | 1787 | 2023-02-20 00:36 | | 49184 | [](https://myanimelist.net/anime/49184/Gunma-chan) | [Gunma-chan](https://subsplease.org/shows/gunma-chan) | TV | 13 / 13 | **Finished Airing** | 5.95 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gunma+chan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49184__gunma_chan.txt) | ~3~ | 505 | 2023-05-17 05:02 | | 48779 | [](https://myanimelist.net/anime/48779/Deaimon) | [Deaimon](https://subsplease.org/shows/deaimon) | TV | 12 / 12 | **Finished Airing** | 7.55 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Deaimon+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48779__deaimon.txt) | ~3~ | 3300 | 2022-06-22 15:31 | | 48776 | [](https://myanimelist.net/anime/48776/Build_Divide__Code_Black) | [Build Divide - Code Black](https://subsplease.org/shows/build-divide-code-black) | TV | 12 / 12 | **Finished Airing** | 6.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Build+Divide+Code+Black+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48776__build_divide_code_black.txt) | ~3~ | 1460 | 2021-12-25 17:01 | | 48649 | [](https://myanimelist.net/anime/48649/Fuuto_Tantei) | [Fuuto Tantei](https://subsplease.org/shows/fuuto-tantei) | TV | 12 / 12 | **Finished Airing** | 7.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fuuto+Tantei+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48649__fuuto_tantei.txt) | ~3~ | 2303 | 2022-10-16 16:01 | | 48644 | [](https://myanimelist.net/anime/48644/Gyakuten_Sekai_no_Denchi_Shoujo) | [Gyakuten Sekai no Denchi Shoujo](https://subsplease.org/shows/gyakuten-sekai-no-denchi-shoujo) | TV | 12 / 12 | **Finished Airing** | 6.31 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gyakuten+Sekai+no+Denchi+Shoujo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48644__gyakuten_sekai_no_denchi_shoujo.txt) | ~3~ | 1620 | 2021-12-27 15:02 | | 48470 | [](https://myanimelist.net/anime/48470/D_Cide_Traumerei_the_Animation) | [D_Cide Traumerei the Animation](https://subsplease.org/shows/d_cide-traumerei-the-animation) | TV | 13 / 13 | **Finished Airing** | 5.81 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+D+Cide+Traumerei+the+Animation+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48470__d_cide_traumerei_the_animation.txt) | ~3~ | 1542 | 2021-10-02 15:01 | | 47639 | [](https://myanimelist.net/anime/47639/Blue_Reflection_Ray) | [Blue Reflection Ray](https://subsplease.org/shows/blue-reflection-ray) | TV | 24 / 24 | **Finished Airing** | 5.85 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Blue+Reflection+Ray+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47639__blue_reflection_ray.txt) | ~3~ | 1191 | 2021-09-24 17:57 | | 47250 | [](https://myanimelist.net/anime/47250/Jouran__The_Princess_of_Snow_and_Blood) | [Joran The Princess of Snow and Blood](https://subsplease.org/shows/joran-the-princess-of-snow-and-blood) | TV | 12 / 12 | **Finished Airing** | 6.11 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Joran+The+Princess+of+Snow+and+Blood+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/47250__jouran_the_princess_of_snow_and_blood.txt) | ~3~ | 1921 | 2021-06-15 17:17 | | 44940 | [](https://myanimelist.net/anime/44940/World_Trigger_3rd_Season) | [World Trigger S3](https://subsplease.org/shows/world-trigger-s3) | TV | 14 / 14 | **Finished Airing** | 8.25 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+World+Trigger+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44940__world_trigger_3rd_season.txt) | ~3~ | 3268 | 2022-01-22 18:32 | | 44275 | [](https://myanimelist.net/anime/44275/Selection_Project) | [Selection Project](https://subsplease.org/shows/selection-project) | TV | 13 / 13 | **Finished Airing** | 7.26 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Selection+Project+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44275__selection_project.txt) | ~3~ | 1540 | 2021-12-24 14:02 | | 44274 | [](https://myanimelist.net/anime/44274/Puraore_Pride_of_Orange) | [Puraore! Pride of Orange](https://subsplease.org/shows/puraore-pride-of-orange) | TV | 12 / 12 | **Finished Airing** | 6.24 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Puraore+Pride+of+Orange+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44274__puraore_pride_of_orange.txt) | ~3~ | 1344 | 2021-12-22 15:02 | | 43814 | [](https://myanimelist.net/anime/43814/Deatte_5-byou_de_Battle) | [Deatte 5-byou de Battle](https://subsplease.org/shows/deatte-5-byou-de-battle) | TV | 12 / 12 | **Finished Airing** | 6.76 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Deatte+5+byou+de+Battle+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43814__deatte_5_byou_de_battle.txt) | ~3~ | 3561 | 2021-09-27 17:32 | | 42627 | [](https://myanimelist.net/anime/42627/Peach_Boy_Riverside) | [Peach Boy Riverside](https://subsplease.org/shows/peach-boy-riverside) | TV | 12 / 12 | **Finished Airing** | 6.24 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Peach+Boy+Riverside+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42627__peach_boy_riverside.txt) | ~3~ | 4573 | 2021-09-16 14:32 | | 42590 | [](https://myanimelist.net/anime/42590/Mashiro_no_Oto) | [Mashiro no Oto](https://subsplease.org/shows/mashiro-no-oto) | TV | 12 / 12 | **Finished Airing** | 7.53 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mashiro+no+Oto+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42590__mashiro_no_oto.txt) | ~3~ | 2258 | 2021-06-18 18:46 | | 42506 | [](https://myanimelist.net/anime/42506/World_Witches_Hasshin_Shimasu) | [World Witches Hasshin Shimasu!](https://subsplease.org/shows/world-witches-hasshin-shimasu) | TV | 12 / 12 | **Finished Airing** | 6.49 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+World+Witches+Hasshin+Shimasu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42506__world_witches_hasshin_shimasu.txt) | ~3~ | 874 | 2021-03-31 17:49 | | 42307 | [](https://myanimelist.net/anime/42307/Subarashiki_Kono_Sekai_The_Animation) | [Subarashiki Kono Sekai The Animation](https://subsplease.org/shows/subarashiki-kono-sekai-the-animation) | TV | 12 / 12 | **Finished Airing** | 6.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Subarashiki+Kono+Sekai+The+Animation+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42307__subarashiki_kono_sekai_the_animation.txt) | ~3~ | 1971 | 2021-06-25 17:27 | | 42129 | [](https://myanimelist.net/anime/42129/Bem_Movie__Become_Human) | [Bem Movie - Become Human](https://subsplease.org/shows/bem-movie-become-human) | Movie | 1 / 1 | **Finished Airing** | 6.81 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bem+Movie+Become+Human+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42129__bem_movie_become_human.txt) | ~3~ | 1074 | 2020-10-30 00:43 | | 41833 | [](https://myanimelist.net/anime/41833/Kyuuketsuki_Sugu_Shinu) | [Kyuuketsuki Sugu Shinu](https://subsplease.org/shows/kyuuketsuki-sugu-shinu) | TV | 12 / 12 | **Finished Airing** | 7.27 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kyuuketsuki+Sugu+Shinu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41833__kyuuketsuki_sugu_shinu.txt) | ~3~ | 2399 | 2021-12-20 15:03 | | 41762 | [](https://myanimelist.net/anime/41762/Tenchi_Souzou_Design-bu) | [Tenchi Souzou Design-bu](https://subsplease.org/shows/tenchi-souzou-design-bu) | TV | 13 / 12 | **Finished Airing** | 7.16 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tenchi+Souzou+Design+bu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41762__tenchi_souzou_design_bu.txt) | ~3~ | 1653 | 2021-04-01 15:31 | | 41611 | [](https://myanimelist.net/anime/41611/Wan_Sheng_Jie) | [All Saints Street](https://subsplease.org/shows/all-saints-street) | ONA | 8 / 12 | **Finished Airing** | 7.97 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+All+Saints+Street+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41611__wan_sheng_jie.txt) | ~3~ | 1094 | 2023-10-03 08:49 | | 41265 | [](https://myanimelist.net/anime/41265/Mars_Red) | [Mars Red](https://subsplease.org/shows/mars-red) | TV | 13 / 13 | **Finished Airing** | 6.59 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mars+Red+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41265__mars_red.txt) | ~3~ | 2601 | 2021-06-28 18:02 | | 41169 | [](https://myanimelist.net/anime/41169/Love_Live_Superstar) | [Love Live! Superstar!!](https://subsplease.org/shows/love-live-superstar) | TV | 12 / 12 | **Finished Airing** | 7.93 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Love+Live+Superstar+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41169__love_live_superstar.txt) | ~3~ | 1831 | 2021-10-19 01:02 | | 41109 | [](https://myanimelist.net/anime/41109/Log_Horizon__Entaku_Houkai) | [Log Horizon S3](https://subsplease.org/shows/log-horizon-s3) | TV | 12 / 12 | **Finished Airing** | 7.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Log+Horizon+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41109__log_horizon_entaku_houkai.txt) | ~3~ | 6593 | 2021-03-31 12:02 | | 40870 | [](https://myanimelist.net/anime/40870/SSSSDynazenon) | [SSSS.Dynazenon](https://subsplease.org/shows/ssss-dynazenon) | TV | 12 / 12 | **Finished Airing** | 7.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+SSSS+Dynazenon+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40870__ssss_dynazenon.txt) | ~3~ | 3701 | 2021-06-18 14:02 | | 40729 | [](https://myanimelist.net/anime/40729/Nomad__Megalo_Box_2) | [Nomad - Megalo Box 2](https://subsplease.org/shows/nomad-megalo-box-2) | TV | 13 / 13 | **Finished Airing** | 8.18 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Nomad+Megalo+Box+2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40729__nomad_megalo_box_2.txt) | ~3~ | 4498 | 2021-06-27 15:02 | | 38959 | [](https://myanimelist.net/anime/38959/Lord_El-Melloi_II-sei_no_Jikenbo__Rail_Zeppelin_Grace_Note) | [Lord El-Melloi II Case Files](https://subsplease.org/shows/lord-el-melloi-ii-case-files) | TV | 1 / 13 | **Finished Airing** | 7.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Lord+El+Melloi+II+Case+Files+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38959__lord_el_melloi_ii_sei_no_jikenbo_rail_zeppelin_grace_note.txt) | ~3~ | 2306 | 2021-12-31 18:24 | | 34572 | [](https://myanimelist.net/anime/34572/Black_Clover) | [Black Clover](https://subsplease.org/shows/black-clover) | TV | 25 / 170 | **Finished Airing** | 8.14 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Black+Clover+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/34572__black_clover.txt) | ~3~ | 3687 | 2021-03-30 10:26 | | 52045 | [](https://myanimelist.net/anime/52045/Obey_Me_Season_2) | [Obey Me! S2](https://subsplease.org/shows/obey-me-s2) | ONA | 12 / 12 | **Finished Airing** | 7.1 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Obey+Me+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52045__obey_me_season_2.txt) | ~3~ | 738 | 2022-12-30 09:00 | | 50421 | [](https://myanimelist.net/anime/50421/Shi_Cao_Lao_Long_Bei_Guan_Yi_E_Long_Zhi_Ming) | [A Herbivorous Dragon of 5000 Years Gets Unfairly Villainized](https://subsplease.org/shows/a-herbivorous-dragon-of-5000-years-gets-unfairly-villainized) | ONA | 12 / 12 | **Finished Airing** | 6.74 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+A+Herbivorous+Dragon+of+5000+Years+Gets+Unfairly+Villainized+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50421__shi_cao_lao_long_bei_guan_yi_e_long_zhi_ming.txt) | ~3~ | 2180 | 2022-10-08 05:01 | | 43767 | [](https://myanimelist.net/anime/43767/Night_Head_2041) | [Night Head 2041](https://subsplease.org/shows/night-head-2041) | TV | 12 / 12 | **Finished Airing** | 6.5 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Night+Head+2041+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43767__night_head_2041.txt) | ~3~ | 1903 | 2021-09-29 18:02 | | 40750 | [](https://myanimelist.net/anime/40750/Kaifuku_Jutsushi_no_Yarinaoshi) | [Kaifuku Jutsushi no Yarinaoshi](https://subsplease.org/shows/kaifuku-jutsushi-no-yarinaoshi) | TV | 12 / 12 | **Finished Airing** | 6.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kaifuku+Jutsushi+no+Yarinaoshi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40750__kaifuku_jutsushi_no_yarinaoshi.txt) | ~3~ | 3989 | 2021-03-31 15:38 | | 38192 | [](https://myanimelist.net/anime/38192/Sakugan) | [Sakugan](https://subsplease.org/shows/sakugan) | TV | 12 / 12 | **Finished Airing** | 6.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sakugan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38192__sakugan.txt) | ~3~ | 4158 | 2021-12-23 15:31 | | 31433 | [](https://myanimelist.net/anime/31433/Ginga_Eiyuu_Densetsu__Die_Neue_These_-_Kaikou) | [Legend of the Galactic Heroes - Die Neue These](https://subsplease.org/shows/legend-of-the-galactic-heroes-die-neue-these) | TV | 24 / 12 | **Finished Airing** | 7.79 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Legend+of+the+Galactic+Heroes+Die+Neue+These+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/31433__ginga_eiyuu_densetsu_die_neue_these_kaikou.txt) | ~3~ | 2818 | 2022-12-16 00:01 | | 48590 | [](https://myanimelist.net/anime/48590/Mini_Dragon) | [Kobayashi-san Chi no Maid Dragon S2 Shorts](https://subsplease.org/shows/kobayashi-san-chi-no-maid-dragon-s2-shorts) | ONA | 16 / 13 | **Finished Airing** | 7.35 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kobayashi+san+Chi+no+Maid+Dragon+S2+Shorts+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48590__mini_dragon.txt) | ~2~ | 3326 | 2021-09-11 00:12 | | 48488 | [](https://myanimelist.net/anime/48488/Higurashi_no_Naku_Koro_ni_Sotsu) | [Higurashi no Naku Koro ni Sotsu](https://subsplease.org/shows/higurashi-no-naku-koro-ni-sotsu) | TV | 15 / 15 | **Finished Airing** | 6.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Higurashi+no+Naku+Koro+ni+Sotsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48488__higurashi_no_naku_koro_ni_sotsu.txt) | ~2~ | 3851 | 2021-09-30 15:32 | | 53414 | [](https://myanimelist.net/anime/53414/Lupin_Zero) | [Lupin Zero](https://subsplease.org/shows/lupin-zero) | ONA | 6 / 6 | **Finished Airing** | 7.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Lupin+Zero+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/53414__lupin_zero.txt) | ~2~ | 2200 | 2023-01-13 15:01 | | 50862 | [](https://myanimelist.net/anime/50862/Estab-Life__Great_Escape) | [Estab-Life - Great Escape](https://subsplease.org/shows/estab-life-great-escape) | TV | 12 / 12 | **Finished Airing** | 6.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Estab+Life+Great+Escape+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50862__estab_life_great_escape.txt) | ~2~ | 1333 | 2022-06-01 23:01 | | 49780 | [](https://myanimelist.net/anime/49780/Atasha_Kawashiri_Kodama_da_yo__Dangerous_Lifehacker_no_Tadareta_Seikatsu) | [Atasha Kawajiri Kodama Da yo](https://subsplease.org/shows/atasha-kawajiri-kodama-da-yo) | TV | 24 / 24 | **Finished Airing** | 5.72 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Atasha+Kawajiri+Kodama+Da+yo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49780__atasha_kawashiri_kodama_da_yo_dangerous_lifehacker_no_tadareta_seikatsu.txt) | ~2~ | 1066 | 2022-08-11 18:55 | | 49692 | [](https://myanimelist.net/anime/49692/Heroine_Tarumono_Kiraware_Heroine_to_Naisho_no_Oshigoto) | [Heroine Tarumono! Kiraware Heroine to Naisho no Oshigoto](https://subsplease.org/shows/heroine-tarumono-kiraware-heroine-to-naisho-no-oshigoto) | TV | 12 / 12 | **Finished Airing** | 7.27 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Heroine+Tarumono+Kiraware+Heroine+to+Naisho+no+Oshigoto+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49692__heroine_tarumono_kiraware_heroine_to_naisho_no_oshigoto.txt) | ~2~ | 1867 | 2022-06-23 15:03 | | 49040 | [](https://myanimelist.net/anime/49040/Lupin_III__Part_6) | [Lupin III - Part 6](https://subsplease.org/shows/lupin-iii-part-6) | TV | 25 / 24 | **Finished Airing** | 7.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Lupin+III+Part+6+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49040__lupin_iii_part_6.txt) | ~2~ | 2645 | 2022-03-26 17:31 | | 48777 | [](https://myanimelist.net/anime/48777/Build_Divide__Code_White) | [Build Divide - Code White](https://subsplease.org/shows/build-divide-code-white) | TV | 12 / 12 | **Finished Airing** | 6.64 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Build+Divide+Code+White+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48777__build_divide_code_white.txt) | ~2~ | 880 | 2022-06-25 17:01 | | 48775 | [](https://myanimelist.net/anime/48775/Kaginado) | [Kaginado](https://subsplease.org/shows/kaginado) | TV | 24 / 12 | **Finished Airing** | 7.12 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kaginado+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48775__kaginado.txt) | ~2~ | 1218 | 2022-06-28 16:00 | | 48702 | [](https://myanimelist.net/anime/48702/Dance_Dance_Danseur) | [Dance Dance Danseur](https://subsplease.org/shows/dance-dance-danseur) | TV | 11 / 11 | **Finished Airing** | 7.82 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dance+Dance+Danseur+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48702__dance_dance_danseur.txt) | ~2~ | 1797 | 2022-06-17 18:47 | | 48567 | [](https://myanimelist.net/anime/48567/Visual_Prison) | [Visual Prison](https://subsplease.org/shows/visual-prison) | TV | 12 / 12 | **Finished Airing** | 6.71 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Visual+Prison+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48567__visual_prison.txt) | ~2~ | 984 | 2021-12-24 16:32 | | 48492 | [](https://myanimelist.net/anime/48492/Scarlet_Nexus) | [Scarlet Nexus](https://subsplease.org/shows/scarlet-nexus) | TV | 26 / 26 | **Finished Airing** | 5.71 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Scarlet+Nexus+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48492__scarlet_nexus.txt) | ~2~ | 2543 | 2021-12-23 13:32 | | 48466 | [](https://myanimelist.net/anime/48466/Kyoukai_Senki) | [Kyoukai Senki](https://subsplease.org/shows/kyoukai-senki) | TV | 25 / 13 | **Finished Airing** | 6.32 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kyoukai+Senki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48466__kyoukai_senki.txt) | ~2~ | 2255 | 2022-06-27 16:01 | | 45665 | [](https://myanimelist.net/anime/45665/Fairy_Ranmaru__Anata_no_Kokoro_Otasuke_Shimasu) | [Fairy Ranmaru](https://subsplease.org/shows/fairy-ranmaru) | TV | 12 / 12 | **Finished Airing** | 5.9 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fairy+Ranmaru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/45665__fairy_ranmaru_anata_no_kokoro_otasuke_shimasu.txt) | ~2~ | 508 | 2021-06-24 15:02 | | 44081 | [](https://myanimelist.net/anime/44081/B-Project__NetsuretsuLove_Call) | [B-Project S3](https://subsplease.org/shows/b-project-s3) | TV | 12 / 12 | **Finished Airing** | 6.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+B+Project+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44081__b_project_netsuretsu_love_call.txt) | ~2~ | 864 | 2023-12-18 18:45 | | 44055 | [](https://myanimelist.net/anime/44055/Sasaki_to_Miyano) | [Sasaki to Miyano](https://subsplease.org/shows/sasaki-to-miyano) | TV | 13 / 12 | **Finished Airing** | 8.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sasaki+to+Miyano+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44055__sasaki_to_miyano.txt) | ~2~ | 1587 | 2022-07-27 04:39 | | 43763 | [](https://myanimelist.net/anime/43763/Cestvs__The_Roman_Fighter) | [Cestvs - The Roman Fighter](https://subsplease.org/shows/cestvs-the-roman-fighter) | TV | 11 / 11 | **Finished Airing** | 5.96 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cestvs+The+Roman+Fighter+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43763__cestvs_the_roman_fighter.txt) | ~2~ | 928 | 2021-06-23 18:42 | | 43756 | [](https://myanimelist.net/anime/43756/Bakuten) | [Bakuten!!](https://subsplease.org/shows/bakuten) | TV | 12 / 12 | **Finished Airing** | 7.49 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bakuten+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43756__bakuten.txt) | ~2~ | 778 | 2021-06-24 18:16 | | 43741 | [](https://myanimelist.net/anime/43741/Getter_Robo_Arc) | [Getter Robo Arc](https://subsplease.org/shows/getter-robo-arc) | TV | 13 / 13 | **Finished Airing** | 6.18 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Getter+Robo+Arc+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43741__getter_robo_arc.txt) | ~2~ | 1257 | 2021-09-26 12:37 | | 42544 | [](https://myanimelist.net/anime/42544/Kaizoku_Oujo) | [Kaizoku Oujo](https://subsplease.org/shows/kaizoku-oujo) | TV | 12 / 12 | **Finished Airing** | 7.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kaizoku+Oujo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42544__kaizoku_oujo.txt) | ~2~ | 4304 | 2021-10-24 04:06 | | 42395 | [](https://myanimelist.net/anime/42395/Shakunetsu_Kabaddi) | [Shakunetsu Kabaddi](https://subsplease.org/shows/shakunetsu-kabaddi) | TV | 12 / 12 | **Finished Airing** | 7.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shakunetsu+Kabaddi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42395__shakunetsu_kabaddi.txt) | ~2~ | 1220 | 2021-06-18 18:01 | | 42321 | [](https://myanimelist.net/anime/42321/Battle_Athletess_Daiundoukai_ReSTART) | [Battle Athletess Daiundoukai ReSTART!](https://subsplease.org/shows/battle-athletess-daiundoukai-restart) | TV | 12 / 12 | **Finished Airing** | 5.11 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Battle+Athletess+Daiundoukai+ReSTART+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42321__battle_athletess_daiundoukai_restart.txt) | ~2~ | 742 | 2021-06-26 16:32 | | 41946 | [](https://myanimelist.net/anime/41946/Shuumatsu_no_Harem) | [Shuumatsu no Harem](https://subsplease.org/shows/shuumatsu-no-harem) | TV | 11 / 11 | **Finished Airing** | 5.91 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shuumatsu+no+Harem+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41946__shuumatsu_no_harem.txt) | ~2~ | 2477 | 2022-03-18 17:31 | | 41915 | [](https://myanimelist.net/anime/41915/Zuihou_de_Zhaohuan_Shi) | [The Last Summoner](https://subsplease.org/shows/the-last-summoner) | ONA | 12 / 12 | **Finished Airing** | 6.55 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+Last+Summoner+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41915__zuihou_de_zhaohuan_shi.txt) | ~2~ | 2254 | 2022-07-05 05:01 | | 41619 | [](https://myanimelist.net/anime/41619/Munou_na_Nana) | [Munou na Nana](https://subsplease.org/shows/munou-na-nana) | TV | 13 / 13 | **Finished Airing** | 7.17 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Munou+na+Nana+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41619__munou_na_nana.txt) | ~2~ | 3106 | 2020-12-27 13:31 | | 41380 | [](https://myanimelist.net/anime/41380/100-man_no_Inochi_no_Ue_ni_Ore_wa_Tatteiru) | [100-man no Inochi no Ue ni Ore wa Tatte Iru](https://subsplease.org/shows/100-man-no-inochi-no-ue-ni-ore-wa-tatte-iru) | TV | 24 / 12 | **Finished Airing** | 6.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+100+man+no+Inochi+no+Ue+ni+Ore+wa+Tatte+Iru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41380__100_man_no_inochi_no_ue_ni_ore_wa_tatteiru.txt) | ~2~ | 3309 | 2021-09-24 16:32 | | 41074 | [](https://myanimelist.net/anime/41074/Digimon_Adventure_) | [Digimon Adventure (2020)](https://subsplease.org/shows/digimon-adventure-2020) | TV | 50 / 67 | **Finished Airing** | 6.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Digimon+Adventure+2020+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41074__digimon_adventure.txt) | ~2~ | 1222 | 2021-09-26 02:32 | | 41006 | [](https://myanimelist.net/anime/41006/Higurashi_no_Naku_Koro_ni_Gou) | [Higurashi no Naku Koro ni Gou](https://subsplease.org/shows/higurashi-no-naku-koro-ni-gou) | TV | 24 / 24 | **Finished Airing** | 7.2 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Higurashi+no+Naku+Koro+ni+Gou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41006__higurashi_no_naku_koro_ni_gou.txt) | ~2~ | 3938 | 2021-03-18 16:32 | | 40961 | [](https://myanimelist.net/anime/40961/Hortensia_Saga) | [Hortensia Saga](https://subsplease.org/shows/hortensia-saga) | TV | 12 / 12 | **Finished Airing** | 5.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hortensia+Saga+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40961__hortensia_saga.txt) | ~2~ | 2100 | 2021-03-24 19:49 | | 40930 | [](https://myanimelist.net/anime/40930/Azur_Lane__Bisoku_Zenshin) | [Azur Lane - Bisoku Zenshin!](https://subsplease.org/shows/azur-lane-bisoku-zenshin) | TV | 12 / 12 | **Finished Airing** | 7.01 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Azur+Lane+Bisoku+Zenshin+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40930__azur_lane_bisoku_zenshin.txt) | ~2~ | 1869 | 2021-03-29 17:00 | | 40908 | [](https://myanimelist.net/anime/40908/Kemono_Jihen) | [Kemono Jihen](https://subsplease.org/shows/kemono-jihen) | TV | 12 / 12 | **Finished Airing** | 7.37 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kemono+Jihen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40908__kemono_jihen.txt) | ~2~ | 4061 | 2021-03-28 13:31 | | 40879 | [](https://myanimelist.net/anime/40879/Love_Live_Nijigasaki_Gakuen_School_Idol_Doukoukai) | [Love Live! Nijigasaki Gakuen School Idol Doukoukai](https://subsplease.org/shows/love-live-nijigasaki-gakuen-school-idol-doukoukai) | TV | 13 / 13 | **Finished Airing** | 7.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Love+Live+Nijigasaki+Gakuen+School+Idol+Doukoukai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40879__love_live_nijigasaki_gakuen_school_idol_doukoukai.txt) | ~2~ | 1629 | 2020-12-26 14:06 | | 40682 | [](https://myanimelist.net/anime/40682/Kingdom_3rd_Season) | [Kingdom S3](https://subsplease.org/shows/kingdom-s3) | TV | 26 / 26 | **Finished Airing** | 8.83 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kingdom+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40682__kingdom_3rd_season.txt) | ~2~ | 1883 | 2021-10-17 18:41 | | 40646 | [](https://myanimelist.net/anime/40646/Yes_ka_No_ka_Hanbun_ka) | [Yes ka No ka Hanbun ka](https://subsplease.org/shows/yes-ka-no-ka-hanbun-ka) | Movie | 1 / 1 | **Finished Airing** | 7.06 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yes+ka+No+ka+Hanbun+ka+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40646__yes_ka_no_ka_hanbun_ka.txt) | ~2~ | 746 | 2021-04-30 16:37 | | 40497 | [](https://myanimelist.net/anime/40497/Mahouka_Koukou_no_Rettousei__Raihousha-hen) | [Mahouka Koukou no Rettousei S2](https://subsplease.org/shows/mahouka-koukou-no-rettousei-s2) | TV | 13 / 13 | **Finished Airing** | 7.27 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Mahouka+Koukou+no+Rettousei+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40497__mahouka_koukou_no_rettousei_raihousha_hen.txt) | ~2~ | 5020 | 2020-12-26 17:01 | | 40085 | [](https://myanimelist.net/anime/40085/Maesetsu) | [Maesetsu!](https://subsplease.org/shows/maesetsu) | TV | 12 / 12 | **Finished Airing** | 5.8 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Maesetsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40085__maesetsu.txt) | ~2~ | 680 | 2020-12-27 15:31 | | 39893 | [](https://myanimelist.net/anime/39893/Muteking_the_Dancing_Hero) | [Muteking the Dancing Hero](https://subsplease.org/shows/muteking-the-dancing-hero) | TV | 12 / 12 | **Finished Airing** | 5.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Muteking+the+Dancing+Hero+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39893__muteking_the_dancing_hero.txt) | ~2~ | 827 | 2021-12-18 17:31 | | 39469 | [](https://myanimelist.net/anime/39469/Tsugu_Tsugumomo) | [Tsugumomo S2 OVA](https://subsplease.org/shows/tsugumomo-s2) | TV | 1 / 12 | **Finished Airing** | 7.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tsugumomo+S2+OVA+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39469__tsugu_tsugumomo.txt) | ~2~ | 952 | 2020-11-06 00:54 | | 38749 | [](https://myanimelist.net/anime/38749/Blade_Runner__Black_Lotus) | [Blade Runner - Black Lotus](https://subsplease.org/shows/blade-runner-black-lotus) | TV | 13 / 13 | **Finished Airing** | 6.29 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Blade+Runner+Black+Lotus+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38749__blade_runner_black_lotus.txt) | ~2~ | 2258 | 2022-02-06 05:01 | | 38476 | [](https://myanimelist.net/anime/38476/Heya_Camp△) | [Heya Camp](https://subsplease.org/shows/heya-camp) | TV | 1 / 12 | **Finished Airing** | 7.36 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Heya+Camp+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38476__heya_camp.txt) | ~2~ | 1289 | 2021-02-25 18:55 | | 38091 | [](https://myanimelist.net/anime/38091/Hachigatsu_no_Cinderella_Nine) | [Hachigatsu no Cinderella Nine](https://subsplease.org/shows/hachigatsu-no-cinderella-nine) | TV | 1 / 12 | **Finished Airing** | 6.12 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hachigatsu+no+Cinderella+Nine+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38091__hachigatsu_no_cinderella_nine.txt) | ~2~ | 541 | 2021-10-01 04:17 | | 35335 | [](https://myanimelist.net/anime/35335/Musashino) | [Musashino!](https://subsplease.org/shows/musashino) | TV | 12 / 12 | **Finished Airing** | 4.65 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Musashino+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/35335__musashino.txt) | ~2~ | 1010 | 2022-09-17 15:30 | | 32455 | [](https://myanimelist.net/anime/32455/Gekidol__Actidol_Project) | [Gekidol](https://subsplease.org/shows/gekidol) | TV | 13 / 12 | **Finished Airing** | 5.51 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gekidol+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/32455__gekidol_actidol_project.txt) | ~2~ | 930 | 2021-03-23 12:31 | | 44069 | [](https://myanimelist.net/anime/44069/Xian_Wang_de_Richang_Shenghuo_2) | [The Daily Life of the Immortal King S2](https://subsplease.org/shows/the-daily-life-of-the-immortal-king-s2) | ONA | 12 / 12 | **Finished Airing** | 7.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+Daily+Life+of+the+Immortal+King+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44069__xian_wang_de_richang_shenghuo_2.txt) | ~2~ | 2409 | 2022-01-08 03:03 | | 41462 | [](https://myanimelist.net/anime/41462/BanG_Dream_Film_Live_2nd_Stage) | [BanG Dream! Film Live 2nd Stage](https://subsplease.org/shows/bang-dream-film-live-2nd-stage) | Movie | 4 / 1 | **Finished Airing** | 7.88 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+BanG+Dream+Film+Live+2nd+Stage+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41462__bang_dream_film_live_2nd_stage.txt) | ~2~ | 897 | 2022-08-07 21:50 | | 40911 | [](https://myanimelist.net/anime/40911/Yuukoku_no_Moriarty) | [Yuukoku no Moriarty](https://subsplease.org/shows/yuukoku-no-moriarty) | TV | 24 / 11 | **Finished Airing** | 8.17 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yuukoku+no+Moriarty+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40911__yuukoku_no_moriarty.txt) | ~2~ | 2468 | 2021-06-27 15:43 | | 40842 | [](https://myanimelist.net/anime/40842/Idoly_Pride) | [Idoly Pride](https://subsplease.org/shows/idoly-pride) | TV | 12 / 12 | **Finished Airing** | 7.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Idoly+Pride+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40842__idoly_pride.txt) | ~2~ | 1009 | 2021-03-28 16:31 | | 40776 | [](https://myanimelist.net/anime/40776/Haikyuu_To_the_Top_Part_2) | [Haikyuu!! To The Top](https://subsplease.org/shows/haikyuu-to-the-top) | TV | 12 / 12 | **Finished Airing** | 8.55 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Haikyuu+To+The+Top+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40776__haikyuu_to_the_top_part_2.txt) | ~2~ | 3962 | 2020-12-18 19:52 | | 40550 | [](https://myanimelist.net/anime/40550/Assault_Lily__Bouquet) | [Assault Lily Bouquet](https://subsplease.org/shows/assault-lily-bouquet) | TV | 12 / 12 | **Finished Airing** | 6.5 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Assault+Lily+Bouquet+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40550__assault_lily_bouquet.txt) | ~2~ | 1878 | 2020-12-25 13:01 | | 38853 | [](https://myanimelist.net/anime/38853/Ex-Arm) | [Ex-Arm](https://subsplease.org/shows/ex-arm) | TV | 12 / 12 | **Finished Airing** | 2.88 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ex+Arm+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38853__ex_arm.txt) | ~2~ | 1566 | 2021-03-28 17:02 | | 50470 | [](https://myanimelist.net/anime/50470/Kami_Kuzu☆Idol) | [Kami Kuzu Idol](https://subsplease.org/shows/kami-kuzu-idol) | TV | 10 / 10 | **Finished Airing** | 6.75 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kami+Kuzu+Idol+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50470__kami_kuzu_idol.txt) | ~1~ | 1693 | 2022-09-02 23:04 | | 40956 | [](https://myanimelist.net/anime/40956/Enen_no_Shouboutai__Ni_no_Shou) | [Enen no Shouboutai S2](https://subsplease.org/shows/enen-no-shouboutai-s2) | TV | 10 / 24 | **Finished Airing** | 7.82 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Enen+no+Shouboutai+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40956__enen_no_shouboutai_ni_no_shou.txt) | ~1~ | 5606 | 2020-12-11 17:56 | | 54143 | [](https://myanimelist.net/anime/54143/Cardfight_Vanguard__Divinez_Season_2) | [Cardfight!! Vanguard - Divinez S2](https://subsplease.org/shows/cardfight-vanguard-divinez-s2) | TV | 13 / 13 | **Finished Airing** | 7.09 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cardfight+Vanguard+Divinez+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/54143__cardfight_vanguard_divinez_season_2.txt) | ~1~ | 756 | 2024-10-11 23:42 | | 52079 | [](https://myanimelist.net/anime/52079/Cardfight_Vanguard__will_Dress_Season_3) | [Cardfight!! Vanguard will+Dress S3](https://subsplease.org/shows/cardfight-vanguard-willdress-s3) | TV | 13 / 13 | **Finished Airing** | 6.57 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cardfight+Vanguard+will+Dress+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52079__cardfight_vanguard_will_dress_season_3.txt) | ~1~ | 684 | 2023-10-06 23:41 | | 50985 | [](https://myanimelist.net/anime/50985/Chimimo) | [Chimimo](https://subsplease.org/shows/chimimo) | TV | 12 / 12 | **Finished Airing** | 6.91 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Chimimo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50985__chimimo.txt) | ~1~ | 994 | 2022-09-22 17:33 | | 50599 | [](https://myanimelist.net/anime/50599/Yami_Shibai_10) | [Yami Shibai 10](https://subsplease.org/shows/yami-shibai-10) | TV | 13 / 13 | **Finished Airing** | 6.18 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yami+Shibai+10+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50599__yami_shibai_10.txt) | ~1~ | 642 | 2022-04-03 19:30 | | 50379 | [](https://myanimelist.net/anime/50379/Shoot_Goal_to_the_Future) | [Shoot! Goal to the Future](https://subsplease.org/shows/shoot-goal-to-the-future) | TV | 13 / 13 | **Finished Airing** | 5.28 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shoot+Goal+to+the+Future+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50379__shoot_goal_to_the_future.txt) | ~1~ | 822 | 2022-09-24 15:01 | | 50185 | [](https://myanimelist.net/anime/50185/Rymans_Club) | [Ryman's Club](https://subsplease.org/shows/rymans-club) | TV | 12 / 12 | **Finished Airing** | 7.35 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ryman+s+Club+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50185__ryman_s_club.txt) | ~1~ | 1390 | 2022-04-16 18:31 | | 50099 | [](https://myanimelist.net/anime/50099/Shin_Tennis_no_Oujisama__U-17_World_Cup) | [The Prince of Tennis II - U-17 World Cup](https://subsplease.org/shows/the-prince-of-tennis-ii-u-17-world-cup) | TV | 13 / 13 | **Finished Airing** | 6.77 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+Prince+of+Tennis+II+U+17+World+Cup+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50099__shin_tennis_no_oujisama_u_17_world_cup.txt) | ~1~ | 1150 | 2022-09-28 16:01 | | 49820 | [](https://myanimelist.net/anime/49820/Cardfight_Vanguard__will_Dress_Season_2) | [Cardfight!! Vanguard will+Dress S2](https://subsplease.org/shows/cardfight-vanguard-willdress-s2) | TV | 12 / 12 | **Finished Airing** | 6.71 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cardfight+Vanguard+will+Dress+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49820__cardfight_vanguard_will_dress_season_2.txt) | ~1~ | 552 | 2023-03-31 23:41 | | 49691 | [](https://myanimelist.net/anime/49691/Gunjou_no_Fanfare) | [Gunjou no Fanfare](https://subsplease.org/shows/gunjou-no-fanfare) | TV | 13 / 13 | **Finished Airing** | 6.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gunjou+no+Fanfare+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49691__gunjou_no_fanfare.txt) | ~1~ | 858 | 2022-06-25 16:01 | | 49551 | [](https://myanimelist.net/anime/49551/Hanabi-chan_wa_Okuregachi) | [Hanabi-chan wa Okuregachi](https://subsplease.org/shows/hanabi-chan-wa-okuregachi) | TV | 12 / 12 | **Finished Airing** | 6.14 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hanabi+chan+wa+Okuregachi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49551__hanabi_chan_wa_okuregachi.txt) | ~1~ | 907 | 2022-09-25 14:00 | | 49522 | [](https://myanimelist.net/anime/49522/Toutotsu_ni_Egypt_Shin_2) | [Toutotsu ni Egypt Shin S2](https://subsplease.org/shows/toutotsu-ni-egypt-shin-s2) | ONA | 10 / 10 | **Finished Airing** | 6.62 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Toutotsu+ni+Egypt+Shin+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49522__toutotsu_ni_egypt_shin_2.txt) | ~1~ | 804 | 2023-03-14 17:00 | | 49292 | [](https://myanimelist.net/anime/49292/Deep_Insanity__The_Lost_Child) | [Deep Insanity - The Lost Child](https://subsplease.org/shows/deep-insanity-the-lost-child) | TV | 12 / 12 | **Finished Airing** | 5.6 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Deep+Insanity+The+Lost+Child+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49292__deep_insanity_the_lost_child.txt) | ~1~ | 1887 | 2021-12-28 16:33 | | 49285 | [](https://myanimelist.net/anime/49285/Waccha_PriMagi) | [Waccha PriMagi!](https://subsplease.org/shows/waccha-primagi) | TV | 51 / 51 | **Finished Airing** | 7.02 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Waccha+PriMagi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49285__waccha_primagi.txt) | ~1~ | 699 | 2022-10-09 02:02 | | 45783 | [](https://myanimelist.net/anime/45783/Saiyuuki_Reload__Zeroin) | [Saiyuuki Reload - Zeroin](https://subsplease.org/shows/saiyuuki-reload-zeroin) | TV | 13 / 13 | **Finished Airing** | 6.66 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Saiyuuki+Reload+Zeroin+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/45783__saiyuuki_reload_zeroin.txt) | ~1~ | 1383 | 2022-03-31 15:05 | | 45577 | [](https://myanimelist.net/anime/45577/IDOLiSH7_Third_Beat) | [IDOLiSH7 S3](https://subsplease.org/shows/idolish7-s3) | TV | 30 / 13 | **Finished Airing** | 8.23 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+IDOLiSH7+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/45577__idolish7_third_beat.txt) | ~1~ | 534 | 2023-02-26 16:02 | | 44387 | [](https://myanimelist.net/anime/44387/Sankaku_Mado_no_Sotogawa_wa_Yoru) | [Sankaku Mado no Sotogawa wa Yoru](https://subsplease.org/shows/sankaku-mado-no-sotogawa-wa-yoru) | TV | 12 / 12 | **Finished Airing** | 6.75 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sankaku+Mado+no+Sotogawa+wa+Yoru+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44387__sankaku_mado_no_sotogawa_wa_yoru.txt) | ~1~ | 1131 | 2021-12-19 14:31 | | 44191 | [](https://myanimelist.net/anime/44191/Tropical-Rouge_Precure) | [Tropical-Rouge! Precure](https://subsplease.org/shows/tropical-rouge-precure) | TV | 46 / 46 | **Finished Airing** | 7.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tropical+Rouge+Precure+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44191__tropical_rouge_precure.txt) | ~1~ | 582 | 2022-01-30 01:31 | | 43771 | [](https://myanimelist.net/anime/43771/Vazzrock_The_Animation) | [Vazzrock the Animation](https://subsplease.org/shows/vazzrock-the-animation) | TV | 13 / 13 | **Finished Airing** | 5.44 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Vazzrock+the+Animation+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43771__vazzrock_the_animation.txt) | ~1~ | 584 | 2022-12-27 15:31 | | 43591 | [](https://myanimelist.net/anime/43591/Hetalia_World★Stars) | [Hetalia World Stars](https://subsplease.org/shows/hetalia-world-stars) | ONA | 12 / 12 | **Finished Airing** | 6.86 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hetalia+World+Stars+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43591__hetalia_world_stars.txt) | ~1~ | 551 | 2021-06-16 16:01 | | 42981 | [](https://myanimelist.net/anime/42981/Idolls) | [Idolls!](https://subsplease.org/shows/idolls) | TV | 10 / 10 | **Finished Airing** | 5.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Idolls+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42981__idolls.txt) | ~1~ | 511 | 2021-03-12 16:00 | | 42959 | [](https://myanimelist.net/anime/42959/Yatogame-chan_Kansatsu_Nikki_Sansatsume) | [Yatogame-chan Kansatsu Nikki S3](https://subsplease.org/shows/yatogame-chan-kansatsu-nikki-s3) | TV | 12 / 12 | **Finished Airing** | 6.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yatogame+chan+Kansatsu+Nikki+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42959__yatogame_chan_kansatsu_nikki_sansatsume.txt) | ~1~ | 801 | 2021-03-28 13:30 | | 42892 | [](https://myanimelist.net/anime/42892/Baraou_no_Souretsu) | [Baraou no Souretsu](https://subsplease.org/shows/baraou-no-souretsu) | TV | 25 / 24 | **Finished Airing** | 6.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Baraou+no+Souretsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42892__baraou_no_souretsu.txt) | ~1~ | 1048 | 2022-06-26 14:02 | | 42822 | [](https://myanimelist.net/anime/42822/Kai_Byoui_Ramune) | [Kai Byoui Ramune](https://subsplease.org/shows/kai-byoui-ramune) | TV | 12 / 12 | **Finished Airing** | 7.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kai+Byoui+Ramune+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42822__kai_byoui_ramune.txt) | ~1~ | 1183 | 2021-03-27 17:01 | | 42808 | [](https://myanimelist.net/anime/42808/Shenmue_the_Animation) | [Shenmue the Animation](https://subsplease.org/shows/shenmue-the-animation) | TV | 13 / 13 | **Finished Airing** | 6.64 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shenmue+the+Animation+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42808__shenmue_the_animation.txt) | ~1~ | 1823 | 2022-05-01 04:01 | | 41917 | [](https://myanimelist.net/anime/41917/Min_Diao_Ju_Yi_Wen_Lu) | [Bureau of Paranormal Investigation](https://subsplease.org/shows/bureau-of-paranormal-investigation) | ONA | 12 / 12 | **Finished Airing** | 6.63 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bureau+of+Paranormal+Investigation+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41917__min_diao_ju_yi_wen_lu.txt) | ~1~ | 1316 | 2023-02-03 21:19 | | 41834 | [](https://myanimelist.net/anime/41834/Kings_Raid__Ishi_wo_Tsugumono-tachi) | [King's Raid - Ishi wo Tsugu Mono-tachi](https://subsplease.org/shows/kings-raid-ishi-wo-tsugu-mono-tachi) | TV | 26 / 26 | **Finished Airing** | 6.04 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+King+s+Raid+Ishi+wo+Tsugu+Mono+tachi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41834__king_s_raid_ishi_wo_tsugumono_tachi.txt) | ~1~ | 1444 | 2021-03-26 17:25 | | 41688 | [](https://myanimelist.net/anime/41688/Toutotsu_ni_Egypt_Shin) | [Toutotsu ni Egypt Kami](https://subsplease.org/shows/toutotsu-ni-egypt-shin) | ONA | 10 / 10 | **Finished Airing** | 6.6 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Toutotsu+ni+Egypt+Kami+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41688__toutotsu_ni_egypt_shin.txt) | ~1~ | 461 | 2021-02-08 04:00 | | 41556 | [](https://myanimelist.net/anime/41556/Maiko-san_Chi_no_Makanai-san) | [Maiko-san Chi no Makanai-san](https://subsplease.org/shows/maiko-san-chi-no-makanai-san) | TV | 12 / 12 | **Finished Airing** | 7.03 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Maiko+san+Chi+no+Makanai+san+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41556__maiko_san_chi_no_makanai_san.txt) | ~1~ | 954 | 2022-01-27 04:01 | | 41521 | [](https://myanimelist.net/anime/41521/WIXOSS_DivaALive) | [WIXOSS Diva(A)Live](https://subsplease.org/shows/wixoss-divaalive) | TV | 12 / 12 | **Finished Airing** | 5.6 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+WIXOSS+Diva+A+Live+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41521__wixoss_diva_a_live.txt) | ~1~ | 697 | 2021-03-26 16:32 | | 41433 | [](https://myanimelist.net/anime/41433/Akudama_Drive) | [Akudama Drive](https://subsplease.org/shows/akudama-drive) | TV | 12 / 12 | **Finished Airing** | 7.58 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Akudama+Drive+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41433__akudama_drive.txt) | ~1~ | 4599 | 2020-12-24 13:02 | | 41389 | [](https://myanimelist.net/anime/41389/Tonikaku_Kawaii) | [Tonikaku Kawaii](https://subsplease.org/shows/tonikaku-kawaii) | TV | 15 / 12 | **Finished Airing** | 7.86 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tonikaku+Kawaii+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41389__tonikaku_kawaii.txt) | ~1~ | 3347 | 2022-11-28 16:56 | | 40964 | [](https://myanimelist.net/anime/40964/Back_Arrow) | [Back Arrow](https://subsplease.org/shows/back-arrow) | TV | 24 / 24 | **Finished Airing** | 6.33 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Back+Arrow+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40964__back_arrow.txt) | ~1~ | 1728 | 2021-06-18 16:32 | | 40907 | [](https://myanimelist.net/anime/40907/World_Trigger_2nd_Season) | [World Trigger S2](https://subsplease.org/shows/world-trigger-s2) | TV | 12 / 12 | **Finished Airing** | 8.05 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+World+Trigger+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40907__world_trigger_2nd_season.txt) | ~1~ | 2644 | 2021-04-03 18:31 | | 40571 | [](https://myanimelist.net/anime/40571/Majo_no_Tabitabi) | [Majo no Tabitabi](https://subsplease.org/shows/majo-no-tabitabi) | TV | 12 / 12 | **Finished Airing** | 7.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Majo+no+Tabitabi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40571__majo_no_tabitabi.txt) | ~1~ | 4487 | 2020-12-18 13:02 | | 39790 | [](https://myanimelist.net/anime/39790/Adachi_to_Shimamura) | [Adachi to Shimamura](https://subsplease.org/shows/adachi-to-shimamura) | TV | 12 / 12 | **Finished Airing** | 7.09 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Adachi+to+Shimamura+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39790__adachi_to_shimamura.txt) | ~1~ | 2491 | 2020-12-24 17:39 | | 39681 | [](https://myanimelist.net/anime/39681/D4DJ_First_Mix) | [D4DJ First Mix](https://subsplease.org/shows/d4dj-first-mix) | TV | 13 / 13 | **Finished Airing** | 7.61 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+D4DJ+First+Mix+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39681__d4dj_first_mix.txt) | ~1~ | 817 | 2021-01-29 14:03 | | 37262 | [](https://myanimelist.net/anime/37262/Ta_ga_Tame_no_Alchemist) | [Ta ga Tame no Alchemist](https://subsplease.org/shows/ta-ga-tame-no-alchemist) | Movie | 1 / 1 | **Finished Airing** | 6.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ta+ga+Tame+no+Alchemist+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/37262__ta_ga_tame_no_alchemist.txt) | ~1~ | 1182 | 2021-02-05 00:18 | | 36458 | [](https://myanimelist.net/anime/36458/Soukou_Musume_Senki) | [Soukou Musume Senki](https://subsplease.org/shows/soukou-musume-senki) | TV | 12 / 12 | **Finished Airing** | 5.78 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Soukou+Musume+Senki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/36458__soukou_musume_senki.txt) | ~1~ | 1202 | 2021-03-24 19:36 | | 36028 | [](https://myanimelist.net/anime/36028/Golden_Kamuy) | [Golden Kamuy](https://subsplease.org/shows/golden-kamuy) | TV | 25 / 12 | **Finished Airing** | 7.88 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Golden+Kamuy+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/36028__golden_kamuy.txt) | ~1~ | 3775 | 2023-06-26 15:01 | | 52273 | [](https://myanimelist.net/anime/52273/Saint_Seiya__Knights_of_the_Zodiac_-_Battle_Sanctuary) | [Knights of the Zodiac - Saint Seiya S2](https://subsplease.org/shows/knights-of-the-zodiac-saint-seiya-s2) | ONA | 12 / 12 | **Finished Airing** | 6.25 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Knights+of+the+Zodiac+Saint+Seiya+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/52273__saint_seiya_knights_of_the_zodiac_battle_sanctuary.txt) | ~1~ | 656 | 2022-10-09 20:01 | | 41930 | [](https://myanimelist.net/anime/41930/Kamisama_ni_Natta_Hi) | [Kamisama ni Natta Hi](https://subsplease.org/shows/kamisama-ni-natta-hi) | TV | 12 / 12 | **Finished Airing** | 6.79 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kamisama+ni+Natta+Hi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41930__kamisama_ni_natta_hi.txt) | ~1~ | 3820 | 2020-12-26 16:31 | | 41364 | [](https://myanimelist.net/anime/41364/One_Room__Third_Season) | [One Room S3](https://subsplease.org/shows/one-room-s3) | TV | 12 / 12 | **Finished Airing** | 6.43 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+One+Room+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41364__one_room_third_season.txt) | ~1~ | 731 | 2020-12-21 18:15 | | 40752 | [](https://myanimelist.net/anime/40752/Bishounen_Tanteidan) | [Bishounen Tanteidan](https://subsplease.org/shows/bishounen-tanteidan) | TV | 12 / 12 | **Finished Airing** | 7.08 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bishounen+Tanteidan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40752__bishounen_tanteidan.txt) | ~1~ | 1428 | 2021-06-26 18:32 | | 37599 | [](https://myanimelist.net/anime/37599/Magatsu_Wahrheit__Zuerst) | [Magatsu Wahrheit - Zuerst](https://subsplease.org/shows/magatsu-wahrheit-zuerst) | TV | 12 / 12 | **Finished Airing** | 6.54 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Magatsu+Wahrheit+Zuerst+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/37599__magatsu_wahrheit_zuerst.txt) | ~1~ | 1136 | 2020-12-29 15:02 | | 41573 | [](https://myanimelist.net/anime/41573/Majutsushi_Orphen_Hagure_Tabi__Kimluck-hen) | [Majutsushi Orphen Hagure Tabi S2](https://subsplease.org/shows/majutsushi-orphen-hagure-tabi-s2) | TV | 11 / 11 | **Finished Airing** | 6.19 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Majutsushi+Orphen+Hagure+Tabi+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41573__majutsushi_orphen_hagure_tabi_kimluck_hen.txt) | ~0~ | 1266 | 2021-03-31 13:02 | | 51203 | [](https://myanimelist.net/anime/51203/Meng_Qi_Shi_Shen__Huanxi_Zhui_Hun) | [Cinderella Chef S3](https://subsplease.org/shows/cinderella-chef-s3) | ONA | 12 / 12 | **Finished Airing** | 7.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cinderella+Chef+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/51203__meng_qi_shi_shen_huanxi_zhui_hun.txt) | ~0~ | 553 | 2022-09-13 05:01 | | 50789 | [](https://myanimelist.net/anime/50789/Jantama_Pong☆) | [Jantama Pong](https://subsplease.org/shows/jantama-pong) | TV | 12 / 11 | **Finished Airing** | 6.08 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Jantama+Pong+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50789__jantama_pong.txt) | ~0~ | 1003 | 2022-06-17 17:00 | | 50537 | [](https://myanimelist.net/anime/50537/Bai_Yao_Pu_3rd_Season) | [Fairies Album S3](https://subsplease.org/shows/fairies-album-s3) | ONA | 12 / 12 | **Finished Airing** | 7.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Fairies+Album+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50537__bai_yao_pu_3rd_season.txt) | ~0~ | 472 | 2022-10-03 05:01 | | 50160 | [](https://myanimelist.net/anime/50160/Kingdom_4th_Season) | [Kingdom S4](https://subsplease.org/shows/kingdom-s4) | TV | 26 / 26 | **Finished Airing** | 8.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kingdom+S4+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50160__kingdom_4th_season.txt) | ~0~ | 2363 | 2022-10-01 18:31 | | 50021 | [](https://myanimelist.net/anime/50021/Dou_Shen_Ji) | [Ancient Girls Frame](https://subsplease.org/shows/ancient-girls-frame) | ONA | 12 / 12 | **Finished Airing** | 5.82 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ancient+Girls+Frame+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/50021__dou_shen_ji.txt) | ~0~ | 761 | 2021-12-29 04:26 | | 49819 | [](https://myanimelist.net/anime/49819/Cardfight_Vanguard__will_Dress) | [Cardfight!! Vanguard will+Dress](https://subsplease.org/shows/cardfight-vanguard-willdress) | TV | 13 / 13 | **Finished Airing** | 6.86 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cardfight+Vanguard+will+Dress+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49819__cardfight_vanguard_will_dress.txt) | ~0~ | 393 | 2022-09-26 15:41 | | 49556 | [](https://myanimelist.net/anime/49556/Love_All_Play) | [Love All Play](https://subsplease.org/shows/love-all-play) | TV | 24 / 24 | **Finished Airing** | 6.53 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Love+All+Play+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49556__love_all_play.txt) | ~0~ | 1002 | 2022-09-24 10:03 | | 49338 | [](https://myanimelist.net/anime/49338/Hakuouki_OVA_2021) | [Hakuouki OVA](https://subsplease.org/shows/hakuouki-ova) | OVA | 3 / 3 | **Finished Airing** | 7.07 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hakuouki+OVA+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49338__hakuouki_ova_2021.txt) | ~0~ | 895 | 2022-01-29 18:37 | | 49263 | [](https://myanimelist.net/anime/49263/Yaku_nara_Mug_Cup_mo__Niban_Gama) | [Yakunara Mug Cup mo S2](https://subsplease.org/shows/yakunara-mug-cup-mo-s2) | TV | 24 / 12 | **Finished Airing** | 6.83 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yakunara+Mug+Cup+mo+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49263__yaku_nara_mug_cup_mo_niban_gama.txt) | ~0~ | 1047 | 2021-12-17 20:19 | | 49110 | [](https://myanimelist.net/anime/49110/Yami_Shibai_9) | [Yami Shibai 9](https://subsplease.org/shows/yami-shibai-9) | TV | 13 / 13 | **Finished Airing** | 5.91 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yami+Shibai+9+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/49110__yami_shibai_9.txt) | ~0~ | 576 | 2021-10-03 19:30 | | 48641 | [](https://myanimelist.net/anime/48641/Obey_Me) | [Obey Me!](https://subsplease.org/shows/obey-me) | ONA | 12 / 12 | **Finished Airing** | 7.15 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Obey+Me+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/48641__obey_me.txt) | ~0~ | 792 | 2021-12-31 08:01 | | 46118 | [](https://myanimelist.net/anime/46118/Wave_Surfing_Yappe_TV) | [Wave!! Surfing Yappe!! (TV)](https://subsplease.org/shows/wave-surfing-yappe-tv) | TV | 12 / 12 | **Finished Airing** | 5.86 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Wave+Surfing+Yappe+TV+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/46118__wave_surfing_yappe_tv.txt) | ~0~ | 478 | 2021-03-29 18:31 | | 45587 | [](https://myanimelist.net/anime/45587/Itazuraguma_no_Gloomy) | [Itazuraguma no Gloomy](https://subsplease.org/shows/itazuraguma-no-gloomy) | TV | 12 / 12 | **Finished Airing** | 5.28 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Itazuraguma+no+Gloomy+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/45587__itazuraguma_no_gloomy.txt) | ~0~ | 435 | 2021-06-28 15:30 | | 44208 | [](https://myanimelist.net/anime/44208/Yami_Shibai_8) | [Yami Shibai 8](https://subsplease.org/shows/yami-shibai-8) | TV | 13 / 13 | **Finished Airing** | 5.82 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yami+Shibai+8+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44208__yami_shibai_8.txt) | ~0~ | 490 | 2021-04-04 19:30 | | 44064 | [](https://myanimelist.net/anime/44064/Liehuo_Jiao_Chou) | [Drowning Sorrows in Raging Fire](https://subsplease.org/shows/drowning-sorrows-in-raging-fire) | ONA | 12 / 12 | **Finished Airing** | 7.27 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Drowning+Sorrows+in+Raging+Fire+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44064__liehuo_jiao_chou.txt) | ~0~ | 969 | 2021-12-16 03:03 | | 44041 | [](https://myanimelist.net/anime/44041/SD_Gundam_World_Heroes) | [SD Gundam World Heroes](https://subsplease.org/shows/sd-gundam-world-heroes) | ONA | 24 / 24 | **Finished Airing** | 5.74 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+SD+Gundam+World+Heroes+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44041__sd_gundam_world_heroes.txt) | ~0~ | 400 | 2021-09-16 10:01 | | 44040 | [](https://myanimelist.net/anime/44040/Abciee_Shuugyou_Nikki) | [Abciee Shuugyou Nikki](https://subsplease.org/shows/abciee-shuugyou-nikki) | TV | 12 / 12 | **Finished Airing** | 5.44 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Abciee+Shuugyou+Nikki+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/44040__abciee_shuugyou_nikki.txt) | ~0~ | 366 | 2021-03-24 18:00 | | 43001 | [](https://myanimelist.net/anime/43001/Youjo_Shachou) | [Youjo Shachou](https://subsplease.org/shows/youjo-shachou) | ONA | 1 / 13 | **Finished Airing** | 6.59 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Youjo+Shachou+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/43001__youjo_shachou.txt) | ~0~ | 642 | 2021-01-01 03:10 | | 42946 | [](https://myanimelist.net/anime/42946/Kusoge_tte_Iuna_Animation) | [Kusoge-tte Iuna!](https://subsplease.org/shows/kusoge-tte-iuna) | ONA | 12 / 12 | **Finished Airing** | 5.38 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kusoge+tte+Iuna+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42946__kusoge_tte_iuna_animation.txt) | ~0~ | 360 | 2021-01-12 03:00 | | 42883 | [](https://myanimelist.net/anime/42883/Sore_dake_ga_Neck) | [Sore dake ga Neck](https://subsplease.org/shows/sore-dake-ga-neck) | TV | 12 / 12 | **Finished Airing** | 5.67 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sore+dake+ga+Neck+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42883__sore_dake_ga_neck.txt) | ~0~ | 310 | 2021-01-04 18:50 | | 42862 | [](https://myanimelist.net/anime/42862/Otona_no_Bouguya-san_II) | [Otona no Bouguya-san S2](https://subsplease.org/shows/otona-no-bouguya-san-s2) | ONA | 12 / 12 | **Finished Airing** | 5.5 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Otona+no+Bouguya+san+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42862__otona_no_bouguya_san_ii.txt) | ~0~ | 733 | 2021-03-19 15:31 | | 42832 | [](https://myanimelist.net/anime/42832/Tales_of_Crestoria__Toga_Waga_wo_Shoite_Kare_wa_Tatsu) | [Tales of Crestoria - Toga Waga wo Shoite Kare wa Tatsu](https://subsplease.org/shows/tales-of-crestoria-toga-waga-wo-shoite-kare-wa-tatsu) | TV Special | 1 / 1 | **Finished Airing** | 6.57 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tales+of+Crestoria+Toga+Waga+wo+Shoite+Kare+wa+Tatsu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42832__tales_of_crestoria_toga_waga_wo_shoite_kare_wa_tatsu.txt) | ~0~ | 1003 | 2020-10-18 17:49 | | 42825 | [](https://myanimelist.net/anime/42825/Project_Scard__Praeter_no_Kizu) | [Project Scard - Praeter no Kizu](https://subsplease.org/shows/project-scard-praeter-no-kizu) | TV | 13 / 13 | **Finished Airing** | 5.92 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Project+Scard+Praeter+no+Kizu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42825__project_scard_praeter_no_kizu.txt) | ~0~ | 992 | 2021-04-02 17:57 | | 42668 | [](https://myanimelist.net/anime/42668/Taisou_Zamurai) | [Taisou Zamurai](https://subsplease.org/shows/taisou-zamurai) | TV | 11 / 11 | **Finished Airing** | 7.23 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Taisou+Zamurai+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42668__taisou_zamurai.txt) | ~0~ | 1037 | 2020-12-19 17:31 | | 42657 | [](https://myanimelist.net/anime/42657/Himitsukessha_Taka_no_Tsume__Golden_Spell) | [Himitsukessha Taka no Tsume - Golden Spell](https://subsplease.org/shows/himitsukessha-taka-no-tsume-golden-spell) | TV | 12 / 12 | **Finished Airing** | 6.18 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Himitsukessha+Taka+no+Tsume+Golden+Spell+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42657__himitsukessha_taka_no_tsume_golden_spell.txt) | ~0~ | 234 | 2020-12-20 18:30 | | 42571 | [](https://myanimelist.net/anime/42571/Dogeza_de_Tanondemita) | [Dogeza de Tanondemita](https://subsplease.org/shows/dogeza-de-tanondemita) | TV | 12 / 12 | **Finished Airing** | 5.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dogeza+de+Tanondemita+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42571__dogeza_de_tanondemita.txt) | ~0~ | 1038 | 2020-12-30 15:40 | | 42568 | [](https://myanimelist.net/anime/42568/Yaku_nara_Mug_Cup_mo) | [Yakunara Mug Cup mo](https://subsplease.org/shows/yakunara-mug-cup-mo) | TV | 24 / 12 | **Finished Airing** | 6.54 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Yakunara+Mug+Cup+mo+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42568__yaku_nara_mug_cup_mo.txt) | ~0~ | 1079 | 2021-06-21 16:57 | | 42516 | [](https://myanimelist.net/anime/42516/Cardfight_Vanguard__overDress) | [Cardfight!! Vanguard overDress](https://subsplease.org/shows/cardfight-vanguard-overdress) | TV | 25 / 12 | **Finished Airing** | 5.97 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cardfight+Vanguard+overDress+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42516__cardfight_vanguard_overdress.txt) | ~0~ | 375 | 2021-12-27 15:41 | | 42514 | [](https://myanimelist.net/anime/42514/Anime_Kapibara-san) | [Anime Kapibara-san](https://subsplease.org/shows/anime-kapibara-san) | TV | 24 / 24 | **Finished Airing** | 6.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Anime+Kapibara+san+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42514__anime_kapibara_san.txt) | ~0~ | 288 | 2021-03-25 23:30 | | 42391 | [](https://myanimelist.net/anime/42391/Osomatsu-san_3rd_Season) | [Osomatsu-san S3](https://subsplease.org/shows/osomatsu-san-s3) | TV | 25 / 25 | **Finished Airing** | 7.39 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Osomatsu+san+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42391__osomatsu_san_3rd_season.txt) | ~0~ | 454 | 2021-03-29 18:01 | | 42250 | [](https://myanimelist.net/anime/42250/Bungou_Stray_Dogs_Wan) | [Bungou Stray Dogs Wan!](https://subsplease.org/shows/bungou-stray-dogs-wan) | TV | 12 / 12 | **Finished Airing** | 7.98 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Bungou+Stray+Dogs+Wan+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/42250__bungou_stray_dogs_wan.txt) | ~0~ | 760 | 2021-03-30 16:30 | | 41911 | [](https://myanimelist.net/anime/41911/Hanyou_no_Yashahime__Sengoku_Otogizoushi) | [Hanyou no Yashahime](https://subsplease.org/shows/hanyou-no-yashahime) | TV | 48 / 24 | **Finished Airing** | 6.7 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hanyou+no+Yashahime+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41911__hanyou_no_yashahime_sengoku_otogizoushi.txt) | ~0~ | 1732 | 2022-03-26 18:14 | | 41783 | [](https://myanimelist.net/anime/41783/Iwa_Kakeru_Sport_Climbing_Girls) | [Iwa Kakeru! Sport Climbing Girls](https://subsplease.org/shows/iwa-kakeru-sport-climbing-girls) | TV | 12 / 12 | **Finished Airing** | 6.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Iwa+Kakeru+Sport+Climbing+Girls+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41783__iwa_kakeru_sport_climbing_girls.txt) | ~0~ | 1671 | 2020-12-19 19:01 | | 41574 | [](https://myanimelist.net/anime/41574/Guraburu) | [Guraburu!](https://subsplease.org/shows/guraburu) | TV | 12 / 12 | **Finished Airing** | 5.79 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Guraburu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41574__guraburu.txt) | ~0~ | 440 | 2020-12-24 14:01 | | 41520 | [](https://myanimelist.net/anime/41520/Show_by_Rock_Stars) | [Show by Rock!! Stars!!](https://subsplease.org/shows/show-by-rock-stars) | TV | 12 / 12 | **Finished Airing** | 7.14 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Show+by+Rock+Stars+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41520__show_by_rock_stars.txt) | ~0~ | 617 | 2021-03-25 14:02 | | 41372 | [](https://myanimelist.net/anime/41372/Senyoku_no_Sigrdrifa) | [Senyoku no Sigrdrifa](https://subsplease.org/shows/senyoku-no-sigrdrifa) | TV | 13 / 12 | **Finished Airing** | 6.45 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Senyoku+no+Sigrdrifa+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41372__senyoku_no_sigrdrifa.txt) | ~0~ | 1549 | 2020-12-26 16:01 | | 41345 | [](https://myanimelist.net/anime/41345/Noblesse) | [Noblesse](https://subsplease.org/shows/noblesse) | TV | 13 / 13 | **Finished Airing** | 6.89 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Noblesse+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41345__noblesse.txt) | ~0~ | 2127 | 2020-12-30 14:01 | | 41283 | [](https://myanimelist.net/anime/41283/Cardfight_Vanguard_Gaiden__If) | [Cardfight!! Vanguard Gaiden - If](https://subsplease.org/shows/cardfight-vanguard-gaiden-if) | TV | 9 / 25 | **Finished Airing** | 6.48 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Cardfight+Vanguard+Gaiden+If+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/41283__cardfight_vanguard_gaiden_if.txt) | ~0~ | 186 | 2020-11-27 23:41 | | 40974 | [](https://myanimelist.net/anime/40974/Kuma_Kuma_Kuma_Bear) | [Kuma Kuma Kuma Bear](https://subsplease.org/shows/kuma-kuma-kuma-bear) | TV | 12 / 12 | **Finished Airing** | 7.23 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kuma+Kuma+Kuma+Bear+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40974__kuma_kuma_kuma_bear.txt) | ~0~ | 2461 | 2020-12-23 13:01 | | 40958 | [](https://myanimelist.net/anime/40958/Rail_Romanesque) | [Rail Romanesque](https://subsplease.org/shows/rail-romanesque) | TV | 12 / 12 | **Finished Airing** | 5.17 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Rail+Romanesque+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40958__rail_romanesque.txt) | ~0~ | 638 | 2020-12-18 17:00 | | 40957 | [](https://myanimelist.net/anime/40957/Shin_Chuuka_Ichiban_2nd_Season) | [Shin Chuuka Ichiban!](https://subsplease.org/shows/shin-chuuka-ichiban) | TV | 12 / 12 | **Finished Airing** | 6.68 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shin+Chuuka+Ichiban+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40957__shin_chuuka_ichiban_2nd_season.txt) | ~0~ | 503 | 2021-03-29 16:42 | | 40906 | [](https://myanimelist.net/anime/40906/Dragon_Quest__Dai_no_Daibouken_2020) | [Dragon Quest - Dai no Daibouken (2020)](https://subsplease.org/shows/dragon-quest-dai-no-daibouken-2020) | TV | 51 / 100 | **Finished Airing** | 7.73 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Dragon+Quest+Dai+no+Daibouken+2020+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40906__dragon_quest_dai_no_daibouken_2020.txt) | ~0~ | 1599 | 2022-10-22 02:04 | | 40901 | [](https://myanimelist.net/anime/40901/Toji_no_Miko__Kizamishi_Issen_no_Tomoshibi) | [Toji no Miko - Kizamishi Issen no Tomoshibi](https://subsplease.org/shows/toji-no-miko-kizamishi-issen-no-tomoshibi) | OVA | 2 / 2 | **Finished Airing** | 6.63 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Toji+no+Miko+Kizamishi+Issen+no+Tomoshibi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40901__toji_no_miko_kizamishi_issen_no_tomoshibi.txt) | ~0~ | 808 | 2020-11-29 18:29 | | 40885 | [](https://myanimelist.net/anime/40885/Can_Ci_Pin__Fangzhu_Xingkong) | [The Defective](https://subsplease.org/shows/the-defective) | ONA | 16 / 16 | **Finished Airing** | 6.97 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+The+Defective+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40885__can_ci_pin_fangzhu_xingkong.txt) | ~0~ | 712 | 2021-11-05 03:02 | | 40833 | [](https://myanimelist.net/anime/40833/Inu_to_Neko_Docchi_mo_Katteru_to_Mainichi_Tanoshii) | [Inu to Neko Docchimo Katteru to Mainichi Tanoshii](https://subsplease.org/shows/inu-to-neko-docchimo-katteru-to-mainichi-tanoshii) | TV | 24 / 24 | **Finished Airing** | 7.23 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Inu+to+Neko+Docchimo+Katteru+to+Mainichi+Tanoshii+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40833__inu_to_neko_docchi_mo_katteru_to_mainichi_tanoshii.txt) | ~0~ | 491 | 2021-03-26 18:00 | | 40803 | [](https://myanimelist.net/anime/40803/Hypnosis_Mic__Division_Rap_Battle_-_Rhyme_Anima) | [Hypnosis Mic -Division Rap Battle- Rhyme Anima](https://subsplease.org/shows/hypnosis-mic-division-rap-battle-rhyme-anima) | TV | 13 / 13 | **Finished Airing** | 6.81 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Hypnosis+Mic+Division+Rap+Battle+Rhyme+Anima+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40803__hypnosis_mic_division_rap_battle_rhyme_anima.txt) | ~0~ | 576 | 2020-12-25 16:31 | | 40786 | [](https://myanimelist.net/anime/40786/Skate-Leading☆Stars) | [Skate Leading Stars](https://subsplease.org/shows/skate-leading-stars) | TV | 12 / 12 | **Finished Airing** | 6.45 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Skate+Leading+Stars+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40786__skate_leading_stars.txt) | ~0~ | 540 | 2021-03-14 14:02 | | 40679 | [](https://myanimelist.net/anime/40679/243__Seiin_Koukou_Danshi_Volley-bu) | [2.43 - Seiin Koukou Danshi Volley-bu](https://subsplease.org/shows/2-43-seiin-koukou-danshi-volley-bu) | TV | 12 / 12 | **Finished Airing** | 6.14 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+2+43+Seiin+Koukou+Danshi+Volley+bu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40679__2_43_seiin_koukou_danshi_volley_bu.txt) | ~0~ | 1370 | 2021-03-25 18:47 | | 40610 | [](https://myanimelist.net/anime/40610/Healin_Good♡Precure) | [Healin Good Precure](https://subsplease.org/shows/healin-good-precure) | TV | 19 / 45 | **Finished Airing** | 6.92 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Healin+Good+Precure+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40610__healin_good_precure.txt) | ~0~ | 264 | 2021-02-21 01:33 | | 40595 | [](https://myanimelist.net/anime/40595/Kimi_to_Boku_no_Saigo_no_Senjou_Aruiwa_Sekai_ga_Hajimaru_Seisen) | [Kimi to Boku no Saigo no Senjou, Arui wa Sekai ga Hajimaru Seisen](https://subsplease.org/shows/kimi-to-boku-no-saigo-no-senjou-arui-wa-sekai-ga-hajimaru-seisen) | TV | 12 / 12 | **Finished Airing** | 6.69 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Kimi+to+Boku+no+Saigo+no+Senjou+Arui+wa+Sekai+ga+Hajimaru+Seisen+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40595__kimi_to_boku_no_saigo_no_senjou_aruiwa_sekai_ga_hajimaru_seisen.txt) | ~0~ | 3112 | 2020-12-23 18:59 | | 40506 | [](https://myanimelist.net/anime/40506/Shadowverse) | [Shadowverse](https://subsplease.org/shows/shadowverse) | TV | 25 / 48 | **Finished Airing** | 5.75 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shadowverse+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40506__shadowverse.txt) | ~0~ | 358 | 2021-03-30 18:41 | | 40504 | [](https://myanimelist.net/anime/40504/Major_2nd_2nd_Season) | [Major 2nd S2](https://subsplease.org/shows/major-2nd-s2) | TV | 6 / 25 | **Finished Airing** | 7.47 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Major+2nd+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40504__major_2nd_2nd_season.txt) | ~0~ | 500 | 2020-11-07 12:01 | | 40488 | [](https://myanimelist.net/anime/40488/Futsal_Boys) | [Futsal Boys!!!!!](https://subsplease.org/shows/futsal-boys) | TV | 12 / 12 | **Finished Airing** | 5.46 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Futsal+Boys+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40488__futsal_boys.txt) | ~0~ | 831 | 2022-03-27 14:32 | | 40397 | [](https://myanimelist.net/anime/40397/Maoujou_de_Oyasumi) | [Maou-jou de Oyasumi](https://subsplease.org/shows/maou-jou-de-oyasumi) | TV | 12 / 12 | **Finished Airing** | 7.96 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Maou+jou+de+Oyasumi+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40397__maoujou_de_oyasumi.txt) | ~0~ | 2926 | 2020-12-21 18:01 | | 40359 | [](https://myanimelist.net/anime/40359/Ikebukuro_West_Gate_Park) | [Ikebukuro West Gate Park](https://subsplease.org/shows/ikebukuro-west-gate-park) | TV | 12 / 12 | **Finished Airing** | 6.87 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ikebukuro+West+Gate+Park+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40359__ikebukuro_west_gate_park.txt) | ~0~ | 1292 | 2020-12-22 13:01 | | 40358 | [](https://myanimelist.net/anime/40358/Gal_to_Kyouryuu) | [Gal to Kyouryuu](https://subsplease.org/shows/gal-to-kyouryuu) | TV | 5 / 12 | **Finished Airing** | 6.45 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gal+to+Kyouryuu+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40358__gal_to_kyouryuu.txt) | ~0~ | 691 | 2020-12-19 17:31 | | 40272 | [](https://myanimelist.net/anime/40272/A3_Season_Autumn___Winter) | [A3! Season Autumn & Winter](https://subsplease.org/shows/a3-season-autumn-winter) | TV | 12 / 12 | **Finished Airing** | 7.25 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+A3+Season+Autumn+Winter+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/40272__a3_season_autumn_winter.txt) | ~0~ | 252 | 2020-12-28 16:02 | | 39917 | [](https://myanimelist.net/anime/39917/Sabiiro_no_Armor__Reimei) | [Sabiiro no Armor - Reimei](https://subsplease.org/shows/sabiiro-no-armor-reimei) | TV | 12 / 12 | **Finished Airing** | 3.83 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Sabiiro+no+Armor+Reimei+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39917__sabiiro_no_armor_reimei.txt) | ~0~ | 784 | 2022-03-27 14:31 | | 39725 | [](https://myanimelist.net/anime/39725/I★Chu__Halfway_Through_the_Idol) | [I-Chu - Halfway Through the Idol](https://subsplease.org/shows/i-chu-halfway-through-the-idol) | TV | 12 / 12 | **Finished Airing** | 6.56 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+I+Chu+Halfway+Through+the+Idol+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39725__i_chu_halfway_through_the_idol.txt) | ~0~ | 387 | 2021-03-24 15:02 | | 39609 | [](https://myanimelist.net/anime/39609/Ochikobore_Fruit_Tart) | [Ochikobore Fruit Tart](https://subsplease.org/shows/ochikobore-fruit-tart) | TV | 12 / 12 | **Finished Airing** | 6.77 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Ochikobore+Fruit+Tart+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/39609__ochikobore_fruit_tart.txt) | ~0~ | 1090 | 2020-12-28 13:31 | | 38669 | [](https://myanimelist.net/anime/38669/Tsukiuta_The_Animation_2) | [Tsukiuta. The Animation S2](https://subsplease.org/shows/tsukiuta-the-animation-s2) | TV | 13 / 13 | **Finished Airing** | 6.65 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tsukiuta+The+Animation+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38669__tsukiuta_the_animation_2.txt) | ~0~ | 221 | 2020-12-30 14:31 | | 38440 | [](https://myanimelist.net/anime/38440/Shikizakura) | [Shikizakura](https://subsplease.org/shows/shikizakura) | TV | 12 / 12 | **Finished Airing** | 5.84 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Shikizakura+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38440__shikizakura.txt) | ~0~ | 1606 | 2021-12-26 03:02 | | 38337 | [](https://myanimelist.net/anime/38337/Gochuumon_wa_Usagi_desu_ka_Bloom) | [Gochuumon wa Usagi Desu ka S3](https://subsplease.org/shows/gochuumon-wa-usagi-desu-ka-s3) | TV | 12 / 12 | **Finished Airing** | 7.92 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Gochuumon+wa+Usagi+Desu+ka+S3+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38337__gochuumon_wa_usagi_desu_ka_bloom.txt) | ~0~ | 1725 | 2020-12-26 13:01 | | 38005 | [](https://myanimelist.net/anime/38005/Strike_Witches__Road_to_Berlin) | [Strike Witches - Road to Berlin](https://subsplease.org/shows/strike-witches-road-to-berlin) | TV | 12 / 12 | **Finished Airing** | 7.35 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Strike+Witches+Road+to+Berlin+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/38005__strike_witches_road_to_berlin.txt) | ~0~ | 1063 | 2020-12-23 17:06 | | 37962 | [](https://myanimelist.net/anime/37962/IDOLiSH7_Second_Beat) | [IDOLiSH7 S2](https://subsplease.org/shows/idolish7-s2) | TV | 11 / 15 | **Finished Airing** | 8.13 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+IDOLiSH7+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/37962__idolish7_second_beat.txt) | ~0~ | 253 | 2020-12-27 15:31 | | 37008 | [](https://myanimelist.net/anime/37008/Tsukipro_The_Animation_2) | [Tsukipro The Animation S2](https://subsplease.org/shows/tsukipro-the-animation-s2) | TV | 13 / 13 | **Finished Airing** | 6.41 | [Search](https://nyaa.si/?f=0&c=1_0&q=subsplease+Tsukipro+The+Animation+S2+1080p+mkv) | [Download](https://huggingface.co/datasets/deepghs/subsplease_animes/resolve/main/magnets/37008__tsukipro_the_animation_2.txt) | ~0~ | 443 | 2021-12-29 14:02 |

isp-uv-es/CloudSEN12Plus

isp-uv-es

--- license: cc0-1.0 task_categories: - image-segmentation language: - en tags: - clouds - earth-observation - remote-sensing - sentinel-2 - deep-learning - multi-spectral - satellite - geospatial pretty_name: cloudsen12 size_categories: - 100K<n<1M --- # 🚨 New Dataset Version Released! ## We are excited to announce the release of **Version [1.1]** of our dataset! ## This update includes: - **[L2A & L1C support]**. - **[Temporal support]**. - **[Check the data without downloading (Cloud-optimized properties)]**. # 📥 Go to: https://huggingface.co/datasets/tacofoundation/cloudsen12 and follow the instructions in colab <center> <img src="cloudsen12.gif" alt="drawing" width="35%"/> </center> **CloudSEN12+** is a significant extension of the [CloudSEN12](https://cloudsen12.github.io/) dataset, which doubles the number of expert-reviewed labels, making it, by a large margin, the largest cloud detection dataset to date for Sentinel-2. All labels from the previous version have been curated and refined, enhancing the dataset's trustworthiness. This new release is licensed **under CC0**, which puts it in the public domain and allows anyone to use, modify, and distribute it without permission or attribution. ## Data Folder order The CloudSEN12+ dataset is organized into `train`, `val`, and `test` splits. The images have been padded from 509x509 to 512x512 and 2000x2000 to 2048x2048 to ensure that the patches are divisible by 32. The padding is filled with zeros in the left and bottom sides of the image. For those who prefer traditional storage formats, GeoTIFF files are available in our [ScienceDataBank](https://www.scidb.cn/en/detail?dataSetId=2036f4657b094edfbb099053d6024b08&version=V1) repository. <center> <img src="https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/9UA4U3WObVeq7BAcf37-C.png" alt="drawing" width="50%"/> </center> *CloudSEN12+ spatial coverage. The terms p509 and p2000 denote the patch size 509 × 509 and 2000 × 2000, respectively. ‘high’, ‘scribble’, and ‘nolabel’ refer to the types of expert-labeled annotations* **ML-STAC Snippet** ```python import mlstac dataset = mlstac.load('isp-uv-es/CloudSEN12Plus') ``` **Sensor: Sentinel2 - MSI** **ML-STAC Task: image-segmentation** **ML-STAC Dataset Version: 1.0.0** **Data raw repository: [https://cloudsen12.github.io/](https://cloudsen12.github.io/)** **Dataset discussion: [https://huggingface.co/datasets/isp-uv-es/CloudSEN12Plus/discussions](https://huggingface.co/datasets/isp-uv-es/CloudSEN12Plus/discussions)** **Split_strategy: stratified** **Paper: [https://www.sciencedirect.com/science/article/pii/S2352340924008163](https://www.sciencedirect.com/science/article/pii/S2352340924008163)** ## Data Providers |Name|Role|URL| | :---: | :---: | :---: | |Image & Signal Processing|['host']|https://isp.uv.es/| |ESA|['producer']|https://www.esa.int/| ## Curators |Name|Organization|URL| | :---: | :---: | :---: | |Cesar Aybar|Image & Signal Processing|http://csaybar.github.io/| ## Labels For human **_high-quality_** labels (also UnetMobV2_V2 & UnetMobV2_V1 predictions). |Name|Value| | :---: | :---: | |clear|0| |thick-cloud|1| |thin-cloud|2| |cloud-shadow|3| For human **_scribble_** labels. |Name|Value| | :---: | :---: | |clear|0| |thick-cloud border|1| |thick-cloud center|2| |thin-cloud border|3| |thin-cloud center|4| |cloud-shadow border|5| |cloud-shadow center|6| ## Dimensions |Axis|Name|Description| | :---: | :---: | :---: | |0|C|Spectral bands| |1|H|Height| |2|W|Width| ## Spectral Bands |Name|Common Name|Description|Center Wavelength|Full Width Half Max|Index| | :---: | :---: | :---: | :---: | :---: | :---: | |B01|coastal aerosol|Band 1 - Coastal aerosol - 60m|443.5|17.0|0| |B02|blue|Band 2 - Blue - 10m|496.5|53.0|1| |B03|green|Band 3 - Green - 10m|560.0|34.0|2| |B04|red|Band 4 - Red - 10m|664.5|29.0|3| |B05|red edge 1|Band 5 - Vegetation red edge 1 - 20m|704.5|13.0|4| |B06|red edge 2|Band 6 - Vegetation red edge 2 - 20m|740.5|13.0|5| |B07|red edge 3|Band 7 - Vegetation red edge 3 - 20m|783.0|18.0|6| |B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|7| |B8A|red edge 4|Band 8A - Vegetation red edge 4 - 20m|864.5|19.0|8| |B09|water vapor|Band 9 - Water vapor - 60m|945.0|18.0|9| |B10|cirrus|Band 10 - Cirrus - 60m|1375.5|31.0|10| |B11|SWIR 1|Band 11 - Shortwave infrared 1 - 20m|1613.5|89.0|11| |B12|SWIR 2|Band 12 - Shortwave infrared 2 - 20m|2199.5|173.0|12| |CM1| Cloud Mask 1| Expert-labeled image. |-|-|13| |CM2| Cloud Mask 2| UnetMobV2-V1 labeled image. |-|-|14| ## Data Structure We use `.mls` format to store the data in HugginFace and GeoTIFF for ScienceDataBank. ## Folder Structure The **fixed/** folder contains high and scribble labels, which have been improved in this new version. These changes have already been integrated. The **demo/** folder contains examples illustrating how to utilize the models trained with CLoudSEN12 to estimate the hardness and trustworthiness indices. The **images/** folder contains the CloudSEN12+ imagery ## Download The code below can be used to download the dataset using the `mlstac` library. For a more detailed example, please refer to the `examples` section in our website [https://cloudsen12.github.io/](https://cloudsen12.github.io/). ```python import mlstac import matplotlib.pyplot as plt import numpy as np ds = mlstac.load(snippet="isp-uv-es/CloudSEN12Plus") subset = ds.metadata[(ds.metadata["split"] == "test") & (ds.metadata["label_type"] == "high") & (ds.metadata["proj_shape"] == 509)][10:14] datacube = mlstac.get_data(dataset=subset) ``` Make a plot of the data point downloaded ```python datapoint = datacube[2] datapoint_rgb = np.moveaxis(datapoint[[3, 2, 1]], 0, -1) / 5_000 fig, ax = plt.subplots(1, 3, figsize=(10, 5)) ax[0].imshow(datapoint_rgb) ax[0].set_title("RGB") ax[1].imshow(datapoint[13], cmap="gray") ax[1].set_title("Human label") ax[2].imshow(datapoint[14], cmap="gray") ax[2].set_title("UnetMobV2 v1.0") ```  ## Citation Cite the dataset as: ```bibtex @article{aybar2024cloudsen12+, title={CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2}, author={Aybar, Cesar and Bautista, Lesly and Montero, David and Contreras, Julio and Ayala, Daryl and Prudencio, Fernando and Loja, Jhomira and Ysuhuaylas, Luis and Herrera, Fernando and Gonzales, Karen and others}, journal={Data in Brief}, pages={110852}, year={2024}, DOI={10.1016/j.dib.2024.110852}, publisher={Elsevier} } ```

EPFL-CVLAB-SPACECRAFT/SwissCube

EPFL-CVLAB-SPACECRAFT

--- license: mit ---

hf-internal-testing/dummy_image_text_data

hf-internal-testing

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1944983.0 num_examples: 20 download_size: 1690123 dataset_size: 1944983.0 --- # Dataset Card for "dummy_image_text_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

lowercaseonly/cghd

lowercaseonly

--- license: cc-by-3.0 pretty_name: A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images size_categories: - 1K<n<10K task_categories: - object-detection - image-segmentation language: - en - de --- # Public Ground-Truth Dataset for Handwritten Circuit Diagrams (GTDB-HD) This repository contains images of hand-drawn electrical circuit diagrams as well as accompanying bounding box annotation for object detection as well as segmentation ground truth files. This dataset is intended to train (e.g. neural network) models for the purpose of the extraction of electrical graphs from raster graphics. ## Structure The folder structure is made up as follows: ``` gtdh-hd │ README.md # This File │ classes.json # Classes List │ classes_color.json # Classes to Color Map │ classes_discontinuous.json # Classes Morphology Info │ classes_ports.json # Electrical Port Descriptions for Classes │ consistency.py # Dataset Statistics and Consistency Check | loader.py # Simple Dataset Loader and Storage Functions │ segmentation.py # Multiclass Segmentation Generation │ utils.py # Helper Functions │ requirements.txt # Requirements for Scripts └───drafter_D │ └───annotations # Bounding Box Annotations │ │ │ CX_DY_PZ.xml │ │ │ ... │ │ │ └───images # Raw Images │ │ │ CX_DY_PZ.jpg │ │ │ ... │ │ │ └───instances # Instance Segmentation Polygons │ │ │ CX_DY_PZ.json │ │ │ ... │ │ │ └───segmentation # Binary Segmentation Maps (Strokes vs. Background) │ │ │ CX_DY_PZ.jpg │ │ │ ... ... ``` Where: - `D` is the (globally) running number of a drafter - `X` is the (globally) running number of the circuit (12 Circuits per Drafter) - `Y` is the Local Number of the Circuit's Drawings (2 Drawings per Circuit) - `Z` is the Local Number of the Drawing's Image (4 Pictures per Drawing) ### Image Files Every image is RGB-colored and either stored as `jpg`, `jpeg` or `png` (both uppercase and lowercase suffixes exist). ### Bounding Box Annotations A complete list of class labels including a suggested mapping table to integer numbers for training and prediction purposes can be found in `classes.json`. The annotations contains **BB**s (Bounding Boxes) of **RoI**s (Regions of Interest) like electrical symbols or texts within the raw images and are stored in the [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) format. Please note: *For every Raw image in the dataset, there is an accompanying bounding box annotation file.* #### Known Labeled Issues - C25_D1_P4 cuts off a text - C27 cuts of some texts - C29_D1_P1 has one additional text - C31_D2_P4 has a text less - C33_D1_P4 has a text less - C46_D2_P2 cuts of a text ### Instance Segmentation For every binary segmentation map, there is an accompanying polygonal annotation file for instance segmentation purposes, which is stored in the [labelme](https://github.com/wkentaro/labelme) format. Note that the contained polygons are quite coarse, intended to be used in conjunction with the binary segmentation maps for connection extraction and to tell individual instances with overlapping BBs apart. ### Segmentation Maps Binary Segmentation images are available for some samples and bear the same resolution as the respective image files. They are considered to contain only black and white pixels indicating areas of drawings strokes and background respectively. ### Netlists For some images, there are also netlist files available, which are stored in the [ASC](http://ltwiki.org/LTspiceHelp/LTspiceHelp/Spice_Netlist.htm) format. ### Consistency and Statistics This repository comes with a stand-alone script to: - Obtain Statistics on - Class Distribution - BB Sizes - Check the BB Consistency - Classes with Regards to the `classes.json` - Counts between Pictures of the same Drawing - Ensure a uniform writing style of the Annotation Files (indent) The respective script is called without arguments to operate on the **entire** dataset: ``` $ python3 consistency.py ``` Note that due to a complete re-write of the annotation data, the script takes several seconds to finish. A drafter can be specified as CLI argument to restrict the evaluation (for example drafter 15): ``` $ python3 consistency.py 15 ``` ### Multi-Class (Instance) Segmentation Processing This dataset comes with a script to process both new and existing (instance) segmentation files. It is invoked as follows: ``` $ python3 segmentation.py <command> <drafter_id> <target> <source> ``` Where: - `<command>` has to be one of: - `transform` - Converts existing BB Annotations to Polygon Annotations - Default target folder: `instances` - Existing polygon files will not be overridden in the default settings, hence this command will take no effect in an completely populated dataset. - Intended to be invoked after adding new binary segmentation maps - **This step has to be performed before all other commands** - `wire` - Generates Wire Describing Polygons - Default target folder: `wires` - `keypoint` - Generates Keypoints for Component Terminals - Default target folder: `keypoints` - `create` - Generates Multi-Class segmentation Maps - Default target folder: `segmentation_multi_class` - `refine` - Refines Coarse Polygon Annotations to precisely match the annotated objects - Default target folder: `instances_refined` - For instance segmentation purposes - `pipeline` - executes `wire`,`keypoint` and `refine` stacked, with one common `source` and `target` folder - Default target folder: `instances_refined` - `assign` - Connector Point to Port Type Assignment by Geometric Transformation Matching - `<drafter_id>` **optionally** restricts the process to one of the drafters - `<target>` **optionally** specifies a divergent target folder for results to be placed in - `<source>` **optionally** specifies a divergent source folder to read from Please note that source and target forlders are **always** subfolder inside the individual drafter folders. Specifying source and target folders allow to stack the results of individual processing steps. For example, to perform the entire pipeline for drafter 20 manually, use: ``` python3 segmentation.py wire 20 instances_processed instances python3 segmentation.py keypoint 20 instances_processed instances_processed python3 segmentation.py refine 20 instances_processed instances_processed ``` ### Dataset Loader This dataset is also shipped with a set of loader and writer functions, which are internally used by the segmentation and consistency scripts and can be used for training. The dataset loader is simple, framework-agnostic and has been prepared to be callable from any location in the file system. Basic usage: ``` from loader import read_dataset db_bb = read_dataset() # Read all BB Annotations db_seg = read_dataset(segmentation=True) # Read all Polygon Annotations db_bb_val = read_dataset(drafter=12) # Read Drafter 12 BB Annotations len(db_bb) # Get The Amount of Samples db_bb[5] # Get an Arbitrary Sample db = read_images(drafter=12) # Returns a list of (Image, Annotation) pairs db = read_snippets(drafter=12) # Returns a list of (Image, Annotation) pairs ``` ## Citation If you use this dataset for scientific publications, please consider citing us as follows: ``` @inproceedings{thoma2021public, title={A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images}, author={Thoma, Felix and Bayer, Johannes and Li, Yakun and Dengel, Andreas}, booktitle={International Conference on Document Analysis and Recognition}, pages={20--27}, year={2021}, organization={Springer} } ``` ## How to Contribute If you want to contribute to the dataset as a drafter or in case of any further questions, please send an email to: <[email protected]> (corresponding author), <[email protected]>, <[email protected]> ## Guidelines These guidelines are used throughout the generation of the dataset. They can be used as an instruction for participants and data providers. ### Drafter Guidelines - 12 Circuits should be drawn, each of them twice (24 drawings in total) - Most important: The drawing should be as natural to the drafter as possible - Free-Hand sketches are preferred, using rulers and drawing Template stencils should be avoided unless it appears unnatural to the drafter - Different types of pens/pencils should be used for different drawings - Different kinds of (colored, structured, ruled, lined) paper should be used - One symbol set (European/American) should be used throughout one drawing (consistency) - It is recommended to use the symbol set that the drafter is most familiar with - It is **strongly** recommended to share the first one or two circuits for review by the dataset organizers before drawing the rest to avoid problems (complete redrawing in worst case) ### Image Capturing Guidelines - For each drawing, 4 images should be taken (96 images in total per drafter) - Angle should vary - Lighting should vary - Moderate (e.g. motion) blur is allowed - All circuit-related aspects of the drawing must be _human-recognicable_ - The drawing should be the main part of the image, but _naturally_ occurring objects from the environment are welcomed - The first image should be _clean_, i.e. ideal capturing conditions - Kinks and Buckling can be applied to the drawing between individual image capturing - Try to use the file name convention (`CX_DY_PZ.jpg`) as early as possible - The circuit range `X` will be given to you - `Y` should be `1` or `2` for the drawing - `Z` should be `1`,`2`,`3` or `4` for the picture ### Object Annotation Guidelines - General Placement - A **RoI** must be **completely** surrounded by its **BB** - A **BB** should be as tight as possible to the **RoI** - In case of connecting lines not completely touching the symbol, the BB should extended (only by a small margin) to enclose those gaps (epecially considering junctions) - Characters that are part of the **essential symbol definition** should be included in the BB (e.g. the `+` of a polarized capacitor should be included in its BB) - **Junction** annotations - Used for actual junction points (Connection of three or more wire segments with a small solid circle) - Used for connection of three or more sraight line wire segements where a physical connection can be inferred by context (i.e. can be distinuished from **crossover**) - Used for wire line corners - Redundant Junction Points should **not** be annotated (small solid circle in the middle of a straight line segment) - Should not be used for corners or junctions that are part of the symbol definition (e.g. Transistors) - **Crossover** Annotations - If dashed/dotted line: BB should cover the two next dots/dashes - **Text** annotations - Individual Text Lines should be annotated Individually - Text Blocks should only be annotated If Related to Circuit or Circuit's Components - Semantically meaningful chunks of information should be annotated Individually - component characteristics enclosed in a single annotation (e.g. __100Ohms__, __10%__ tolerance, __5V__ max voltage) - Component Names and Types (e.g. __C1__, __R5__, __ATTINY2313__) - Custom Component Terminal Labels (i.e. __Integrated Circuit__ Pins) - Circuit Descriptor (e.g. "Radio Amplifier") - Texts not related to the Circuit should be ignored - e.g. Brief paper, Company Logos - Drafters auxiliary markings for internal organization like "D12" - Texts on Surrounding or Background Papers - Characters which are part of the essential symbol definition should __not__ be annotated as Text dedicatedly - e.g. Schmitt Trigger __S__, , and gate __&__, motor __M__, Polarized capacitor __+__ - Only add terminal text annotation if the terminal is not part of the essential symbol definition - **Table** cells should be annotated independently - **Operation Amplifiers** - Both the triangular US symbols and the european IC-like symbols symbols for OpAmps should be labeled `operational_amplifier` - The `+` and `-` signs at the OpAmp's input terminals are considered essential and should therefore not be annotated as texts - **Complex Components** - Both the entire Component and its sub-Components and internal connections should be annotated: | Complex Component | Annotation | | ----------------- | ------------------------------------------------------ | | Optocoupler | 0. `optocoupler` as Overall Annotation | | | 1. `diode.light_emitting` | | | 2. `transistor.photo` (or `resistor.photo`) | | | 3. `optical` if LED and Photo-Sensor arrows are shared | | | Then the arrows area should be includes in all | | Relay | 0. `relay` as Overall Annotation | | (also for | 1. `inductor` | | coupled switches) | 2. `switch` | | | 3. `mechanical` for the dashed line between them | | Transformer | 0. `transformer` as Overall Annotation | | | 1. `inductor` or `inductor.coupled` (watch the dot) | | | 3. `magnetic` for the core | #### Rotation Annotations The Rotation (integer in degree) should capture the overall rotation of the symbol shape. However, the position of the terminals should also be taked into consideration. Under idealized circumstances (no perspective distorion and accurately drawn symbols according to the symbol library), these two requirements equal each other. For pathological cases however, in which shape and the set of terminals (or even individual terminals) are conflicting, the rotation should compromise between all factors. Rotation annotations are currently work in progress. They should be provided for at least the following classes: - "voltage.dc" - "resistor" - "capacitor.unpolarized" - "diode" - "transistor.bjt" #### Text Annotations - The Character Sequence in the Text Label Annotations should describe the actual Characters depicted in the respective Bounding Box as Precisely as Possible - Bounding Box Annotations of class `text` - Bear an additional `<text>` tag in which their content is given as string - The `Omega` and `Mikro` Symbols are escaped respectively - Currently Work in Progress - The utils script allows for migrating text annotations from one annotation file to another: `python3 utils.py source target` ### Segmentation Map Guidelines - Areas of __Intended__ drawing strokes (ink and pencil abrasion respectively) should be marked black, all other pixels (background) should be white - shining through the paper (from the rear side or other sheets) should be considered background ### Polygon Annotation Guidelines 0. Before starting, make sure the respective files exist for the image sample to be polygon-annotated: - BB Annotations (Pascal VOC XML File) - (Binary) Segmentation Map 1. Transform the BB annotations into raw polygons - Use: `python3 segmentation.py transform` 2. Refine the Polygons - **To Avoid Embedding Image Data into the resulting JSON**, use: `labelme --nodata` - Just make sure there are no overlaps between instances - Especially take care about overlaps with structural elements like junctions and crossovers 3. Generate Multi-Class Segmentation Maps from the refined polygons - Use: `python3 segmentation.py create` - Use the generated images for a visual inspection - After spotting problems, continue with Step 2 ### Terminal Annotation Guidelines ``` labelme --labels "connector" --config "{shift_auto_shape_color: 1}" --nodata ``` ## Licence The entire content of this repository, including all image files, annotation files as well as has sourcecode, metadata and documentation has been published under the [Creative Commons Attribution Share Alike Licence 3.0](https://creativecommons.org/licenses/by-sa/3.0/).

davanstrien/MAMe2

davanstrien

--- dataset_info: config_name: '256' features: - name: image dtype: image - name: label dtype: class_label: names: '0': Albumen photograph '1': Bronze '2': Ceramic '3': Clay '4': Engraving '5': Etching '6': Faience '7': Glass '8': Gold '9': Graphite '10': Hand-colored engraving '11': Hand-colored etching '12': Iron '13': Ivory '14': Limestone '15': Lithograph '16': Marble '17': Oil on canvas '18': Pen and brown ink '19': Polychromed wood '20': Porcelain '21': Silk and metal thread '22': Silver '23': Steel '24': Wood '25': Wood engraving '26': Woodblock '27': Woodcut '28': Woven fabric - name: Museum dtype: string - name: Museum-based instance ID dtype: string - name: Width dtype: float32 - name: Height dtype: float32 - name: Product size dtype: float32 - name: Aspect ratio dtype: float32 splits: - name: train num_bytes: 441294458.5 num_examples: 20300 - name: validation num_bytes: 26810584.95 num_examples: 1450 - name: test num_bytes: 362018531.291 num_examples: 15657 download_size: 723376699 dataset_size: 830123574.7409999 configs: - config_name: '256' data_files: - split: train path: 256/train-* - split: validation path: 256/validation-* - split: test path: 256/test-* --- # Dataset Card for "MAMe2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

alkzar90/ddpm-rl-finetuning-evals

alkzar90

--- license: apache-2.0 size_categories: - 100K<n<1M pretty_name: evalhrf configs: - config_name: ddpm-celebahq data_files: - split: train path: data/ddpm-celebahq/train/batch_*.zip - config_name: ddpm-celebahq-mini data_files: - split: train path: data/celebahq-baseline-mini/train/batch_*.zip - config_name: ddpo-aesthetic data_files: - split: train path: data/ddpo-aesthetic-celebahq/train/batch_*.zip - config_name: ddpo-aesthetic-fix-lr data_files: - split: train path: data/ddpo-aesthetic-fix-lr/train/batch_*.zip - config_name: ddpo-compressibility data_files: - split: train path: data/ddpo-compressibility-celebahq/train/batch_*.zip - config_name: ddpo-compressibility-mini data_files: - split: train path: data/ddpo-compressibility-celebahq-mini/train/batch_*.zip - config_name: ddpo-incompressibility data_files: - split: train path: data/ddpo-incompressibility-celebahq/train/batch_*.zip - config_name: ddpo-incompressibility-mini data_files: - split: train path: data/ddpo-incompressibility-celebahq-mini/train/batch_*.zip - config_name: hrf-compressibility-window-baseline1 data_files: - split: train path: data/hrf-compressibility-window-baseline-seed1/train/batch_*.zip - config_name: hrf-compressibility-window-baseline2 data_files: - split: train path: data/hrf-compressibility-window-baseline-seed2/train/batch_*.zip - config_name: hrf-compressibility-window-baseline3 data_files: - split: train path: data/hrf-compressibility-window-baseline-seed3/train/batch_*.zip - config_name: hrf-compressibility-window-early1 data_files: - split: train path: data/hrf-compressibility-window-early-seed1/train/batch_*.zip - config_name: hrf-compressibility-window-early2 data_files: - split: train path: data/hrf-compressibility-window-early-seed2/train/batch_*.zip - config_name: hrf-compressibility-window-early3 data_files: - split: train path: data/hrf-compressibility-window-early-seed3/train/batch_*.zip - config_name: hrf-compressibility-window-later1 data_files: - split: train path: data/hrf-compressibility-window-later-seed1/train/batch_*.zip - config_name: hrf-compressibility-window-later2 data_files: - split: train path: data/hrf-compressibility-window-later-seed2/train/batch_*.zip - config_name: hrf-compressibility-window-later3 data_files: - split: train path: data/hrf-compressibility-window-later-seed3/train/batch_*.zip - config_name: hrf-incompressibility-window-baseline1 data_files: - split: train path: data/hrf-incompressibility-window-baseline-seed1/train/batch_*.zip - config_name: hrf-incompressibility-window-baseline2 data_files: - split: train path: data/hrf-incompressibility-window-baseline-seed2/train/batch_*.zip - config_name: hrf-incompressibility-window-baseline3 data_files: - split: train path: data/hrf-incompressibility-window-baseline-seed3/train/batch_*.zip - config_name: hrf-incompressibility-window-early1 data_files: - split: train path: data/hrf-incompressibility-window-early-seed1/train/batch_*.zip - config_name: hrf-incompressibility-window-early2 data_files: - split: train path: data/hrf-incompressibility-window-early-seed2/train/batch_*.zip - config_name: hrf-incompressibility-window-early3 data_files: - split: train path: data/hrf-incompressibility-window-early-seed3/train/batch_*.zip - config_name: hrf-incompressibility-window-later1 data_files: - split: train path: data/hrf-incompressibility-window-later-seed1/train/batch_*.zip - config_name: hrf-incompressibility-window-later2 data_files: - split: train path: data/hrf-incompressibility-window-later-seed2/train/batch_*.zip - config_name: hrf-incompressibility-window-later3 data_files: - split: train path: data/hrf-incompressibility-window-later-seed3/train/batch_*.zip - config_name: hrf-aesthetic-window-baseline1 data_files: - split: train path: data/hrf-aesthetic-window-baseline-seed1/train/batch_*.zip - config_name: hrf-aesthetic-window-baseline2 data_files: - split: train path: data/hrf-aesthetic-window-baseline-seed2/train/batch_*.zip - config_name: hrf-aesthetic-window-baseline3 data_files: - split: train path: data/hrf-aesthetic-window-baseline-seed3/train/batch_*.zip - config_name: hrf-aesthetic-window-early1 data_files: - split: train path: data/hrf-aesthetic-window-early-seed1/train/batch_*.zip - config_name: hrf-aesthetic-window-early2 data_files: - split: train path: data/hrf-aesthetic-window-early-seed2/train/batch_*.zip - config_name: hrf-aesthetic-window-early3 data_files: - split: train path: data/hrf-aesthetic-window-early-seed3/train/batch_*.zip - config_name: hrf-aesthetic-window-later1 data_files: - split: train path: data/hrf-aesthetic-window-later-seed1/train/batch_*.zip - config_name: hrf-aesthetic-window-later2 data_files: - split: train path: data/hrf-aesthetic-window-later-seed2/train/batch_*.zip - config_name: hrf-aesthetic-window-later3 data_files: - split: train path: data/hrf-aesthetic-window-later-seed3/train/batch_*.zip - config_name: hrf-aesthetic-window-adaptive data_files: - split: train path: data/hrf-aesthetic-window-adaptive/train/batch_*.zip - config_name: hrf-compressibility-window-adaptive data_files: - split: train path: data/hrf-compressibility-window-adaptive/train/batch_*.zip - config_name: hrf-incompressibility-window-adaptive data_files: - split: train path: data/hrf-incompressibility-window-adaptive/train/batch_*.zip - config_name: hrf-compressibility-window-adaptive2 data_files: - split: train path: data/hrf-compressibility-window-adaptive2/train/batch_*.zip - config_name: hrf-aesthetic-window-adaptive2 data_files: - split: train path: data/hrf-aesthetic-window-adaptive2/train/batch_*.zip dataset_info: - config_name: ddpm-celebahq features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-compressibility-window-baseline1 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-compressibility-window-baseline2 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-compressibility-window-baseline3 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-compressibility-window-early1 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-compressibility-window-early2 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-compressibility-window-early3 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-compressibility-window-later1 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-compressibility-window-later2 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-compressibility-window-later3 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-incompressibility-window-baseline1 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-incompressibility-window-baseline2 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-incompressibility-window-baseline3 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-incompressibility-window-early1 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-incompressibility-window-early2 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-incompressibility-window-early3 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-incompressibility-window-later1 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-incompressibility-window-later2 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-incompressibility-window-later3 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-aesthetic-window-baseline1 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-aesthetic-window-baseline2 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-aesthetic-window-baseline3 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-aesthetic-window-early1 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-aesthetic-window-early2 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-aesthetic-window-early3 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-aesthetic-window-later1 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-aesthetic-window-later2 features: - name: image dtype: image - name: reward dtype: float - config_name: hrf-aesthetic-window-later3 features: - name: image dtype: image - name: reward dtype: float - config_name: ddpo-aesthetic features: - name: image dtype: image - name: reward dtype: float - config_name: ddpo-compressibility features: - name: image dtype: image - name: reward dtype: float - config_name: ddpo-incompressibility features: - name: image dtype: image - name: reward dtype: float - config_name: ddpo-aesthetic-fix-lr features: - name: image dtype: image - name: reward dtype: float - config_name: ddpm-celebahq-mini features: - name: image dtype: image - config_name: ddpo-compressibility-mini features: - name: image dtype: image - config_name: ddpo-incompressibility-mini features: - name: image dtype: image - config_name: hrf-aesthetic-window-adaptive features: - name: image dtype: image - config_name: hrf-compressibility-window-adaptive features: - name: image dtype: image - config_name: hrf-incompressibility-window-adaptive features: - name: image dtype: image - config_name: hrf-aesthetic-window-adaptive2 features: - name: image dtype: image - config_name: hrf-compressibility-window-adaptive2 features: - name: image dtype: image --- # Dataset Card for Eval Finetuning Diffusion Models with Reinforcement Learning XYZ

Avelina/smollm-corpus

Avelina

--- license: odc-by dataset_info: - config_name: default features: - name: text dtype: string configs: - config_name: default data_files: - split: train path: data*/train-* task_categories: - text-generation language: - en size_categories: - 100M<n<1B --- # SmolLM-Corpus: Now shuffled and sharded! This is a version of the SmolLM-Corpus where the 3 subsets have been interleved, shuffled and sharded as 23698 `jsonl.zst` files for easy streaming! The dataset is comprised of the `cosmopedia-v2` and `fineweb-edu-dedup` subsets from the original [SmolLM-Corpus repo](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus), with the `python-edu` subset being pulled from my [python-edu repo](https://huggingface.co/datasets/Avelina/python-edu). ## Dataset Structure The dataset is split into 24 subdirectories, with the first 23 containing 1000 shards and the 24th containing the final 698. The repository is structured as follows: ``` data00/ ├── train-00000-of-23698.jsonl.zst ├── ... └── train-00999-of-23698.jsonl.zst data01/ ├── train-01000-of-23698.jsonl.zst ├── ... └── train-01999-of-23698.jsonl.zst ... data22/ ├── train-22000-of-23698.jsonl.zst ├── ... └── train-22999-of-23698.jsonl.zst data23/ ├── train-23000-of-23698.jsonl.zst ├── ... └── train-23697-of-23698.jsonl.zst ``` In general, you can obtain the exact download URL for all shards using the following python function: ```py def get_url_from_shard( index: int ) -> str: if index >= 23_698: raise ValueError( f'Shard index must be less than 23,698 but received {index}' ) group = index // 1000 return f'https://huggingface.co/datasets/Avelina/smollm-corpus/resolve/main/data{group:02d}/train-{index:05d}-of-23698.jsonl.zst' ``` ## Generation Code Here is the code which was used to generate the shuffled shards. Note the use of non-contiguous interleaving in attempt to uniformly pull documents from across entire subsets to loosely decouple shard index from original document position. Please make sure you `pip install zstandard`!!! ```py import tqdm import datasets from datasets import load_dataset # Output directory and file format. Note that the file extension enforces zst compression is used. OUTPUT_FMT = '/YOUR/FILE/PATH/HERE/data/train-{index:05d}-of-{num_shards:05d}.jsonl.zst' # Total number of shards giving approximately 10,000 documents per shard OUTPUT_NUM_SHARDS = 23698 # Grab the three datasets ds_python = load_dataset( 'Avelina/python-edu' ) ds_cosmo = load_dataset( 'HuggingFaceTB/smollm-corpus', 'cosmopedia-v2' ) ds_edu = load_dataset( 'HuggingFaceTB/smollm-corpus', 'fineweb-edu-dedup' ) # Retain only the text columns and the train splits ds_python = ds_python.select_columns( 'text' )[ 'train' ] ds_cosmo = ds_cosmo.select_columns( 'text' )[ 'train' ] ds_edu = ds_edu.select_columns( 'text' )[ 'train' ] # Iterate over all shards with a nice progbar for index in tqdm.tqdm( range( OUTPUT_NUM_SHARDS ) ): # Get non-contiguous in-memory sub-shards for the three datasets curr_python = ds_python.shard( num_shards=OUTPUT_NUM_SHARDS, index=index, contiguous=False, keep_in_memory=True ) curr_cosmo = ds_cosmo.shard( num_shards=OUTPUT_NUM_SHARDS, index=index, contiguous=False, keep_in_memory=True ) curr_edu = ds_edu.shard( num_shards=OUTPUT_NUM_SHARDS, index=index, contiguous=False, keep_in_memory=True ) # Concatenate the sub-shards curr_shard = datasets.concatenate_datasets( [ curr_python, curr_cosmo, curr_edu ] ) # Deterministically shuffle using the current shard index for reproducibility curr_shard = curr_shard.shuffle( seed=index, keep_in_memory=True ) # Dump the shards to .jsonl.zst curr_shard.to_json( OUTPUT_FMT.format( index=index, num_shards=OUTPUT_NUM_SHARDS ) ) ``` ## In-Memory Decompression Zstandard was chosen as it enables trivial in-memory decompression to minimise the storage impact of the dataset. Here is some example code which creates a python generator that yields each json line from a compressed shard stored at `file_name`, and a second function which creates a python generator that parses and yields the compressed shard. ```py import json from json import JSONDecodeError import zstandard def read_lines_zst( file_name ): # Open the file for reading in binary mode with open( file_name, 'rb' ) as file_handle: # Initialise an empty buffer buffer = '' # Create a reader for the opened file reader = zstandard.ZstdDecompressor( max_window_size=2**31 ).stream_reader( file_handle ) while True: # Read a chunk of up to 128MB chunk = reader.read( 2**27 ).decode() # If chunk is empty we've reached the end of the file and can break out if not chunk: break # Combine any prior buffer with the current chunk and split by newline lines = ( buffer + chunk ).split( '\n' ) # Yield the full lines so far for line in lines[ : -1 ]: yield line # The last 'line' is incomplete, so place in buffer for next chunk buffer = lines[ -1 ] # Always remember to close your reader! reader.close() def parse_jsonl_zst( file_name ): # Iterate over the yielded lines of the compressed shard for i, line in enumerate( read_lines_zst( file_name ) ): try: # Convert the line into a python dict and yield the text field yield json.loads( line )[ 'text' ] except ( KeyError, JSONDecodeError ): # Catch KeyError for 'text' not present in dict # Catch JSONDecodeError for malformed line print( f'JSON error @ shard={file_name}, line={i}' ) ``` Of course you *could* use HuggingFace's in-built streaming mechanics to handle things for you, but in my experience that approach is less reliable, doesn't handle `JSONDecodeError`s if there are malformed lines, can cause memory leaks, and has forced sharding behaviour when used inside a multi-worker PyTorch `DataLoader` which I've not yet found a way to disable!

NeelNanda/pile-10k

NeelNanda

--- license: bigscience-bloom-rail-1.0 --- The first 10K elements of [The Pile](https://pile.eleuther.ai/), useful for debugging models trained on it. See the [HuggingFace page for the full Pile](https://huggingface.co/datasets/the_pile) for more info. Inspired by [stas' great resource](https://huggingface.co/datasets/stas/openwebtext-10k) doing the same for OpenWebText

lmms-lab/VizWiz-VQA

lmms-lab

--- dataset_info: features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answers sequence: string - name: category dtype: string splits: - name: val num_bytes: 2097998373.0 num_examples: 4319 - name: test num_bytes: 3982325314.0 num_examples: 8000 download_size: 6050372614 dataset_size: 6080323687.0 --- # Dataset Card for "VizWiz-VQA" <p align="center" width="100%"> <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> </p> # Large-scale Multi-modality Models Evaluation Suite > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) # This Dataset This is a formatted version of [VizWiz-VQA](https://vizwiz.org/tasks-and-datasets/vqa/). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. ``` @inproceedings{gurari2018vizwiz, title={Vizwiz grand challenge: Answering visual questions from blind people}, author={Gurari, Danna and Li, Qing and Stangl, Abigale J and Guo, Anhong and Lin, Chi and Grauman, Kristen and Luo, Jiebo and Bigham, Jeffrey P}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={3608--3617}, year={2018} } ``` [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

eduagarcia-temp/OSCAR-2301_meta

eduagarcia-temp

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: meta struct: - name: categories sequence: string - name: dedup struct: - name: exact_norm struct: - name: cluster_main_idx dtype: int64 - name: cluster_size dtype: int64 - name: exact_hash_idx dtype: int64 - name: is_duplicate dtype: bool - name: minhash struct: - name: cluster_main_idx dtype: int64 - name: cluster_size dtype: int64 - name: is_duplicate dtype: bool - name: minhash_idx dtype: int64 - name: harmful_pp dtype: float64 - name: identification struct: - name: label dtype: string - name: prob dtype: float64 - name: quality_warnings sequence: string - name: sentence_identifications list: - name: label dtype: string - name: prob dtype: float64 - name: tlsh dtype: string - name: warc_headers struct: - name: content-length dtype: int64 - name: content-type dtype: string - name: warc-block-digest dtype: string - name: warc-date dtype: string - name: warc-identified-content-language dtype: string - name: warc-record-id dtype: string - name: warc-refers-to dtype: string - name: warc-target-uri dtype: string - name: warc-type dtype: string splits: - name: train num_bytes: 127702717461 num_examples: 18031400 download_size: 40317121912 dataset_size: 127702717461 --- # Dataset Card for "OSCAR-2301_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

klue/klue

klue

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ko license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - fill-mask - question-answering - text-classification - text-generation - token-classification task_ids: - extractive-qa - named-entity-recognition - natural-language-inference - parsing - semantic-similarity-scoring - text-scoring - topic-classification paperswithcode_id: klue pretty_name: KLUE config_names: - dp - mrc - ner - nli - re - sts - wos - ynat tags: - relation-extraction dataset_info: - config_name: dp features: - name: sentence dtype: string - name: index list: int32 - name: word_form list: string - name: lemma list: string - name: pos list: string - name: head list: int32 - name: deprel list: string splits: - name: train num_bytes: 7899965 num_examples: 10000 - name: validation num_bytes: 1557462 num_examples: 2000 download_size: 3742577 dataset_size: 9457427 - config_name: mrc features: - name: title dtype: string - name: context dtype: string - name: news_category dtype: string - name: source dtype: string - name: guid dtype: string - name: is_impossible dtype: bool - name: question_type dtype: int32 - name: question dtype: string - name: answers sequence: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 46505593 num_examples: 17554 - name: validation num_bytes: 15583017 num_examples: 5841 download_size: 30098472 dataset_size: 62088610 - config_name: ner features: - name: sentence dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-DT '1': I-DT '2': B-LC '3': I-LC '4': B-OG '5': I-OG '6': B-PS '7': I-PS '8': B-QT '9': I-QT '10': B-TI '11': I-TI '12': O splits: - name: train num_bytes: 19891905 num_examples: 21008 - name: validation num_bytes: 4937563 num_examples: 5000 download_size: 5265887 dataset_size: 24829468 - config_name: nli features: - name: guid dtype: string - name: source dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: train num_bytes: 5719882 num_examples: 24998 - name: validation num_bytes: 673260 num_examples: 3000 download_size: 2056116 dataset_size: 6393142 - config_name: re features: - name: guid dtype: string - name: sentence dtype: string - name: subject_entity struct: - name: word dtype: string - name: start_idx dtype: int32 - name: end_idx dtype: int32 - name: type dtype: string - name: object_entity struct: - name: word dtype: string - name: start_idx dtype: int32 - name: end_idx dtype: int32 - name: type dtype: string - name: label dtype: class_label: names: '0': no_relation '1': org:dissolved '2': org:founded '3': org:place_of_headquarters '4': org:alternate_names '5': org:member_of '6': org:members '7': org:political/religious_affiliation '8': org:product '9': org:founded_by '10': org:top_members/employees '11': org:number_of_employees/members '12': per:date_of_birth '13': per:date_of_death '14': per:place_of_birth '15': per:place_of_death '16': per:place_of_residence '17': per:origin '18': per:employee_of '19': per:schools_attended '20': per:alternate_names '21': per:parents '22': per:children '23': per:siblings '24': per:spouse '25': per:other_family '26': per:colleagues '27': per:product '28': per:religion '29': per:title - name: source dtype: string splits: - name: train num_bytes: 11145426 num_examples: 32470 - name: validation num_bytes: 2559272 num_examples: 7765 download_size: 8190257 dataset_size: 13704698 - config_name: sts features: - name: guid dtype: string - name: source dtype: string - name: sentence1 dtype: string - name: sentence2 dtype: string - name: labels struct: - name: label dtype: float64 - name: real-label dtype: float64 - name: binary-label dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 2832889 num_examples: 11668 - name: validation num_bytes: 122641 num_examples: 519 download_size: 1587855 dataset_size: 2955530 - config_name: wos features: - name: guid dtype: string - name: domains list: string - name: dialogue list: - name: role dtype: string - name: text dtype: string - name: state list: string splits: - name: train num_bytes: 26676970 num_examples: 8000 - name: validation num_bytes: 3488911 num_examples: 1000 download_size: 6358855 dataset_size: 30165881 - config_name: ynat features: - name: guid dtype: string - name: title dtype: string - name: label dtype: class_label: names: '0': IT과학 '1': 경제 '2': 사회 '3': 생활문화 '4': 세계 '5': 스포츠 '6': 정치 - name: url dtype: string - name: date dtype: string splits: - name: train num_bytes: 10109584 num_examples: 45678 - name: validation num_bytes: 2039181 num_examples: 9107 download_size: 5012303 dataset_size: 12148765 configs: - config_name: dp data_files: - split: train path: dp/train-* - split: validation path: dp/validation-* - config_name: mrc data_files: - split: train path: mrc/train-* - split: validation path: mrc/validation-* - config_name: ner data_files: - split: train path: ner/train-* - split: validation path: ner/validation-* - config_name: nli data_files: - split: train path: nli/train-* - split: validation path: nli/validation-* - config_name: re data_files: - split: train path: re/train-* - split: validation path: re/validation-* - config_name: sts data_files: - split: train path: sts/train-* - split: validation path: sts/validation-* - config_name: wos data_files: - split: train path: wos/train-* - split: validation path: wos/validation-* - config_name: ynat data_files: - split: train path: ynat/train-* - split: validation path: ynat/validation-* --- # Dataset Card for KLUE ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://klue-benchmark.com/ - **Repository:** https://github.com/KLUE-benchmark/KLUE - **Paper:** [KLUE: Korean Language Understanding Evaluation](https://arxiv.org/abs/2105.09680) - **Leaderboard:** [Leaderboard](https://klue-benchmark.com/leaderboard) - **Point of Contact:** https://github.com/KLUE-benchmark/KLUE/issues ### Dataset Summary KLUE is a collection of 8 tasks to evaluate natural language understanding capability of Korean language models. We delibrately select the 8 tasks, which are Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking. ### Supported Tasks and Leaderboards Topic Classification, Semantic Textual Similarity, Natural Language Inference, Named Entity Recognition, Relation Extraction, Dependency Parsing, Machine Reading Comprehension, and Dialogue State Tracking ### Languages `ko-KR` ## Dataset Structure ### Data Instances #### ynat An example of 'train' looks as follows. ``` {'date': '2016.06.30. 오전 10:36', 'guid': 'ynat-v1_train_00000', 'label': 3, 'title': '유튜브 내달 2일까지 크리에이터 지원 공간 운영', 'url': 'https://news.naver.com/main/read.nhn?mode=LS2D&mid=shm&sid1=105&sid2=227&oid=001&aid=0008508947'} ``` #### sts An example of 'train' looks as follows. ``` {'guid': 'klue-sts-v1_train_00000', 'labels': {'label': 3.7, 'real-label': 3.714285714285714, 'binary-label': 1}, 'sentence1': '숙소 위치는 찾기 쉽고 일반적인 한국의 반지하 숙소입니다.', 'sentence2': '숙박시설의 위치는 쉽게 찾을 수 있고 한국의 대표적인 반지하 숙박시설입니다.', 'source': 'airbnb-rtt'} ``` #### nli An example of 'train' looks as follows. ``` {'guid': 'klue-nli-v1_train_00000', 'hypothesis': '힛걸 진심 최고로 멋지다.', 'label': 0, 'premise': '힛걸 진심 최고다 그 어떤 히어로보다 멋지다', 'source': 'NSMC'} ``` #### ner An example of 'train' looks as follows. ``` {'tokens': ['특', '히', ' ', '영', '동', '고', '속', '도', '로', ' ', '강', '릉', ' ', '방', '향', ' ', '문', '막', '휴', '게', '소', '에', '서', ' ', '만', '종', '분', '기', '점', '까', '지', ' ', '5', '㎞', ' ', '구', '간', '에', '는', ' ', '승', '용', '차', ' ', '전', '용', ' ', '임', '시', ' ', '갓', '길', '차', '로', '제', '를', ' ', '운', '영', '하', '기', '로', ' ', '했', '다', '.'], 'ner_tags': [12, 12, 12, 2, 3, 3, 3, 3, 3, 12, 2, 3, 12, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 2, 3, 3, 3, 3, 12, 12, 12, 8, 9, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12], 'sentence': '특히 <영동고속도로:LC> <강릉:LC> 방향 <문막휴게소:LC>에서 <만종분기점:LC>까지 <5㎞:QT> 구간에는 승용차 전용 임시 갓길차로제를 운영하기로 했다.'} ``` #### re An example of 'train' looks as follows. ``` {'guid': 'klue-re-v1_train_00000', 'label': 0, 'object_entity': {'word': '조지 해리슨', 'start_idx': 13, 'end_idx': 18, 'type': 'PER'}, 'sentence': '〈Something〉는 조지 해리슨이 쓰고 비틀즈가 1969년 앨범 《Abbey Road》에 담은 노래다.', 'source': 'wikipedia', 'subject_entity': {'word': '비틀즈', 'start_idx': 24, 'end_idx': 26, 'type': 'ORG'}} ``` #### dp An example of 'train' looks as follows. ``` {'deprel': ['NP', 'NP_OBJ', 'VP', 'NP', 'NP_SBJ', 'NP', 'NP_MOD', 'NP_CNJ', 'NP_CNJ', 'NP', 'NP', 'NP_OBJ', 'AP', 'VP'], 'head': [2, 3, 14, 5, 14, 7, 10, 10, 10, 11, 12, 14, 14, 0], 'index': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], 'lemma': ['해당', '그림 을', '보 면', '디즈니', '공주 들 이', '브리트니', '스피어스 의', '앨범 이나', '뮤직 비디오 ,', '화보', '속', '모습 을', '똑같이', '재연 하 였 다 .'], 'pos': ['NNG', 'NNG+JKO', 'VV+EC', 'NNP', 'NNG+XSN+JKS', 'NNP', 'NNP+JKG', 'NNG+JC', 'NNG+NNG+SP', 'NNG', 'NNG', 'NNG+JKO', 'MAG', 'NNG+XSA+EP+EF+SF'], 'sentence': '해당 그림을 보면 디즈니 공주들이 브리트니 스피어스의 앨범이나 뮤직비디오, 화보 속 모습을 똑같이 재연했다.', 'word_form': ['해당', '그림을', '보면', '디즈니', '공주들이', '브리트니', '스피어스의', '앨범이나', '뮤직비디오,', '화보', '속', '모습을', '똑같이', '재연했다.']} ``` #### mrc An example of 'train' looks as follows. ``` {'answers': {'answer_start': [478, 478], 'text': ['한 달가량', '한 달']}, 'context': '올여름 장마가 17일 제주도에서 시작됐다. 서울 등 중부지방은 예년보다 사나흘 정도 늦은 이달 말께 장마가 시작될 전망이다.17일 기상청에 따르면 제주도 남쪽 먼바다에 있는 장마전선의 영향으로 이날 제주도 산간 및 내륙지역에 호우주의보가 내려지면서 곳곳에 100㎜에 육박하는 많은 비가 내렸다. 제주의 장마는 평년보다 2~3일, 지난해보다는 하루 일찍 시작됐다. 장마는 고온다습한 북태평양 기단과 한랭 습윤한 오호츠크해 기단이 만나 형성되는 장마전선에서 내리는 비를 뜻한다.장마전선은 18일 제주도 먼 남쪽 해상으로 내려갔다가 20일께 다시 북상해 전남 남해안까지 영향을 줄 것으로 보인다. 이에 따라 20~21일 남부지방에도 예년보다 사흘 정도 장마가 일찍 찾아올 전망이다. 그러나 장마전선을 밀어올리는 북태평양 고기압 세력이 약해 서울 등 중부지방은 평년보다 사나흘가량 늦은 이달 말부터 장마가 시작될 것이라는 게 기상청의 설명이다. 장마전선은 이후 한 달가량 한반도 중남부를 오르내리며 곳곳에 비를 뿌릴 전망이다. 최근 30년간 평균치에 따르면 중부지방의 장마 시작일은 6월24~25일이었으며 장마기간은 32일, 강수일수는 17.2일이었다.기상청은 올해 장마기간의 평균 강수량이 350~400㎜로 평년과 비슷하거나 적을 것으로 내다봤다. 브라질 월드컵 한국과 러시아의 경기가 열리는 18일 오전 서울은 대체로 구름이 많이 끼지만 비는 오지 않을 것으로 예상돼 거리 응원에는 지장이 없을 전망이다.', 'guid': 'klue-mrc-v1_train_12759', 'is_impossible': False, 'news_category': '종합', 'question': '북태평양 기단과 오호츠크해 기단이 만나 국내에 머무르는 기간은?', 'question_type': 1, 'source': 'hankyung', 'title': '제주도 장마 시작 … 중부는 이달 말부터'} ``` #### wos An example of 'train' looks as follows. ``` {'dialogue': [{'role': 'user', 'text': '쇼핑을 하려는데 서울 서쪽에 있을까요?', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽']}, {'role': 'sys', 'text': '서울 서쪽에 쇼핑이 가능한 곳이라면 노량진 수산물 도매시장이 있습니다.', 'state': []}, {'role': 'user', 'text': '오 네 거기 주소 좀 알려주세요.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '노량진 수산물 도매시장의 주소는 서울 동작구 93806입니다.', 'state': []}, {'role': 'user', 'text': '알려주시는김에 연락처랑 평점도 좀 알려주세요.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '그럼. 연락처는 6182006591이고 평점은 4점입니다.', 'state': []}, {'role': 'user', 'text': '와 감사합니다.', 'state': ['관광-종류-쇼핑', '관광-지역-서울 서쪽', '관광-이름-노량진 수산물 도매시장']}, {'role': 'sys', 'text': '감사합니다.', 'state': []}], 'domains': ['관광'], 'guid': 'wos-v1_train_00001'} ``` ### Data Fields #### ynat + `guid`: a `string` feature + `title`: a `string` feature + `label`: a classification label, with possible values `IT과학`(0), `경제`(1), `사회`(2), `생활문화`(3), `세계`(4), `스포츠`(5), `정치`(6) + `url`: a `string` feature + `date`: a `string` feature #### sts + `guid`: a `string` feature + `source`: a `string` feature + `sentence1`: a `string` feature + `sentence2`: a `string` feature + `labels`: a dictionary feature containing + `label`: a `float64` feature + `real-label`: a `float64` feature + `binary-label`: a classification label, with possible values `negative`(0), `positive`(1) #### nli + `guid`: a `string` feature + `source`: a `string` feature + `premise`: a `string` feature + `hypothesis`: a `string` feature + `label`: a classification label, with possible values `entailment`(0), `neutral`(1), `contradiction`(2) #### ner + `sentence`: a `string` feature + `tokens`: a list of a `string` feature (tokenization is at character level) + `ner_tags`: a list of classification labels, with possible values including `B-DT`(0), `I-DT`(1), `B-LC`(2), `I-LC`(3), `B-OG`(4), `I-OG`(5), `B-PS`(6), `I-PS`(7), `B-QT`(8), `I-QT`(9), `B-TI`(10), `I-TI`(11), `O`(12) #### re + `guid`: a `string` feature + `sentence`: a `string` feature + `subject_entity`: a dictionary feature containing + `word`: a `string` feature + `start_idx`: a `int32` feature + `end_idx`: a `int32` feature + `type`: a `string` feature + `object_entity`: a dictionary feature containing + `word`: a `string` feature + `start_idx`: a `int32` feature + `end_idx`: a `int32` feature + `type`: a `string` feature + `label`: a list of labels, with possible values including `no_relation`(0), `org:dissolved`(1), `org:founded`(2), `org:place_of_headquarters`(3), `org:alternate_names`(4), `org:member_of`(5), `org:members`(6), `org:political/religious_affiliation`(7), `org:product`(8), `org:founded_by`(9),`org:top_members/employees`(10), `org:number_of_employees/members`(11), `per:date_of_birth`(12), `per:date_of_death`(13), `per:place_of_birth`(14), `per:place_of_death`(15), `per:place_of_residence`(16), `per:origin`(17), `per:employee_of`(18), `per:schools_attended`(19), `per:alternate_names`(20), `per:parents`(21), `per:children`(22), `per:siblings`(23), `per:spouse`(24), `per:other_family`(25), `per:colleagues`(26), `per:product`(27), `per:religion`(28), `per:title`(29), + `source`: a `string` feature #### dp + `sentence`: a `string` feature + `index`: a list of `int32` feature + `word_form`: a list of `string` feature + `lemma`: a list of `string` feature + `pos`: a list of `string` feature + `head`: a list of `int32` feature + `deprel`: a list of `string` feature #### mrc + `title`: a `string` feature + `context`: a `string` feature + `news_category`: a `string` feature + `source`: a `string` feature + `guid`: a `string` feature + `is_impossible`: a `bool` feature + `question_type`: a `int32` feature + `question`: a `string` feature + `answers`: a dictionary feature containing + `answer_start`: a `int32` feature + `text`: a `string` feature #### wos + `guid`: a `string` feature + `domains`: a `string` feature + `dialogue`: a list of dictionary feature containing + `role`: a `string` feature + `text`: a `string` feature + `state`: a `string` feature ### Data Splits #### ynat You can see more details in [here](https://klue-benchmark.com/tasks/66/data/description). + train: 45,678 + validation: 9,107 #### sts You can see more details in [here](https://klue-benchmark.com/tasks/67/data/description). + train: 11,668 + validation: 519 #### nli You can see more details in [here](https://klue-benchmark.com/tasks/68/data/description). + train: 24,998 + validation: 3,000 #### ner You can see more details in [here](https://klue-benchmark.com/tasks/69/overview/description). + train: 21,008 + validation: 5,000 #### re You can see more details in [here](https://klue-benchmark.com/tasks/70/overview/description). + train: 32,470 + validation: 7,765 #### dp You can see more details in [here](https://klue-benchmark.com/tasks/71/data/description). + train: 10,000 + validation: 2,000 #### mrc You can see more details in [here](https://klue-benchmark.com/tasks/72/overview/description). + train: 17,554 + validation: 5,841 #### wos You can see more details in [here](https://klue-benchmark.com/tasks/73/overview/description). + train: 8,000 + validation: 1,000 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @misc{park2021klue, title={KLUE: Korean Language Understanding Evaluation}, author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jungwoo Ha and Kyunghyun Cho}, year={2021}, eprint={2105.09680}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@jungwhank](https://github.com/jungwhank), [@bzantium](https://github.com/bzantium) for adding this dataset.

PolyAI/minds14

PolyAI

--- annotations_creators: - expert-generated - crowdsourced - machine-generated language_creators: - crowdsourced - expert-generated language: - en - fr - it - es - pt - de - nl - ru - pl - cs - ko - zh license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K task_categories: - automatic-speech-recognition task_ids: - keyword-spotting pretty_name: MInDS-14 language_bcp47: - en - en-GB - en-US - en-AU - fr - it - es - pt - de - nl - ru - pl - cs - ko - zh tags: - speech-recognition --- # MInDS-14 ## Dataset Description - **Fine-Tuning script:** [pytorch/audio-classification](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification) - **Paper:** [Multilingual and Cross-Lingual Intent Detection from Spoken Data](https://arxiv.org/abs/2104.08524) - **Total amount of disk used:** ca. 500 MB MINDS-14 is training and evaluation resource for intent detection task with spoken data. It covers 14 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties. ## Example MInDS-14 can be downloaded and used as follows: ```py from datasets import load_dataset minds_14 = load_dataset("PolyAI/minds14", "fr-FR") # for French # to download all data for multi-lingual fine-tuning uncomment following line # minds_14 = load_dataset("PolyAI/all", "all") # see structure print(minds_14) # load audio sample on the fly audio_input = minds_14["train"][0]["audio"] # first decoded audio sample intent_class = minds_14["train"][0]["intent_class"] # first transcription intent = minds_14["train"].features["intent_class"].names[intent_class] # use audio_input and language_class to fine-tune your model for audio classification ``` ## Dataset Structure We show detailed information the example configurations `fr-FR` of the dataset. All other configurations have the same structure. ### Data Instances **fr-FR** - Size of downloaded dataset files: 471 MB - Size of the generated dataset: 300 KB - Total amount of disk used: 471 MB An example of a datainstance of the config `fr-FR` looks as follows: ``` { "path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav", "audio": { "path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav", "array": array( [0.0, 0.0, 0.0, ..., 0.0, 0.00048828, -0.00024414], dtype=float32 ), "sampling_rate": 8000, }, "transcription": "je souhaite changer mon adresse", "english_transcription": "I want to change my address", "intent_class": 1, "lang_id": 6, } ``` ### Data Fields The data fields are the same among all splits. - **path** (str): Path to the audio file - **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio - **transcription** (str): Transcription of the audio file - **english_transcription** (str): English transcription of the audio file - **intent_class** (int): Class id of intent - **lang_id** (int): Id of language ### Data Splits Every config only has the `"train"` split containing of *ca.* 600 examples. ## Dataset Creation [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). ### Citation Information ``` @article{DBLP:journals/corr/abs-2104-08524, author = {Daniela Gerz and Pei{-}Hao Su and Razvan Kusztos and Avishek Mondal and Michal Lis and Eshan Singhal and Nikola Mrksic and Tsung{-}Hsien Wen and Ivan Vulic}, title = {Multilingual and Cross-Lingual Intent Detection from Spoken Data}, journal = {CoRR}, volume = {abs/2104.08524}, year = {2021}, url = {https://arxiv.org/abs/2104.08524}, eprinttype = {arXiv}, eprint = {2104.08524}, timestamp = {Mon, 26 Apr 2021 17:25:10 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-08524.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset

zh-plus/tiny-imagenet

zh-plus

--- annotations_creators: - crowdsourced extra_gated_prompt: "By clicking on \u201CAccess repository\u201D below, you also\ \ agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\"\ ) has requested permission to use the ImageNet database (the \"Database\") at Princeton\ \ University and Stanford University. In exchange for such permission, Researcher\ \ hereby agrees to the following terms and conditions:\n1. Researcher shall use\ \ the Database only for non-commercial research and educational purposes.\n2. Princeton\ \ University, Stanford University and Hugging Face make no representations or warranties\ \ regarding the Database, including but not limited to warranties of non-infringement\ \ or fitness for a particular purpose.\n3. Researcher accepts full responsibility\ \ for his or her use of the Database and shall defend and indemnify the ImageNet\ \ team, Princeton University, Stanford University and Hugging Face, including their\ \ employees, Trustees, officers and agents, against any and all claims arising from\ \ Researcher's use of the Database, including but not limited to Researcher's use\ \ of any copies of copyrighted images that he or she may create from the Database.\n\ 4. Researcher may provide research associates and colleagues with access to the\ \ Database provided that they first agree to be bound by these terms and conditions.\n\ 5. Princeton University, Stanford University and Hugging Face reserve the right\ \ to terminate Researcher's access to the Database at any time.\n6. If Researcher\ \ is employed by a for-profit, commercial entity, Researcher's employer shall also\ \ be bound by these terms and conditions, and Researcher hereby represents that\ \ he or she is fully authorized to enter into this agreement on behalf of such employer.\n\ 7. The law of the State of New Jersey shall apply to all disputes under this agreement." language: - en language_creators: - crowdsourced license: [] multilinguality: - monolingual paperswithcode_id: imagenet pretty_name: Tiny-ImageNet size_categories: - 100K<n<1M source_datasets: - extended|imagenet-1k task_categories: - image-classification task_ids: - multi-class-image-classification --- # Dataset Card for tiny-imagenet ## Dataset Description - **Homepage:** https://www.kaggle.com/c/tiny-imagenet - **Repository:** [Needs More Information] - **Paper:** http://cs231n.stanford.edu/reports/2017/pdfs/930.pdf - **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-tiny-imagenet-1 ### Dataset Summary Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images. ### Languages The class labels in the dataset are in English. ## Dataset Structure ### Data Instances ```json { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190, 'label': 15 } ``` ### Data Fields - image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0]. - label: an int classification label. -1 for test set as the labels are missing. Check `classes.py` for the map of numbers & labels. ### Data Splits | | Train | Valid | | ------------ | ------ | ----- | | # of samples | 100000 | 10000 | ## Usage ### Example #### Load Dataset ```python def example_usage(): tiny_imagenet = load_dataset('Maysee/tiny-imagenet', split='train') print(tiny_imagenet[0]) if __name__ == '__main__': example_usage() ```

Omartificial-Intelligence-Space/FineWeb2-MSA

Omartificial-Intelligence-Space

--- license: odc-by language: - ar tags: - arabicf - fineweb - MSA pretty_name: FineWeb2 MSA size_categories: - 10M<n<100M --- # FineWeb2 MSA Arabic  This is the MSA Arabic Portion of The [FineWeb2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2#additional-information) Dataset. This dataset contains a rich collection of text in **MSA Arabic** (ISO 639-3: arz), a widely spoken dialect within the Afro-Asiatic language family. With over **439 million words** and **1.4 million** documents, it serves as a valuable resource for NLP development and linguistic research focused on Egyptian Arabic. ## Purpose of This Repository This repository provides easy access to the **Arabic portion - MSA** of the extensive **FineWeb2** dataset. My primary goal is to make this valuable data more accessible and impactful for researchers, developers, and anyone working on **Arabic** natural language processing (NLP) projects. By focusing on Arabic, I aim to: - **Simplify Access**: Provide a direct and streamlined way to download the Arabic portion of the dataset without navigating through the larger collection. - **Promote Research**: Enable more efficient use of Arabic text data for NLP, LLMs, and linguistic research. - **Empower the Community**: Support Arabic language processing and contribute to the growth of multilingual NLP capabilities. - **Encourage Collaboration**: Foster an environment where researchers and developers can build impactful applications using Arabic data. ## Credit to the Original Work The dataset is released under the [Open Data Commons Attribution License (ODC-By) v1.0](https://opendatacommons.org/licenses/by/1-0/), with additional usage subject to CommonCrawl's Terms of Use.. ### Citation If you use this dataset, please cite it as follows: ```bibtex @software{penedo2024fineweb-2, author = {Penedo, Guilherme and Kydlíček, Hynek and Sabolčec, Vinko and Messmer, Bettina and Foroutan, Negar and Jaggi, Martin and von Werra, Leandro and Wolf, Thomas}, title = {FineWeb2: A sparkling update with 1000s of languages}, month = dec, year = 2024, doi = {10.57967/hf/3744}, url = {https://huggingface.co/datasets/HuggingFaceFW/fineweb-2} }

JetBrains-Research/commit-chronicle

JetBrains-Research

--- license: other language: - code - en task_categories: - text-generation - summarization tags: - code - commit_message_generation pretty_name: CommitChronicle size_categories: - 1M<n<10M dataset_info: - config_name: default features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 5760117409 num_examples: 1486267 - name: train num_bytes: 30084265848 num_examples: 7659458 - name: validation num_bytes: 5905326070 num_examples: 1554042 download_size: 14168436205 dataset_size: 41749709327 - config_name: subset_cmg features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 772774959 num_examples: 204336 download_size: 258151047 dataset_size: 772774959 - config_name: subset_llm features: - name: author dtype: int64 - name: date dtype: string - name: timezone dtype: int64 - name: hash dtype: string - name: message dtype: string - name: mods list: - name: change_type dtype: string - name: old_path dtype: string - name: new_path dtype: string - name: diff dtype: string - name: language dtype: string - name: license dtype: string - name: repo dtype: string - name: original_message dtype: string splits: - name: test num_bytes: 15121048 num_examples: 4025 download_size: 5068039 dataset_size: 15121048 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* - config_name: subset_cmg data_files: - split: test path: subset_cmg/test-* - config_name: subset_llm data_files: - split: test path: subset_llm/test-* --- # 📜 CommitChronicle 🔮 This is the dataset for commit message generation (and/or completion), introduced in the paper "From Commit Message Generation to History-Aware Commit Message Completion", ASE 2023. Its key features: * *large-scale and multilingual*: contains 10.7M commits from 11.9k GitHub repositories in 20 programming languages; * *diverse*: avoids restrictive filtering on commit messages or commit diffs structure; * *suitable for experiments with commit history*: provides metadata about commit authors and dates and uses split-by-project. ## Dataset Creation > 🔍 For further details, please refer to: > * **Paper**: [https://arxiv.org/abs/2308.07655](https://arxiv.org/abs/2308.07655) > * **Repository**: [https://github.com/JetBrains-Research/commit_message_generation](https://github.com/JetBrains-Research/commit_message_generation) We used [GitHub Search](https://seart-ghs.si.usi.ch/) tool and official GitHub API to select relevant repositories with permissive licenses (Apache, BSD 3-clause, MIT). On February 9th, 2023, we collected all commits made since 2017 from these repositories via [PyDriller](https://github.com/ishepard/pydriller). Next, we extensively cleaned the data, including filtering outliers, dropping commits from bot authors, and dropping duplicates. Note: to avoid disclosing personal information, we replaced the commit authors' names and emails with unique identifiers. ## Dataset Structure ### Data Instances Each data instance in the dataset is a commit. [A commit example](https://github.com/saridormi/commit_chronicle/commit/a7fb3b64184f0af5b08285cce14b9139baa94049) would look like the following: ``` { 'repo': 'saridormi/commit_chronicle', 'hash': 'a7fb3b64184f0af5b08285cce14b9139baa94049', 'author': 123, 'date': '05.07.2021 15:10:07', 'timezone': 0, 'license': 'MIT License', 'language': 'Jupyter Notebook', 'message': 'Add license badge to readme', 'original_message': 'Add license badge to readme', 'mods': [{'change_type': 'MODIFY', 'new_path': 'README.md', 'old_path': 'README.md' 'diff': '@@ -1,6 +1,6 @@\n' ' # Commits dataset\n' ' \n' '-> :heavy_exclamation_mark: **TODO:** license\n' '+\n'}], } ``` ### Data Fields Each example has the following fields: | **Field** | **Description** | |:------------------:|:----------------------------------------:| | `repo` | Commit repository. | | `hash` | Commit hash. | | `author` | Unique id for commit author | | `date` | Commit date (from author). | | `timezone` | Commit timezone (from author). | | `license` | Commit repository's license. | | `language` | Commit repository's main language. | | `message` | Commit message (after processing). | | `original_message` | Commit message (without any processing). | | `mods` | List of file modifications from commit. | Each file modification has the following fields: | **Field** | **Description** | |:-------------:|:-------------------------------------------------------------------------------------------------:| | `change_type` | Type of change to current file. One of: `ADD`, `COPY`, `RENAME`, `DELETE`, `MODIFY` or `UNKNOWN`. | | `old_path` | Path to file before change (might be empty). | | `new_path` | Path to file after change (might be empty). | | `diff` | `git diff` for current file. | ### Data Splits We provide the following configurations: * `default` * `train`: full training split (7.66M commits) * `validation`: full validation split (1.55M commits) * `test`: full test split (1.49M commits) * `subset_cmg` * `test`: test subset used for experiments with CMG approaches (204k commits) * `subset_llm` * `test`: test subset used for experiments with a LLM (4k commits) ## Considerations for Using the Data > Adopted from [the Stack](https://huggingface.co/datasets/bigcode/the-stack). The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. In the event that the dataset contains personal information, researchers should only use public, non-personal information in support of conducting and publishing their open-access research. Personal information should not be used for spamming purposes, including sending unsolicited emails or selling of personal information. The dataset is a collection of commits from repositories with various licenses. Any use of all or part of the code gathered in this dataset must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. ## Citation ``` TODO ```

Jackmin108/bert-base-uncased-refined-web-segment0

Jackmin108

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input_ids sequence: int32 - name: length dtype: int64 splits: - name: train num_bytes: 234885131268 num_examples: 100000000 download_size: 10689166809 dataset_size: 234885131268 --- # Dataset Card for "bert-base-uncased-refined-web-segment0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Major-TOM/Core-S2L1C

Major-TOM

--- license: cc-by-sa-4.0 tags: - earth-observation - remote-sensing - sentinel-2 - multi-spectral - satellite - geospatial size_categories: - 1M<n<10M dataset_info: - config_name: default features: - name: product_id dtype: string - name: grid_cell dtype: string - name: product_datetime dtype: string - name: thumbnail dtype: image - name: B01 dtype: binary - name: B02 dtype: binary - name: B03 dtype: binary - name: B04 dtype: binary - name: B05 dtype: binary - name: B06 dtype: binary - name: B07 dtype: binary - name: B08 dtype: binary - name: B8A dtype: binary - name: B09 dtype: binary - name: B10 dtype: binary - name: B11 dtype: binary - name: B12 dtype: binary - name: cloud_mask dtype: binary configs: - config_name: default data_files: images/*.parquet - config_name: metadata data_files: metadata.parquet --- # Core-S2L1C Contains a global coverage of Sentinel-2 (Level 1C) patches, each of size 1,068 x 1,068 pixels. | Source | Sensing Type | Number of Patches | Patch Size | Total Pixels | |--------|--------------|-------------------|------------|--------------| |Sentinel-2 Level-1C |Optical Multispectral|2,245,886|1,068x1,068|2.56 Trillion| ## Content | Column | Details | Resolution | |--------|---------|------------| | B01 | Coastal aerosol, 442.7 nm (S2A), 442.3 nm (S2B) | 60m | | B02 | Blue, 492.4 nm (S2A), 492.1 nm (S2B) | 10m | | B03 | Green, 559.8 nm (S2A), 559.0 nm (S2B) | 10m | | B04 | Red, 664.6 nm (S2A), 665.0 nm (S2B) | 10m | | B05 | Vegetation red edge, 704.1 nm (S2A), 703.8 nm (S2B) | 20m | | B06 | Vegetation red edge, 740.5 nm (S2A), 739.1 nm (S2B) | 20m | | B07 | Vegetation red edge, 782.8 nm (S2A), 779.7 nm (S2B) | 20m | | B08 | NIR, 832.8 nm (S2A), 833.0 nm (S2B) | 10m | | B8A | Narrow NIR, 864.7 nm (S2A), 864.0 nm (S2B) | 20m | | B09 | Water vapour, 945.1 nm (S2A), 943.2 nm (S2B) | 60m | | B10 | SWIR – Cirrus, 1373.5 nm (S2A), 1376.9 nm (S2B) | 60m | | B11 | SWIR, 1613.7 nm (S2A), 1610.4 nm (S2B) | 20m | | B12 | SWIR, 2202.4 nm (S2A), 2185.7 nm (S2B) | 20m | | cloud_mask | Cloud Mask produced by [SEnSeI](https://huggingface.co/aliFrancis/SEnSeIv2) | 10m | | thumbnail | RGB composite [B04, B03, B02] saved as png | 10m | ## Spatial Coverage This is a global monotemporal dataset. Nearly every piece of Earth captured by Sentinel-2 is contained at least once in this dataset (and only once, excluding some marginal overlaps). The following figure demonstrates the spatial coverage (only black pixels are absent):  ## Example Use Interface scripts are available at https://github.com/ESA-PhiLab/Major-TOM Here's a sneak peek with a thumbnail image: ```python from fsspec.parquet import open_parquet_file import pyarrow.parquet as pq from io import BytesIO from PIL import Image PARQUET_FILE = 'part_03900' # parquet number ROW_INDEX = 42 # row number (about 500 per parquet) url = "https://huggingface.co/datasets/Major-TOM/Core-S2L1C/resolve/main/images/{}.parquet".format(PARQUET_FILE) with open_parquet_file(url,columns = ["thumbnail"]) as f: with pq.ParquetFile(f) as pf: first_row_group = pf.read_row_group(ROW_INDEX, columns=['thumbnail']) stream = BytesIO(first_row_group['thumbnail'][0].as_py()) image = Image.open(stream) ``` ## Cite [](https://arxiv.org/abs/2402.12095/) ```latex @inproceedings{Major_TOM, title={Major TOM: Expandable Datasets for Earth Observation}, author={Alistair Francis and Mikolaj Czerkawski}, year={2024}, booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium}, eprint={2402.12095}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` Powered by [Φ-lab, European Space Agency (ESA) 🛰️](https://huggingface.co/ESA-philab)

TIGER-Lab/MMEB-eval

TIGER-Lab

--- dataset_info: - config_name: A-OKVQA features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 14048199 num_examples: 1000 download_size: 1168340 dataset_size: 14048199 - config_name: CIFAR-100 features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 1519890 num_examples: 1000 download_size: 20544 dataset_size: 1519890 - config_name: CIRR features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 70162098 num_examples: 1000 download_size: 1565489 dataset_size: 70162098 - config_name: ChartQA features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 14354641 num_examples: 1000 download_size: 1434448 dataset_size: 14354641 - config_name: Country211 features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 3678000 num_examples: 1000 download_size: 31556 dataset_size: 3678000 - config_name: DocVQA features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 23044459 num_examples: 1000 download_size: 1734476 dataset_size: 23044459 - config_name: EDIS features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 184208708 num_examples: 1000 download_size: 3350382 dataset_size: 184208708 - config_name: FashionIQ features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 71169665 num_examples: 1000 download_size: 1729457 dataset_size: 71169665 - config_name: GQA features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 40809641 num_examples: 1000 download_size: 1764457 dataset_size: 40809641 - config_name: HatefulMemes features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 184890 num_examples: 1000 download_size: 9972 dataset_size: 184890 - config_name: ImageNet-1K features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 28773890 num_examples: 1000 download_size: 185019 dataset_size: 28773890 - config_name: ImageNet-A features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 28772890 num_examples: 1000 download_size: 147780 dataset_size: 28772890 - config_name: ImageNet-R features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 3456890 num_examples: 1000 download_size: 23656 dataset_size: 3456890 - config_name: InfographicsVQA features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 19114439 num_examples: 1000 download_size: 1439837 dataset_size: 19114439 - config_name: MSCOCO features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 97759085 num_examples: 1000 download_size: 1681753 dataset_size: 97759085 - config_name: MSCOCO_i2t features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 60201740 num_examples: 1000 download_size: 1785583 dataset_size: 60201740 - config_name: MSCOCO_t2i features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 87127008 num_examples: 1000 download_size: 1296167 dataset_size: 87127008 - config_name: N24News features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 630658 num_examples: 1000 download_size: 110698 dataset_size: 630658 - config_name: NIGHTS features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 75116000 num_examples: 1000 download_size: 1528646 dataset_size: 75116000 - config_name: OK-VQA features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 15332578 num_examples: 1000 download_size: 1564823 dataset_size: 15332578 - config_name: OVEN features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 717934263 num_examples: 1000 download_size: 406792141 dataset_size: 717934263 - config_name: ObjectNet features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 2036000 num_examples: 1000 download_size: 27132 dataset_size: 2036000 - config_name: Place365 features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 7045000 num_examples: 1000 download_size: 89866 dataset_size: 7045000 - config_name: RefCOCO features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 96493941 num_examples: 1000 download_size: 1858145 dataset_size: 96493941 - config_name: RefCOCO-Matching features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 145712476 num_examples: 1000 download_size: 2879385 dataset_size: 145712476 - config_name: SUN397 features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 7990000 num_examples: 1000 download_size: 118447 dataset_size: 7990000 - config_name: ScienceQA features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 23870406 num_examples: 1000 download_size: 958782 dataset_size: 23870406 - config_name: TextVQA features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 17435986 num_examples: 1000 download_size: 1571656 dataset_size: 17435986 - config_name: VOC2007 features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 368000 num_examples: 1000 download_size: 13813 dataset_size: 368000 - config_name: VisDial features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 67989850 num_examples: 1000 download_size: 1730820 dataset_size: 67989850 - config_name: Visual7W features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 22047066 num_examples: 1000 download_size: 1564788 dataset_size: 22047066 - config_name: Visual7W-Pointing features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 94906832 num_examples: 1000 download_size: 1299380 dataset_size: 94906832 - config_name: VisualNews_i2t features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 118329649 num_examples: 1000 download_size: 81491360 dataset_size: 118329649 - config_name: VisualNews_t2i features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 97176206 num_examples: 1000 download_size: 1763677 dataset_size: 97176206 - config_name: VizWiz features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 20550246 num_examples: 1000 download_size: 1425789 dataset_size: 20550246 - config_name: WebQA features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 197701404 num_examples: 1000 download_size: 3257136 dataset_size: 197701404 - config_name: Wiki-SS-NQ features: - name: qry_text dtype: string - name: qry_img_path dtype: string - name: tgt_text sequence: string - name: tgt_img_path sequence: string splits: - name: test num_bytes: 74583207 num_examples: 1000 download_size: 1900579 dataset_size: 74583207 configs: - config_name: A-OKVQA data_files: - split: test path: A-OKVQA/test-* - config_name: CIFAR-100 data_files: - split: test path: CIFAR-100/test-* - config_name: CIRR data_files: - split: test path: CIRR/test-* - config_name: ChartQA data_files: - split: test path: ChartQA/test-* - config_name: Country211 data_files: - split: test path: Country211/test-* - config_name: DocVQA data_files: - split: test path: DocVQA/test-* - config_name: EDIS data_files: - split: test path: EDIS/test-* - config_name: FashionIQ data_files: - split: test path: FashionIQ/test-* - config_name: GQA data_files: - split: test path: GQA/test-* - config_name: HatefulMemes data_files: - split: test path: HatefulMemes/test-* - config_name: ImageNet-1K data_files: - split: test path: ImageNet-1K/test-* - config_name: ImageNet-A data_files: - split: test path: ImageNet-A/test-* - config_name: ImageNet-R data_files: - split: test path: ImageNet-R/test-* - config_name: InfographicsVQA data_files: - split: test path: InfographicsVQA/test-* - config_name: MSCOCO data_files: - split: test path: MSCOCO/test-* - config_name: MSCOCO_i2t data_files: - split: test path: MSCOCO_i2t/test-* - config_name: MSCOCO_t2i data_files: - split: test path: MSCOCO_t2i/test-* - config_name: N24News data_files: - split: test path: N24News/test-* - config_name: NIGHTS data_files: - split: test path: NIGHTS/test-* - config_name: OK-VQA data_files: - split: test path: OK-VQA/test-* - config_name: OVEN data_files: - split: test path: OVEN/test-* - config_name: ObjectNet data_files: - split: test path: ObjectNet/test-* - config_name: Place365 data_files: - split: test path: Place365/test-* - config_name: RefCOCO data_files: - split: test path: RefCOCO/test-* - config_name: RefCOCO-Matching data_files: - split: test path: RefCOCO-Matching/test-* - config_name: SUN397 data_files: - split: test path: SUN397/test-* - config_name: ScienceQA data_files: - split: test path: ScienceQA/test-* - config_name: TextVQA data_files: - split: test path: TextVQA/test-* - config_name: VOC2007 data_files: - split: test path: VOC2007/test-* - config_name: VisDial data_files: - split: test path: VisDial/test-* - config_name: Visual7W data_files: - split: test path: Visual7W/test-* - config_name: Visual7W-Pointing data_files: - split: test path: Visual7W-Pointing/test-* - config_name: VisualNews_i2t data_files: - split: test path: VisualNews_i2t/test-* - config_name: VisualNews_t2i data_files: - split: test path: VisualNews_t2i/test-* - config_name: VizWiz data_files: - split: test path: VizWiz/test-* - config_name: WebQA data_files: - split: test path: WebQA/test-* - config_name: Wiki-SS-NQ data_files: - split: test path: Wiki-SS-NQ/test-* license: apache-2.0 language: - en tags: - ranking pretty_name: MMEB size_categories: - 10K<n<100K --- # Massive Multimodal Embedding Benchmark We compile a large set of evaluation tasks to understand the capabilities of multimodal embedding models. This benchmark covers 4 meta tasks and 36 datasets meticulously selected for evaluation. The dataset is published in our paper [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://arxiv.org/abs/2410.05160). ## Dataset Usage For each dataset, we have 1000 examples for evaluation. Each example contains a query and a set of targets. Both the query and target could be any combination of image and text. The first one in the candidate list is the groundtruth target. ## Statistics We show the statistics of all the datasets as follows: <img width="900" alt="abs" src="statistics.png"> ## Per-dataset Results We list the performance of different embedding models in the following: <img width="900" alt="abs" src="leaderboard.png"> ## Submission We will set a formal leaderboard soon. If you want to add your results to the leaderboard, please send email to us at [email protected]. ## Cite Us ``` @article{jiang2024vlm2vec, title={VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks}, author={Jiang, Ziyan and Meng, Rui and Yang, Xinyi and Yavuz, Semih and Zhou, Yingbo and Chen, Wenhu}, journal={arXiv preprint arXiv:2410.05160}, year={2024} } ```

google/xtreme_s

google

--- annotations_creators: - expert-generated - crowdsourced - machine-generated language_creators: - crowdsourced - expert-generated language: - afr - amh - ara - asm - ast - azj - bel - ben - bos - cat - ceb - cmn - ces - cym - dan - deu - ell - eng - spa - est - fas - ful - fin - tgl - fra - gle - glg - guj - hau - heb - hin - hrv - hun - hye - ind - ibo - isl - ita - jpn - jav - kat - kam - kea - kaz - khm - kan - kor - ckb - kir - ltz - lug - lin - lao - lit - luo - lav - mri - mkd - mal - mon - mar - msa - mlt - mya - nob - npi - nld - nso - nya - oci - orm - ory - pan - pol - pus - por - ron - rus - bul - snd - slk - slv - sna - som - srp - swe - swh - tam - tel - tgk - tha - tur - ukr - umb - urd - uzb - vie - wol - xho - yor - yue - zul license: - cc-by-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - extended|multilingual_librispeech - extended|covost2 task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: librispeech-1 pretty_name: 'The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.' tags: - speech-recognition --- # XTREME-S ## Dataset Description - **Fine-Tuning script:** [research-projects/xtreme-s](https://github.com/huggingface/transformers/tree/master/examples/research_projects/xtreme-s) - **Paper:** [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752) - **Leaderboard:** [TODO(PVP)]() - **FLEURS amount of disk used:** 350 GB - **Multilingual Librispeech amount of disk used:** 2700 GB - **Voxpopuli amount of disk used:** 400 GB - **Covost2 amount of disk used:** 70 GB - **Minds14 amount of disk used:** 5 GB - **Total amount of disk used:** ca. 3500 GB The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval. ***TLDR; XTREME-S is the first speech benchmark that is both diverse, fully accessible, and reproducible. All datasets can be downloaded with a single line of code. An easy-to-use and flexible fine-tuning script is provided and actively maintained.*** XTREME-S covers speech recognition with Fleurs, Multilingual LibriSpeech (MLS) and VoxPopuli, speech translation with CoVoST-2, speech classification with LangID (Fleurs) and intent classification (MInds-14) and finally speech(-text) retrieval with Fleurs. Each of the tasks covers a subset of the 102 languages included in XTREME-S, from various regions: - **Western Europe**: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh* - **Eastern Europe**: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian* - **Central-Asia/Middle-East/North-Africa**: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek* - **Sub-Saharan Africa**: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu* - **South-Asia**: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu* - **South-East Asia**: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese* - **CJK languages**: *Cantonese and Mandarin Chinese, Japanese, Korean* ## Design principles ### Diversity XTREME-S aims for task, domain and language diversity. Tasks should be diverse and cover several domains to provide a reliable evaluation of model generalization and robustness to noisy naturally-occurring speech in different environments. Languages should be diverse to ensure that models can adapt to a wide range of linguistic and phonological phenomena. ### Accessibility The sub-dataset for each task can be downloaded with a **single line of code** as shown in [Supported Tasks](#supported-tasks). Each task is available under a permissive license that allows the use and redistribution of the data for research purposes. Tasks have been selected based on their usage by pre-existing multilingual pre-trained models, for simplicity. ### Reproducibility We produce fully **open-sourced, maintained and easy-to-use** fine-tuning scripts for each task as shown under [Fine-tuning Example](#fine-tuning-and-evaluation-example). XTREME-S encourages submissions that leverage publicly available speech and text datasets. Users should detail which data they use. In general, we encourage settings that can be reproduced by the community, but also encourage the exploration of new frontiers for speech representation learning. ## Fine-tuning and Evaluation Example We provide a fine-tuning script under [**research-projects/xtreme-s**](https://github.com/huggingface/transformers/tree/master/examples/research_projects/xtreme-s). The fine-tuning script is written in PyTorch and allows one to fine-tune and evaluate any [Hugging Face model](https://huggingface.co/models) on XTREME-S. The example script is actively maintained by [@anton-l](https://github.com/anton-l) and [@patrickvonplaten](https://github.com/patrickvonplaten). Feel free to reach out via issues or pull requests on GitHub if you have any questions. ## Leaderboards The leaderboard for the XTREME-S benchmark can be found at [this address (TODO(PVP))](). ## Supported Tasks Note that the suppoprted tasks are focused particularly on linguistic aspect of speech, while nonlinguistic/paralinguistic aspects of speech relevant to e.g. speech synthesis or voice conversion are **not** evaluated. <p align="center"> <img src="https://github.com/patrickvonplaten/scientific_images/raw/master/xtreme_s.png" alt="Datasets used in XTREME"/> </p> ### 1. Speech Recognition (ASR) We include three speech recognition datasets: FLEURS-ASR, MLS and VoxPopuli (optionally BABEL). Multilingual fine-tuning is used for these three datasets. #### FLEURS-ASR *FLEURS-ASR* is the speech version of the FLORES machine translation benchmark, covering 2000 n-way parallel sentences in n=102 languages. ```py from datasets import load_dataset fleurs_asr = load_dataset("google/xtreme_s", "fleurs.af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_asr = load_dataset("google/xtreme_s", "fleurs.all") # see structure print(fleurs_asr) # load audio sample on the fly audio_input = fleurs_asr["train"][0]["audio"] # first decoded audio sample transcription = fleurs_asr["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR # for analyses see language groups all_language_groups = fleurs_asr["train"].features["lang_group_id"].names lang_group_id = fleurs_asr["train"][0]["lang_group_id"] all_language_groups[lang_group_id] ``` #### Multilingual LibriSpeech (MLS) *MLS* is a large multilingual corpus derived from read audiobooks from LibriVox and consists of 8 languages. For this challenge the training data is limited to 10-hours splits. ```py from datasets import load_dataset mls = load_dataset("google/xtreme_s", "mls.pl") # for Polish # to download all data for multi-lingual fine-tuning uncomment following line # mls = load_dataset("google/xtreme_s", "mls.all") # see structure print(mls) # load audio sample on the fly audio_input = mls["train"][0]["audio"] # first decoded audio sample transcription = mls["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` #### VoxPopuli *VoxPopuli* is a large-scale multilingual speech corpus for representation learning and semi-supervised learning, from which we use the speech recognition dataset. The raw data is collected from 2009-2020 European Parliament event recordings. We acknowledge the European Parliament for creating and sharing these materials. **VoxPopuli has to download the whole dataset 100GB since languages are entangled into each other - maybe not worth testing here due to the size** ```py from datasets import load_dataset voxpopuli = load_dataset("google/xtreme_s", "voxpopuli.ro") # for Romanian # to download all data for multi-lingual fine-tuning uncomment following line # voxpopuli = load_dataset("google/xtreme_s", "voxpopuli.all") # see structure print(voxpopuli) # load audio sample on the fly audio_input = voxpopuli["train"][0]["audio"] # first decoded audio sample transcription = voxpopuli["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` #### (Optionally) BABEL *BABEL* from IARPA is a conversational speech recognition dataset in low-resource languages. First, download LDC2016S06, LDC2016S12, LDC2017S08, LDC2017S05 and LDC2016S13. BABEL is the only dataset in our benchmark who is less easily accessible, so you will need to sign in to get access to it on LDC. Although not officially part of the XTREME-S ASR datasets, BABEL is often used for evaluating speech representations on a difficult domain (phone conversations). ```py from datasets import load_dataset babel = load_dataset("google/xtreme_s", "babel.as") ``` **The above command is expected to fail with a nice error message, explaining how to download BABEL** The following should work: ```py from datasets import load_dataset babel = load_dataset("google/xtreme_s", "babel.as", data_dir="/path/to/IARPA_BABEL_OP1_102_LDC2016S06.zip") # see structure print(babel) # load audio sample on the fly audio_input = babel["train"][0]["audio"] # first decoded audio sample transcription = babel["train"][0]["transcription"] # first transcription # use `audio_input` and `transcription` to fine-tune your model for ASR ``` ### 2. Speech Translation (ST) We include the CoVoST-2 dataset for automatic speech translation. #### CoVoST-2 The *CoVoST-2* benchmark has become a commonly used dataset for evaluating automatic speech translation. It covers language pairs from English into 15 languages, as well as 21 languages into English. We use only the "X->En" direction to evaluate cross-lingual representations. The amount of supervision varies greatly in this setting, from one hour for Japanese->English to 180 hours for French->English. This makes pretraining particularly useful to enable such few-shot learning. We enforce multiligual fine-tuning for simplicity. Results are splitted in high/med/low-resource language pairs as explained in the [paper (TODO(PVP))]. ```py from datasets import load_dataset covost_2 = load_dataset("google/xtreme_s", "covost2.id.en") # for Indonesian to English # to download all data for multi-lingual fine-tuning uncomment following line # covost_2 = load_dataset("google/xtreme_s", "covost2.all") # see structure print(covost_2) # load audio sample on the fly audio_input = covost_2["train"][0]["audio"] # first decoded audio sample transcription = covost_2["train"][0]["transcription"] # first transcription translation = covost_2["train"][0]["translation"] # first translation # use audio_input and translation to fine-tune your model for AST ``` ### 3. Speech Classification We include two multilingual speech classification datasets: FLEURS-LangID and Minds-14. #### Language Identification - FLEURS-LangID LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all. ```py from datasets import load_dataset fleurs_langID = load_dataset("google/xtreme_s", "fleurs.all") # to download all data # see structure print(fleurs_langID) # load audio sample on the fly audio_input = fleurs_langID["train"][0]["audio"] # first decoded audio sample language_class = fleurs_langID["train"][0]["lang_id"] # first id class language = fleurs_langID["train"].features["lang_id"].names[language_class] # use audio_input and language_class to fine-tune your model for audio classification ``` #### Intent classification - Minds-14 Minds-14 is an intent classification made from e-banking speech datasets in 14 languages, with 14 intent labels. We impose a single multilingual fine-tuning to increase the size of the train and test sets and reduce the variance associated with the small size of the dataset per language. ```py from datasets import load_dataset minds_14 = load_dataset("google/xtreme_s", "minds14.fr-FR") # for French # to download all data for multi-lingual fine-tuning uncomment following line # minds_14 = load_dataset("google/xtreme_s", "minds14.all") # see structure print(minds_14) # load audio sample on the fly audio_input = minds_14["train"][0]["audio"] # first decoded audio sample intent_class = minds_14["train"][0]["intent_class"] # first transcription intent = minds_14["train"].features["intent_class"].names[intent_class] # use audio_input and language_class to fine-tune your model for audio classification ``` ### 4. (Optionally) Speech Retrieval We optionally include one speech retrieval dataset: FLEURS-Retrieval as explained in the [FLEURS paper](https://arxiv.org/abs/2205.12446). #### FLEURS-Retrieval FLEURS-Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use FLEURS-Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of FLEURS-Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult. ```py from datasets import load_dataset fleurs_retrieval = load_dataset("google/xtreme_s", "fleurs.af_za") # for Afrikaans # to download all data for multi-lingual fine-tuning uncomment following line # fleurs_retrieval = load_dataset("google/xtreme_s", "fleurs.all") # see structure print(fleurs_retrieval) # load audio sample on the fly audio_input = fleurs_retrieval["train"][0]["audio"] # decoded audio sample text_sample_pos = fleurs_retrieval["train"][0]["transcription"] # positive text sample text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples # use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval ``` Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech. ## Dataset Structure The XTREME-S benchmark is composed of the following datasets: - [FLEURS](https://huggingface.co/datasets/google/fleurs#dataset-structure) - [Multilingual Librispeech (MLS)](https://huggingface.co/datasets/facebook/multilingual_librispeech#dataset-structure) Note that for MLS, XTREME-S uses `path` instead of `file` and `transcription` instead of `text`. - [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli#dataset-structure) - [Minds14](https://huggingface.co/datasets/polyai/minds14#dataset-structure) - [Covost2](https://huggingface.co/datasets/covost2#dataset-structure) Note that for Covost2, XTREME-S uses `path` instead of `file` and `transcription` instead of `sentence`. - [BABEL](https://huggingface.co/datasets/ldc/iarpa_babel#dataset-structure) Please click on the link of the dataset cards to get more information about its dataset structure. ## Dataset Creation The XTREME-S benchmark is composed of the following datasets: - [FLEURS](https://huggingface.co/datasets/google/fleurs#dataset-creation) - [Multilingual Librispeech (MLS)](https://huggingface.co/datasets/facebook/multilingual_librispeech#dataset-creation) - [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli#dataset-creation) - [Minds14](https://huggingface.co/datasets/polyai/minds14#dataset-creation) - [Covost2](https://huggingface.co/datasets/covost2#dataset-creation) - [BABEL](https://huggingface.co/datasets/ldc/iarpa_babel#dataset-creation) Please visit the corresponding dataset cards to get more information about the source data. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos). ### Discussion of Biases Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through XTREME-S should generalize to all languages. ### Other Known Limitations The benchmark has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on XTREME-S should still correlate well with actual progress made for speech understanding. ## Additional Information All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/). ### Citation Information #### XTREME-S ``` @article{conneau2022xtreme, title={XTREME-S: Evaluating Cross-lingual Speech Representations}, author={Conneau, Alexis and Bapna, Ankur and Zhang, Yu and Ma, Min and von Platen, Patrick and Lozhkov, Anton and Cherry, Colin and Jia, Ye and Rivera, Clara and Kale, Mihir and others}, journal={arXiv preprint arXiv:2203.10752}, year={2022} } ``` #### MLS ``` @article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ``` #### VoxPopuli ``` @article{wang2021voxpopuli, title={Voxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation}, author={Wang, Changhan and Riviere, Morgane and Lee, Ann and Wu, Anne and Talnikar, Chaitanya and Haziza, Daniel and Williamson, Mary and Pino, Juan and Dupoux, Emmanuel}, journal={arXiv preprint arXiv:2101.00390}, year={2021} } ``` #### CoVoST 2 ``` @article{DBLP:journals/corr/abs-2007-10310, author = {Changhan Wang and Anne Wu and Juan Miguel Pino}, title = {CoVoST 2: {A} Massively Multilingual Speech-to-Text Translation Corpus}, journal = {CoRR}, volume = {abs/2007.10310}, year = {2020}, url = {https://arxiv.org/abs/2007.10310}, eprinttype = {arXiv}, eprint = {2007.10310}, timestamp = {Thu, 12 Aug 2021 15:37:06 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2007-10310.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` #### Minds14 ``` @article{gerz2021multilingual, title={Multilingual and cross-lingual intent detection from spoken data}, author={Gerz, Daniela and Su, Pei-Hao and Kusztos, Razvan and Mondal, Avishek and Lis, Micha{\l} and Singhal, Eshan and Mrk{\v{s}}i{\'c}, Nikola and Wen, Tsung-Hsien and Vuli{\'c}, Ivan}, journal={arXiv preprint arXiv:2104.08524}, year={2021} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@anton-l](https://github.com/anton-l), [@aconneau](https://github.com/aconneau) for adding this dataset

big-banyan-tree/BBT_CommonCrawl_2024

big-banyan-tree

--- license: mit language: - en pretty_name: BBT-CC24 size_categories: - 10M<n<100M configs: - config_name: script_extraction data_files: "script_extraction/*.arrow" - config_name: ipmaxmind data_files: "ipmaxmind/*.arrow" --- # Context BigBanyanTree is an initiative to empower colleges to set up their data engineering clusters, and drive interest towards data processing and analysis using tools such as Apache Spark. The data provided here is the direct result of this initiative. The data was processed by [Gautam](https://www.linkedin.com/in/gautam-menon-9a30a3233/) and [Suchit](https://www.linkedin.com/in/suchitg04/), under the guidance of [Harsh Singhal](https://www.linkedin.com/in/harshsinghal/). # Content Each `arrow` file contains a table with fields extracted from Common Crawl WARC files. The datasets provided are derived from processing randomly sampled 900 WARC files from the [2024-33 CommonCrawl dump](https://data.commoncrawl.org/crawl-data/CC-MAIN-2024-33/index.html). The MaxMind database used to enrich WARC data with geolocation information is GeoLite2-City_20240903 (released on 3rd Sept. 2024). ## <span style="color:red">⚠️ WARNING ⚠️</span> The **URLs** and **IP addresses** extracted in this dataset are sourced from **publicly available Common Crawl data dumps**. Please be aware that: - The data may contain **inaccuracies** or **outdated information**. - **No validation or filtering** has been performed on the extracted URLs or IP addresses. - As the data has **not been filtered**, it may contain URLs promoting **obscene or objectionable content**. - Use this data **with caution**, especially for tasks involving personal or sensitive information. ## Disclaimer These data points are included solely for the purpose of: - **Analyzing domain distributions** - **IP metadata analysis**

alvations/c4p0-x1-en-es

alvations

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 2308 num_examples: 2 download_size: 15489 dataset_size: 2308 configs: - config_name: default data_files: - split: train path: 1f24f16745a166b0/train-* ---

Hemabhushan/capstone_sakuga_preproc_optical_flow

Hemabhushan

--- dataset_info: - config_name: sample_subset features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 30244617541 num_examples: 2396 download_size: 5461228507 dataset_size: 30244617541 - config_name: seg1_part1 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31482846975 num_examples: 2498 download_size: 5683747736 dataset_size: 31482846975 - config_name: seg1_part10 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31533368384 num_examples: 2498 download_size: 5651523132 dataset_size: 31533368384 - config_name: seg1_part11 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31495470684 num_examples: 2498 download_size: 5646719194 dataset_size: 31495470684 - config_name: seg1_part12 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31533346986 num_examples: 2498 download_size: 5705163694 dataset_size: 31533346986 - config_name: seg1_part14 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31432281831 num_examples: 2498 download_size: 5627562296 dataset_size: 31432281831 - config_name: seg1_part15 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31508092434 num_examples: 2498 download_size: 5647225033 dataset_size: 31508092434 - config_name: seg1_part18 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31482843486 num_examples: 2498 download_size: 5703102313 dataset_size: 31482843486 - config_name: seg1_part2 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31457575891 num_examples: 2498 download_size: 5650519682 dataset_size: 31457575891 - config_name: seg1_part20 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31470181418 num_examples: 2498 download_size: 5625192608 dataset_size: 31470181418 - config_name: seg1_part21 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31508103461 num_examples: 2498 download_size: 5680819286 dataset_size: 31508103461 - config_name: seg1_part24 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31520754590 num_examples: 2498 download_size: 5697959164 dataset_size: 31520754590 - config_name: seg1_part25 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31482806663 num_examples: 2498 download_size: 5628329196 dataset_size: 31482806663 - config_name: seg1_part26 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31533316255 num_examples: 2498 download_size: 5662161621 dataset_size: 31533316255 - config_name: seg1_part27 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31533349249 num_examples: 2498 download_size: 5654417461 dataset_size: 31533349249 - config_name: seg1_part28 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31533349763 num_examples: 2498 download_size: 5644209592 dataset_size: 31533349763 - config_name: seg1_part29 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31508101677 num_examples: 2498 download_size: 5725512822 dataset_size: 31508101677 - config_name: seg1_part30 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31520696316 num_examples: 2498 download_size: 5649748978 dataset_size: 31520696316 - config_name: seg1_part31 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31470211581 num_examples: 2498 download_size: 5691521624 dataset_size: 31470211581 - config_name: seg1_part32 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31520703122 num_examples: 2498 download_size: 5611392470 dataset_size: 31520703122 - config_name: seg1_part33 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31533395644 num_examples: 2498 download_size: 5765660331 dataset_size: 31533395644 - config_name: seg1_part34 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31482802747 num_examples: 2498 download_size: 5654024836 dataset_size: 31482802747 - config_name: seg1_part35 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31508072238 num_examples: 2498 download_size: 5632935439 dataset_size: 31508072238 - config_name: seg1_part36 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31508118773 num_examples: 2498 download_size: 5708713170 dataset_size: 31508118773 - config_name: seg1_part39 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31508101149 num_examples: 2498 download_size: 5697274819 dataset_size: 31508101149 - config_name: seg1_part4 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31482829874 num_examples: 2498 download_size: 5700440041 dataset_size: 31482829874 - config_name: seg1_part40 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31508069004 num_examples: 2498 download_size: 5640935450 dataset_size: 31508069004 - config_name: seg1_part41 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31558603213 num_examples: 2498 download_size: 5713447755 dataset_size: 31558603213 - config_name: seg1_part42 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31482828955 num_examples: 2498 download_size: 5640954061 dataset_size: 31482828955 - config_name: seg1_part43 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31520704278 num_examples: 2498 download_size: 5736520090 dataset_size: 31520704278 - config_name: seg1_part44 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31166950691 num_examples: 2471 download_size: 5640666013 dataset_size: 31166950691 - config_name: seg1_part45 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' splits: - name: train num_bytes: 0 num_examples: 0 download_size: 6857 dataset_size: 0 - config_name: seg1_part6 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31545984682 num_examples: 2498 download_size: 5674259234 dataset_size: 31545984682 - config_name: seg1_part8 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31545987289 num_examples: 2498 download_size: 5733443343 dataset_size: 31545987289 - config_name: seg1_part9 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: word_count dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: frames dtype: 'null' - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31533382844 num_examples: 2498 download_size: 5634081955 dataset_size: 31533382844 - config_name: seg2_part1 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: prev_conv list: - name: content list: - name: text dtype: string - name: type dtype: string - name: role dtype: string - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31284870750 num_examples: 2498 download_size: 6023339313 dataset_size: 31284870750 - config_name: seg2_part3 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: prev_conv list: - name: content list: - name: text dtype: string - name: type dtype: string - name: role dtype: string - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31297486132 num_examples: 2498 download_size: 6033401989 dataset_size: 31297486132 - config_name: seg2_part4 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: prev_conv list: - name: content list: - name: text dtype: string - name: type dtype: string - name: role dtype: string - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31209057858 num_examples: 2498 download_size: 6033150218 dataset_size: 31209057858 - config_name: seg2_part7 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: prev_conv list: - name: content list: - name: text dtype: string - name: type dtype: string - name: role dtype: string - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31310131354 num_examples: 2498 download_size: 6026279130 dataset_size: 31310131354 - config_name: seg3_part1 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31091165546 num_examples: 2498 download_size: 6013722850 dataset_size: 31091165546 - config_name: seg3_part3 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 30964835031 num_examples: 2498 download_size: 5981318118 dataset_size: 30964835031 - config_name: seg3_part4 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31141675653 num_examples: 2498 download_size: 6035418048 dataset_size: 31141675653 - config_name: seg3_part7 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31230125953 num_examples: 2498 download_size: 6080001698 dataset_size: 31230125953 - config_name: seg4_part1 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31230126642 num_examples: 2498 download_size: 6074698944 dataset_size: 31230126642 - config_name: seg4_part3 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31040618574 num_examples: 2498 download_size: 5968129650 dataset_size: 31040618574 - config_name: seg4_part5 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31015355157 num_examples: 2498 download_size: 6027043124 dataset_size: 31015355157 - config_name: seg4_part6 features: - name: identifier dtype: string - name: hash_identifier dtype: string - name: url_link dtype: string - name: scene_start_time dtype: string - name: scene_end_time dtype: string - name: frame_number dtype: float64 - name: key_frame_number dtype: float64 - name: anime_tags dtype: string - name: user_tags dtype: string - name: text_description dtype: string - name: aesthetic_score dtype: float64 - name: dynamic_score dtype: float64 - name: rating dtype: string - name: text_prob dtype: float64 - name: width dtype: int64 - name: height dtype: int64 - name: file_ext dtype: string - name: fps dtype: float64 - name: Taxonomy_Time dtype: string - name: Taxonomy_Venue dtype: string - name: Taxonomy_Media dtype: string - name: Taxonomy_Filming dtype: string - name: Taxonomy_Composition dtype: string - name: Taxonomy_Character dtype: string - name: __index_level_0__ dtype: int64 - name: video_id dtype: string - name: video_segment_no dtype: int64 - name: word_count dtype: int64 - name: video_frames sequence: sequence: sequence: sequence: float32 splits: - name: train num_bytes: 31116405163 num_examples: 2498 download_size: 6079250810 dataset_size: 31116405163 configs: - config_name: sample_subset data_files: - split: train path: sample_subset/train-* - config_name: seg1_part1 data_files: - split: train path: seg1_part1/train-* - config_name: seg1_part10 data_files: - split: train path: seg1_part10/train-* - config_name: seg1_part11 data_files: - split: train path: seg1_part11/train-* - config_name: seg1_part12 data_files: - split: train path: seg1_part12/train-* - config_name: seg1_part14 data_files: - split: train path: seg1_part14/train-* - config_name: seg1_part15 data_files: - split: train path: seg1_part15/train-* - config_name: seg1_part18 data_files: - split: train path: seg1_part18/train-* - config_name: seg1_part2 data_files: - split: train path: seg1_part2/train-* - config_name: seg1_part20 data_files: - split: train path: seg1_part20/train-* - config_name: seg1_part21 data_files: - split: train path: seg1_part21/train-* - config_name: seg1_part24 data_files: - split: train path: seg1_part24/train-* - config_name: seg1_part25 data_files: - split: train path: seg1_part25/train-* - config_name: seg1_part26 data_files: - split: train path: seg1_part26/train-* - config_name: seg1_part27 data_files: - split: train path: seg1_part27/train-* - config_name: seg1_part28 data_files: - split: train path: seg1_part28/train-* - config_name: seg1_part29 data_files: - split: train path: seg1_part29/train-* - config_name: seg1_part30 data_files: - split: train path: seg1_part30/train-* - config_name: seg1_part31 data_files: - split: train path: seg1_part31/train-* - config_name: seg1_part32 data_files: - split: train path: seg1_part32/train-* - config_name: seg1_part33 data_files: - split: train path: seg1_part33/train-* - config_name: seg1_part34 data_files: - split: train path: seg1_part34/train-* - config_name: seg1_part35 data_files: - split: train path: seg1_part35/train-* - config_name: seg1_part36 data_files: - split: train path: seg1_part36/train-* - config_name: seg1_part39 data_files: - split: train path: seg1_part39/train-* - config_name: seg1_part4 data_files: - split: train path: seg1_part4/train-* - config_name: seg1_part40 data_files: - split: train path: seg1_part40/train-* - config_name: seg1_part41 data_files: - split: train path: seg1_part41/train-* - config_name: seg1_part42 data_files: - split: train path: seg1_part42/train-* - config_name: seg1_part43 data_files: - split: train path: seg1_part43/train-* - config_name: seg1_part44 data_files: - split: train path: seg1_part44/train-* - config_name: seg1_part45 data_files: - split: train path: seg1_part45/train-* - config_name: seg1_part6 data_files: - split: train path: seg1_part6/train-* - config_name: seg1_part8 data_files: - split: train path: seg1_part8/train-* - config_name: seg1_part9 data_files: - split: train path: seg1_part9/train-* - config_name: seg2_part1 data_files: - split: train path: seg2_part1/train-* - config_name: seg2_part3 data_files: - split: train path: seg2_part3/train-* - config_name: seg2_part4 data_files: - split: train path: seg2_part4/train-* - config_name: seg2_part7 data_files: - split: train path: seg2_part7/train-* - config_name: seg3_part1 data_files: - split: train path: seg3_part1/train-* - config_name: seg3_part3 data_files: - split: train path: seg3_part3/train-* - config_name: seg3_part4 data_files: - split: train path: seg3_part4/train-* - config_name: seg3_part7 data_files: - split: train path: seg3_part7/train-* - config_name: seg4_part1 data_files: - split: train path: seg4_part1/train-* - config_name: seg4_part3 data_files: - split: train path: seg4_part3/train-* - config_name: seg4_part5 data_files: - split: train path: seg4_part5/train-* - config_name: seg4_part6 data_files: - split: train path: seg4_part6/train-* ---

openlifescienceai/medmcqa

openlifescienceai

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering - multiple-choice task_ids: - multiple-choice-qa - open-domain-qa paperswithcode_id: medmcqa pretty_name: MedMCQA dataset_info: features: - name: id dtype: string - name: question dtype: string - name: opa dtype: string - name: opb dtype: string - name: opc dtype: string - name: opd dtype: string - name: cop dtype: class_label: names: '0': a '1': b '2': c '3': d - name: choice_type dtype: string - name: exp dtype: string - name: subject_name dtype: string - name: topic_name dtype: string splits: - name: train num_bytes: 131903297 num_examples: 182822 - name: test num_bytes: 1399350 num_examples: 6150 - name: validation num_bytes: 2221428 num_examples: 4183 download_size: 88311487 dataset_size: 135524075 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* --- # Dataset Card for MedMCQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://medmcqa.github.io - **Repository:** https://github.com/medmcqa/medmcqa - **Paper:** [MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://proceedings.mlr.press/v174/pal22a) - **Leaderboard:** https://paperswithcode.com/dataset/medmcqa - **Point of Contact:** [Aaditya Ura](mailto:[email protected]) ### Dataset Summary MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which require a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above information, is provided in this study. MedMCQA provides an open-source dataset for the Natural Language Processing community. It is expected that this dataset would facilitate future research toward achieving better QA systems. The dataset contains questions about the following topics: - Anesthesia - Anatomy - Biochemistry - Dental - ENT - Forensic Medicine (FM) - Obstetrics and Gynecology (O&G) - Medicine - Microbiology - Ophthalmology - Orthopedics - Pathology - Pediatrics - Pharmacology - Physiology - Psychiatry - Radiology - Skin - Preventive & Social Medicine (PSM) - Surgery ### Supported Tasks and Leaderboards multiple-choice-QA, open-domain-QA: The dataset can be used to train a model for multi-choice questions answering, open domain questions answering. Questions in these exams are challenging and generally require deeper domain and language understanding as it tests the 10+ reasoning abilities across a wide range of medical subjects & topics. ### Languages The questions and answers are available in English. ## Dataset Structure ### Data Instances ``` { "question":"A 40-year-old man presents with 5 days of productive cough and fever. Pseudomonas aeruginosa is isolated from a pulmonary abscess. CBC shows an acute effect characterized by marked leukocytosis (50,000 mL) and the differential count reveals a shift to left in granulocytes. Which of the following terms best describes these hematologic findings?", "exp": "Circulating levels of leukocytes and their precursors may occasionally reach very high levels (>50,000 WBC mL). These extreme elevations are sometimes called leukemoid reactions because they are similar to the white cell counts observed in leukemia, from which they must be distinguished. The leukocytosis occurs initially because of the accelerated release of granulocytes from the bone marrow (caused by cytokines, including TNF and IL-1) There is a rise in the number of both mature and immature neutrophils in the blood, referred to as a shift to the left. In contrast to bacterial infections, viral infections (including infectious mononucleosis) are characterized by lymphocytosis Parasitic infestations and certain allergic reactions cause eosinophilia, an increase in the number of circulating eosinophils. Leukopenia is defined as an absolute decrease in the circulating WBC count.", "cop":1, "opa":"Leukemoid reaction", "opb":"Leukopenia", "opc":"Myeloid metaplasia", "opd":"Neutrophilia", "subject_name":"Pathology", "topic_name":"Basic Concepts and Vascular changes of Acute Inflammation", "id":"4e1715fe-0bc3-494e-b6eb-2d4617245aef", "choice_type":"single" } ``` ### Data Fields - `id` : a string question identifier for each example - `question` : question text (a string) - `opa` : Option A - `opb` : Option B - `opc` : Option C - `opd` : Option D - `cop` : Correct option, i.e., 1,2,3,4 - `choice_type` ({"single", "multi"}): Question choice type. - "single": Single-choice question, where each choice contains a single option. - "multi": Multi-choice question, where each choice contains a combination of multiple suboptions. - `exp` : Expert's explanation of the answer - `subject_name` : Medical Subject name of the particular question - `topic_name` : Medical topic name from the particular subject ### Data Splits The goal of MedMCQA is to emulate the rigor of real word medical exams. To enable that, a predefined split of the dataset is provided. The split is by exams instead of the given questions. This also ensures the reusability and generalization ability of the models. The training set of MedMCQA consists of all the collected mock & online test series, whereas the test set consists of all AIIMS PG exam MCQs (years 1991-present). The development set consists of NEET PG exam MCQs (years 2001-present) to approximate real exam evaluation. Similar questions from train , test and dev set were removed based on similarity. The final split sizes are as follow: | | Train | Test | Valid | | ----- | ------ | ----- | ---- | | Question #| 182,822 | 6,150 | 4,183| | Vocab | 94,231 | 11,218 | 10,800 | | Max Ques tokens | 220 | 135| 88 | | Max Ans tokens | 38 | 21 | 25 | ## Dataset Creation ### Curation Rationale Before this attempt, very few works have been done to construct biomedical MCQA datasets (Vilares and Gomez-Rodr, 2019), and they are (1) mostly small, containing up to few thousand questions, and (2) cover a limited number of Medical topics and Subjects. This paper addresses the aforementioned limitations by introducing MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. ### Source Data #### Initial Data Collection and Normalization Historical Exam questions from official websites - AIIMS & NEET PG (1991- present) The raw data is collected from open websites and books #### Who are the source language producers? The dataset was created by Ankit Pal, Logesh Kumar Umapathi and Malaikannan Sankarasubbu ### Annotations #### Annotation process The dataset does not contain any additional annotations. #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information If you find this useful in your research, please consider citing the dataset paper ``` @InProceedings{pmlr-v174-pal22a, title = {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering}, author = {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan}, booktitle = {Proceedings of the Conference on Health, Inference, and Learning}, pages = {248--260}, year = {2022}, editor = {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan}, volume = {174}, series = {Proceedings of Machine Learning Research}, month = {07--08 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf}, url = {https://proceedings.mlr.press/v174/pal22a.html}, abstract = {This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS &amp; NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects &amp; topics. A detailed explanation of the solution, along with the above information, is provided in this study.} } ``` ### Contributions Thanks to [@monk1337](https://github.com/monk1337) for adding this dataset.

common-canvas/commoncatalog-cc-by

common-canvas

--- license: cc-by-4.0 dataset_info: features: - name: jpg dtype: image - name: blip2_caption dtype: string - name: caption dtype: string - name: licensename dtype: string - name: licenseurl dtype: string - name: width dtype: int32 - name: height dtype: int32 - name: original_width dtype: int32 - name: original_height dtype: int32 - name: photoid dtype: int64 - name: uid dtype: string - name: unickname dtype: string - name: datetaken dtype: timestamp[us] - name: dateuploaded dtype: int64 - name: capturedevice dtype: string - name: title dtype: string - name: usertags dtype: string - name: machinetags dtype: string - name: longitude dtype: float64 - name: latitude dtype: float64 - name: accuracy dtype: int64 - name: pageurl dtype: string - name: downloadurl dtype: string - name: serverid dtype: int64 - name: farmid dtype: int64 - name: secret dtype: string - name: secretoriginal dtype: string - name: ext dtype: string - name: url dtype: string - name: key dtype: string - name: status dtype: string - name: error_message dtype: string - name: exif dtype: string - name: sha256 dtype: string - name: description dtype: string task_categories: - text-to-image language: - en --- # Dataset Card for CommonCatalog CC-BY This dataset is a large collection of high-resolution Creative Common images (composed of different licenses, see paper Table 1 in the Appendix) collected in 2014 from users of Yahoo Flickr. The dataset contains images of up to 4k resolution, making this one of the highest resolution captioned image datasets. ## Dataset Details ### Dataset Description We provide captions synthetic captions to approximately 100 million high resolution images collected from Yahoo Flickr Creative Commons (YFCC). - **Curated by:** Aaron Gokaslan - **Language(s) (NLP):** en - **License:** See relevant yaml tag / dataset name. ### Dataset Sources <!-- Provide the basic links for the dataset. --> - **Repository:** https://github.com/mosaicml/diffusion - **Paper:** https://arxiv.org/abs/2310.16825 - **Demo:** See CommonCanvas Gradios ## Uses We use CommonCatalog to train a family latent diffusion models called CommonCanvas. The goal is to produce a model that is competitive with Stable Diffusion 2, but to do so using an easily accessible dataset of known provenance. Doing so makes replicating the model significantly easier, and provides a clearer mechanism for applying training-data attribution techniques. ### Direct Use Training text-to-image models Training image-to-text models ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> * Crafting content that is offensive or injurious towards individuals, including negative portrayals of their living conditions, cultural backgrounds, religious beliefs, etc. * Deliberately creating or spreading content that is discriminatory or reinforces harmful stereotypes. * Falsely representing individuals without their permission. * Generating sexual content that may be seen by individuals without their consent. * Producing or disseminating false or misleading information. * Creating content that depicts extreme violence or bloodshed. * Distributing content that modifies copyrighted or licensed material in a way that breaches its usage terms. ## Dataset Structure The dataset is divided into 10 subsets each containing parquets about 4GB each. Each subfolder within contains a resolution range of the images and their respective aspect ratios. The dataset is also divided along images licensed for commercial use (C) and those that are not (NC). ## Dataset Creation ### Curation Rationale Creating a standardized, accessible dataset with synthetic caption and releasing it so other people can train on a common dataset for open source image generation. ### Source Data Yahoo Flickr Creative Commons 100M Dataset and Synthetically Generated Caption Data. #### Data Collection and Processing All synthetic captions were generated with BLIP2. See paper for more details. #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> Users of Flickr ## Bias, Risks, and Limitations See Yahoo Flickr Creative Commons 100M dataset for more information. The information was collected circa 2014 and known to have a bias towards internet connected Western countries. Some areas such as the global south lack representation. ## Citation **BibTeX:** ``` @article{gokaslan2023commoncanvas, title={CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images}, author={Gokaslan, Aaron and Cooper, A Feder and Collins, Jasmine and Seguin, Landan and Jacobson, Austin and Patel, Mihir and Frankle, Jonathan and Stephenson, Cory and Kuleshov, Volodymyr}, journal={arXiv preprint arXiv:2310.16825}, year={2023} } ``` ## Dataset Card Authors [Aaron Gokaslan](https://huggingface.co/Skylion007) ## Dataset Card Contact [Aaron Gokaslan](https://huggingface.co/Skylion007)

andstor/methods2test_small

andstor

--- language: - en license: mit task_categories: - text-generation configs: - config_name: fm data_files: - split: train path: data/fm/train-* - split: test path: data/fm/test-* - split: validation path: data/fm/validation-* - config_name: fm_indented data_files: - split: train path: data/fm_indented/train-* - split: test path: data/fm_indented/test-* - split: validation path: data/fm_indented/validation-* - config_name: fm+t data_files: - split: train path: data/fm+t/train-* - split: test path: data/fm+t/test-* - split: validation path: data/fm+t/validation-* - config_name: fm+fc data_files: - split: train path: data/fm+fc/train-* - split: test path: data/fm+fc/test-* - split: validation path: data/fm+fc/validation-* - config_name: fm+fc+t+tc data_files: - split: train path: data/fm+fc+t+tc/train-* - split: test path: data/fm+fc+t+tc/test-* - split: validation path: data/fm+fc+t+tc/validation-* - config_name: fm+fc+c data_files: - split: train path: data/fm+fc+c/train-* - split: test path: data/fm+fc+c/test-* - split: validation path: data/fm+fc+c/validation-* - config_name: fm+fc+c+t+tc data_files: - split: train path: data/fm+fc+c+t+tc/train-* - split: test path: data/fm+fc+c+t+tc/test-* - split: validation path: data/fm+fc+c+t+tc/validation-* - config_name: fm+fc+c+m data_files: - split: train path: data/fm+fc+c+m/train-* - split: test path: data/fm+fc+c+m/test-* - split: validation path: data/fm+fc+c+m/validation-* - config_name: fm+fc+c+m+t+tc data_files: - split: train path: data/fm+fc+c+m+t+tc/train-* - split: test path: data/fm+fc+c+m+t+tc/test-* - split: validation path: data/fm+fc+c+m+t+tc/validation-* - config_name: fm+fc+c+m+f data_files: - split: train path: data/fm+fc+c+m+f/train-* - split: test path: data/fm+fc+c+m+f/test-* - split: validation path: data/fm+fc+c+m+f/validation-* - config_name: fm+fc+c+m+f+t+tc data_files: - split: train path: data/fm+fc+c+m+f+t+tc/train-* - split: test path: data/fm+fc+c+m+f+t+tc/test-* - split: validation path: data/fm+fc+c+m+f+t+tc/validation-* - config_name: t data_files: - split: train path: data/t/train-* - split: test path: data/t/test-* - split: validation path: data/t/validation-* - config_name: t_indented data_files: - split: train path: data/t_indented/train-* - split: test path: data/t_indented/test-* - split: validation path: data/t_indented/validation-* - config_name: t+tc data_files: - split: train path: data/t+tc/train-* - split: test path: data/t+tc/test-* - split: validation path: data/t+tc/validation-* dataset_info: - config_name: fm features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 4696431 num_examples: 7440 - name: test num_bytes: 642347 num_examples: 1017 - name: validation num_bytes: 662917 num_examples: 953 download_size: 2633268 dataset_size: 6001695 - config_name: fm+fc features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 5387123 num_examples: 7440 - name: test num_bytes: 738049 num_examples: 1017 - name: validation num_bytes: 757167 num_examples: 953 download_size: 2925807 dataset_size: 6882339 - config_name: fm+fc+c features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 5906873 num_examples: 7440 - name: test num_bytes: 820149 num_examples: 1017 - name: validation num_bytes: 824441 num_examples: 953 download_size: 3170873 dataset_size: 7551463 - config_name: fm+fc+c+m features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 11930672 num_examples: 7440 - name: test num_bytes: 1610045 num_examples: 1017 - name: validation num_bytes: 1553249 num_examples: 953 download_size: 5406454 dataset_size: 15093966 - config_name: fm+fc+c+m+f features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 12722890 num_examples: 7440 - name: test num_bytes: 1713683 num_examples: 1017 - name: validation num_bytes: 1654607 num_examples: 953 download_size: 5753116 dataset_size: 16091180 - config_name: fm+fc+c+m+f+t+tc features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 18332635 num_examples: 7440 - name: test num_bytes: 2461169 num_examples: 1017 - name: validation num_bytes: 2510969 num_examples: 953 download_size: 8280985 dataset_size: 23304773 - config_name: fm+fc+c+m+t+tc features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 17537661 num_examples: 7440 - name: test num_bytes: 2357359 num_examples: 1017 - name: validation num_bytes: 2409506 num_examples: 953 download_size: 8178222 dataset_size: 22304526 - config_name: fm+fc+c+t+tc features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 11445562 num_examples: 7440 - name: test num_bytes: 1565365 num_examples: 1017 - name: validation num_bytes: 1676986 num_examples: 953 download_size: 5944482 dataset_size: 14687913 - config_name: fm+fc+t+tc features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 10923038 num_examples: 7440 - name: test num_bytes: 1483265 num_examples: 1017 - name: validation num_bytes: 1609296 num_examples: 953 download_size: 5715335 dataset_size: 14015599 - config_name: fm+t features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 8889443 num_examples: 7440 - name: test num_bytes: 1207763 num_examples: 1017 - name: validation num_bytes: 1336798 num_examples: 953 download_size: 4898458 dataset_size: 11434004 - config_name: fm_indented features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 5054397 num_examples: 7440 - name: test num_bytes: 692948 num_examples: 1017 - name: validation num_bytes: 714462 num_examples: 953 download_size: 2703115 dataset_size: 6461807 - config_name: t features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 4316096 num_examples: 7440 - name: test num_bytes: 582266 num_examples: 1017 - name: validation num_bytes: 689647 num_examples: 953 download_size: 2434024 dataset_size: 5588009 - config_name: t+tc features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 5648321 num_examples: 7440 - name: test num_bytes: 761386 num_examples: 1017 - name: validation num_bytes: 867350 num_examples: 953 download_size: 3024686 dataset_size: 7277057 - config_name: t_indented features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 4606253 num_examples: 7440 - name: test num_bytes: 623576 num_examples: 1017 - name: validation num_bytes: 734221 num_examples: 953 download_size: 2496661 dataset_size: 5964050 tags: - unit test - java - code pretty_name: Methods2Test Small --- ## Dataset Description Microsoft created the `methods2test` dataset, consisting of Java Junit test cases with their corresponding focal methods. It contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K Java open-source projects hosted on GitHub. This is a smaller subset of the assembled version of the `methods2test` dataset. It provides convenient access to the different context levels based on the raw source code (e.g. newlines are preserved). The test cases and associated classes are also made available. The subset is created by randomly selecting only one sample from each of the 91k projects. The mapping between test case and focal methods is based on heuristics rules and Java developer's best practice. More information can be found here: - [methods2test Github repo](https://github.com/microsoft/methods2test) - [Methods2Test: A dataset of focal methods mapped to test cases](https://arxiv.org/pdf/2203.12776.pdf) ## Dataset Schema ``` t: <TEST_CASE> t+tc: <TEST_CLASS_NAME> <TEST_CASE> fm: <FOCAL_METHOD> fm+t: <FOCAL_METHOD> fm+fc: <FOCAL_CLASS_NAME> <FOCAL_METHOD> fm+fc: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <TEST_CLASS_NAME> <TEST_CASE> fm+fc+c: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> fm+fc+c: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <TEST_CLASS_NAME> <TEST_CASE> fm+fc+c+m: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> fm+fc+c+m: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> <TEST_CLASS_NAME> <TEST_CASE> fm+fc+c+m+f: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> <FIELDS> fm+fc+c+m+f+t+tc: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> <FIELDS> <TEST_CLASS_NAME> <TEST_CASE> ``` ## Focal Context - fm: this representation incorporates exclusively the source code of the focal method. Intuitively, this contains the most important information for generating accurate test cases for the given method. - fm+fc: this representation adds the focal class name, which can provide meaningful semantic information to the model. - fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this augmentation is that the test case may require instantiating an object of the focal class in order to properly test the focal method. - fm+fc+c+m: this representation adds the signatures of the other public methods in the focal class. The rationale that motivated this inclusion is that the test case may need to invoke other auxiliary methods within the class (e.g., getters, setters) to set up or tear down the testing environment. - fm+fc+c+m+f: this representation adds the public fields of the focal class. The motivation is that test cases may need to inspect the status of the public fields to properly test a focal method. The test case along with the class name is also provided for each focal context.  The different levels of focal contexts are the following: ``` fm: focal method fm+fc: focal method + focal class name fm+fc+c: focal method + focal class name + constructor signatures fm+fc+c+m: focal method + focal class name + constructor signatures + public method signatures fm+fc+c+m+f: focal method + focal class name + constructor signatures + public method signatures + public fields ```

google-research-datasets/natural_questions

google-research-datasets

--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: natural-questions pretty_name: Natural Questions dataset_info: - config_name: default features: - name: id dtype: string - name: document struct: - name: html dtype: string - name: title dtype: string - name: tokens sequence: - name: end_byte dtype: int64 - name: is_html dtype: bool - name: start_byte dtype: int64 - name: token dtype: string - name: url dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: long_answer_candidates sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: short_answers sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' splits: - name: train num_bytes: 143039948860 num_examples: 307373 - name: validation num_bytes: 3451288641 num_examples: 7830 download_size: 56843626971 dataset_size: 146491237501 - config_name: dev features: - name: id dtype: string - name: document struct: - name: title dtype: string - name: url dtype: string - name: html dtype: string - name: tokens sequence: - name: token dtype: string - name: is_html dtype: bool - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: long_answer_candidates sequence: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: top_level dtype: bool - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: candidate_index dtype: int64 - name: short_answers sequence: - name: start_token dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: end_byte dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' splits: - name: validation num_bytes: 3451288639 num_examples: 7830 download_size: 1337126358 dataset_size: 3451288639 configs: - config_name: default data_files: - split: train path: default/train-* - split: validation path: default/validation-* - config_name: dev data_files: - split: validation path: dev/validation-* --- # Dataset Card for Natural Questions ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://ai.google.com/research/NaturalQuestions/dataset](https://ai.google.com/research/NaturalQuestions/dataset) - **Repository:** [https://github.com/google-research-datasets/natural-questions](https://github.com/google-research-datasets/natural-questions) - **Paper:** [https://research.google/pubs/pub47761/](https://research.google/pubs/pub47761/) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 45.07 GB - **Size of the generated dataset:** 99.80 GB - **Total amount of disk used:** 144.87 GB ### Dataset Summary The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not contain the answer to the question. The inclusion of real user questions, and the requirement that solutions should read an entire page to find the answer, cause NQ to be a more realistic and challenging task than prior QA datasets. ### Supported Tasks and Leaderboards [https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions) ### Languages en ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 45.07 GB - **Size of the generated dataset:** 99.80 GB - **Total amount of disk used:** 144.87 GB An example of 'train' looks as follows. This is a toy example. ``` { "id": "797803103760793766", "document": { "title": "Google", "url": "http://www.wikipedia.org/Google", "html": "<html><body><h1>Google Inc.</h1><p>Google was founded in 1998 By:<ul><li>Larry</li><li>Sergey</li></ul></p></body></html>", "tokens":[ {"token": "<h1>", "start_byte": 12, "end_byte": 16, "is_html": True}, {"token": "Google", "start_byte": 16, "end_byte": 22, "is_html": False}, {"token": "inc", "start_byte": 23, "end_byte": 26, "is_html": False}, {"token": ".", "start_byte": 26, "end_byte": 27, "is_html": False}, {"token": "</h1>", "start_byte": 27, "end_byte": 32, "is_html": True}, {"token": "<p>", "start_byte": 32, "end_byte": 35, "is_html": True}, {"token": "Google", "start_byte": 35, "end_byte": 41, "is_html": False}, {"token": "was", "start_byte": 42, "end_byte": 45, "is_html": False}, {"token": "founded", "start_byte": 46, "end_byte": 53, "is_html": False}, {"token": "in", "start_byte": 54, "end_byte": 56, "is_html": False}, {"token": "1998", "start_byte": 57, "end_byte": 61, "is_html": False}, {"token": "by", "start_byte": 62, "end_byte": 64, "is_html": False}, {"token": ":", "start_byte": 64, "end_byte": 65, "is_html": False}, {"token": "<ul>", "start_byte": 65, "end_byte": 69, "is_html": True}, {"token": "<li>", "start_byte": 69, "end_byte": 73, "is_html": True}, {"token": "Larry", "start_byte": 73, "end_byte": 78, "is_html": False}, {"token": "</li>", "start_byte": 78, "end_byte": 83, "is_html": True}, {"token": "<li>", "start_byte": 83, "end_byte": 87, "is_html": True}, {"token": "Sergey", "start_byte": 87, "end_byte": 92, "is_html": False}, {"token": "</li>", "start_byte": 92, "end_byte": 97, "is_html": True}, {"token": "</ul>", "start_byte": 97, "end_byte": 102, "is_html": True}, {"token": "</p>", "start_byte": 102, "end_byte": 106, "is_html": True} ], }, "question" :{ "text": "who founded google", "tokens": ["who", "founded", "google"] }, "long_answer_candidates": [ {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "top_level": True}, {"start_byte": 65, "end_byte": 102, "start_token": 13, "end_token": 21, "top_level": False}, {"start_byte": 69, "end_byte": 83, "start_token": 14, "end_token": 17, "top_level": False}, {"start_byte": 83, "end_byte": 92, "start_token": 17, "end_token": 20 , "top_level": False} ], "annotations": [{ "id": "6782080525527814293", "long_answer": {"start_byte": 32, "end_byte": 106, "start_token": 5, "end_token": 22, "candidate_index": 0}, "short_answers": [ {"start_byte": 73, "end_byte": 78, "start_token": 15, "end_token": 16, "text": "Larry"}, {"start_byte": 87, "end_byte": 92, "start_token": 18, "end_token": 19, "text": "Sergey"} ], "yes_no_answer": -1 }] } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `document` a dictionary feature containing: - `title`: a `string` feature. - `url`: a `string` feature. - `html`: a `string` feature. - `tokens`: a dictionary feature containing: - `token`: a `string` feature. - `is_html`: a `bool` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `question`: a dictionary feature containing: - `text`: a `string` feature. - `tokens`: a `list` of `string` features. - `long_answer_candidates`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `top_level`: a `bool` feature. - `annotations`: a dictionary feature containing: - `id`: a `string` feature. - `long_answers`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `candidate_index`: a `int64` feature. - `short_answers`: a dictionary feature containing: - `start_token`: a `int64` feature. - `end_token`: a `int64` feature. - `start_byte`: a `int64` feature. - `end_byte`: a `int64` feature. - `text`: a `string` feature. - `yes_no_answer`: a classification label, with possible values including `NO` (0), `YES` (1). ### Data Splits | name | train | validation | |---------|-------:|-----------:| | default | 307373 | 7830 | | dev | N/A | 7830 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [Creative Commons Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{47761, title = {Natural Questions: a Benchmark for Question Answering Research}, author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov}, year = {2019}, journal = {Transactions of the Association of Computational Linguistics} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

ikala/tmmluplus

ikala

--- license: mit license_name: mit task_categories: - question-answering language: - zh tags: - traditional chinese - finance - medical - taiwan - benchmark - zh-tw - zh-hant pretty_name: tmmlu++ size_categories: - 100K<n<1M configs: - config_name: engineering_math data_files: - split: train path: "data/engineering_math_dev.csv" - split: validation path: "data/engineering_math_val.csv" - split: test path: "data/engineering_math_test.csv" - config_name: dentistry data_files: - split: train path: "data/dentistry_dev.csv" - split: validation path: "data/dentistry_val.csv" - split: test path: "data/dentistry_test.csv" - config_name: traditional_chinese_medicine_clinical_medicine data_files: - split: train path: "data/traditional_chinese_medicine_clinical_medicine_dev.csv" - split: validation path: "data/traditional_chinese_medicine_clinical_medicine_val.csv" - split: test path: "data/traditional_chinese_medicine_clinical_medicine_test.csv" - config_name: clinical_psychology data_files: - split: train path: "data/clinical_psychology_dev.csv" - split: validation path: "data/clinical_psychology_val.csv" - split: test path: "data/clinical_psychology_test.csv" - config_name: technical data_files: - split: train path: "data/technical_dev.csv" - split: validation path: "data/technical_val.csv" - split: test path: "data/technical_test.csv" - config_name: culinary_skills data_files: - split: train path: "data/culinary_skills_dev.csv" - split: validation path: "data/culinary_skills_val.csv" - split: test path: "data/culinary_skills_test.csv" - config_name: mechanical data_files: - split: train path: "data/mechanical_dev.csv" - split: validation path: "data/mechanical_val.csv" - split: test path: "data/mechanical_test.csv" - config_name: logic_reasoning data_files: - split: train path: "data/logic_reasoning_dev.csv" - split: validation path: "data/logic_reasoning_val.csv" - split: test path: "data/logic_reasoning_test.csv" - config_name: real_estate data_files: - split: train path: "data/real_estate_dev.csv" - split: validation path: "data/real_estate_val.csv" - split: test path: "data/real_estate_test.csv" - config_name: general_principles_of_law data_files: - split: train path: "data/general_principles_of_law_dev.csv" - split: validation path: "data/general_principles_of_law_val.csv" - split: test path: "data/general_principles_of_law_test.csv" - config_name: finance_banking data_files: - split: train path: "data/finance_banking_dev.csv" - split: validation path: "data/finance_banking_val.csv" - split: test path: "data/finance_banking_test.csv" - config_name: anti_money_laundering data_files: - split: train path: "data/anti_money_laundering_dev.csv" - split: validation path: "data/anti_money_laundering_val.csv" - split: test path: "data/anti_money_laundering_test.csv" - config_name: ttqav2 data_files: - split: train path: "data/ttqav2_dev.csv" - split: validation path: "data/ttqav2_val.csv" - split: test path: "data/ttqav2_test.csv" - config_name: marketing_management data_files: - split: train path: "data/marketing_management_dev.csv" - split: validation path: "data/marketing_management_val.csv" - split: test path: "data/marketing_management_test.csv" - config_name: business_management data_files: - split: train path: "data/business_management_dev.csv" - split: validation path: "data/business_management_val.csv" - split: test path: "data/business_management_test.csv" - config_name: organic_chemistry data_files: - split: train path: "data/organic_chemistry_dev.csv" - split: validation path: "data/organic_chemistry_val.csv" - split: test path: "data/organic_chemistry_test.csv" - config_name: advance_chemistry data_files: - split: train path: "data/advance_chemistry_dev.csv" - split: validation path: "data/advance_chemistry_val.csv" - split: test path: "data/advance_chemistry_test.csv" - config_name: physics data_files: - split: train path: "data/physics_dev.csv" - split: validation path: "data/physics_val.csv" - split: test path: "data/physics_test.csv" - config_name: secondary_physics data_files: - split: train path: "data/secondary_physics_dev.csv" - split: validation path: "data/secondary_physics_val.csv" - split: test path: "data/secondary_physics_test.csv" - config_name: human_behavior data_files: - split: train path: "data/human_behavior_dev.csv" - split: validation path: "data/human_behavior_val.csv" - split: test path: "data/human_behavior_test.csv" - config_name: national_protection data_files: - split: train path: "data/national_protection_dev.csv" - split: validation path: "data/national_protection_val.csv" - split: test path: "data/national_protection_test.csv" - config_name: jce_humanities data_files: - split: train path: "data/jce_humanities_dev.csv" - split: validation path: "data/jce_humanities_val.csv" - split: test path: "data/jce_humanities_test.csv" - config_name: politic_science data_files: - split: train path: "data/politic_science_dev.csv" - split: validation path: "data/politic_science_val.csv" - split: test path: "data/politic_science_test.csv" - config_name: agriculture data_files: - split: train path: "data/agriculture_dev.csv" - split: validation path: "data/agriculture_val.csv" - split: test path: "data/agriculture_test.csv" - config_name: official_document_management data_files: - split: train path: "data/official_document_management_dev.csv" - split: validation path: "data/official_document_management_val.csv" - split: test path: "data/official_document_management_test.csv" - config_name: financial_analysis data_files: - split: train path: "data/financial_analysis_dev.csv" - split: validation path: "data/financial_analysis_val.csv" - split: test path: "data/financial_analysis_test.csv" - config_name: pharmacy data_files: - split: train path: "data/pharmacy_dev.csv" - split: validation path: "data/pharmacy_val.csv" - split: test path: "data/pharmacy_test.csv" - config_name: educational_psychology data_files: - split: train path: "data/educational_psychology_dev.csv" - split: validation path: "data/educational_psychology_val.csv" - split: test path: "data/educational_psychology_test.csv" - config_name: statistics_and_machine_learning data_files: - split: train path: "data/statistics_and_machine_learning_dev.csv" - split: validation path: "data/statistics_and_machine_learning_val.csv" - split: test path: "data/statistics_and_machine_learning_test.csv" - config_name: management_accounting data_files: - split: train path: "data/management_accounting_dev.csv" - split: validation path: "data/management_accounting_val.csv" - split: test path: "data/management_accounting_test.csv" - config_name: introduction_to_law data_files: - split: train path: "data/introduction_to_law_dev.csv" - split: validation path: "data/introduction_to_law_val.csv" - split: test path: "data/introduction_to_law_test.csv" - config_name: computer_science data_files: - split: train path: "data/computer_science_dev.csv" - split: validation path: "data/computer_science_val.csv" - split: test path: "data/computer_science_test.csv" - config_name: veterinary_pathology data_files: - split: train path: "data/veterinary_pathology_dev.csv" - split: validation path: "data/veterinary_pathology_val.csv" - split: test path: "data/veterinary_pathology_test.csv" - config_name: accounting data_files: - split: train path: "data/accounting_dev.csv" - split: validation path: "data/accounting_val.csv" - split: test path: "data/accounting_test.csv" - config_name: fire_science data_files: - split: train path: "data/fire_science_dev.csv" - split: validation path: "data/fire_science_val.csv" - split: test path: "data/fire_science_test.csv" - config_name: optometry data_files: - split: train path: "data/optometry_dev.csv" - split: validation path: "data/optometry_val.csv" - split: test path: "data/optometry_test.csv" - config_name: insurance_studies data_files: - split: train path: "data/insurance_studies_dev.csv" - split: validation path: "data/insurance_studies_val.csv" - split: test path: "data/insurance_studies_test.csv" - config_name: pharmacology data_files: - split: train path: "data/pharmacology_dev.csv" - split: validation path: "data/pharmacology_val.csv" - split: test path: "data/pharmacology_test.csv" - config_name: taxation data_files: - split: train path: "data/taxation_dev.csv" - split: validation path: "data/taxation_val.csv" - split: test path: "data/taxation_test.csv" - config_name: trust_practice data_files: - split: train path: "data/trust_practice_dev.csv" - split: validation path: "data/trust_practice_val.csv" - split: test path: "data/trust_practice_test.csv" - config_name: geography_of_taiwan data_files: - split: train path: "data/geography_of_taiwan_dev.csv" - split: validation path: "data/geography_of_taiwan_val.csv" - split: test path: "data/geography_of_taiwan_test.csv" - config_name: physical_education data_files: - split: train path: "data/physical_education_dev.csv" - split: validation path: "data/physical_education_val.csv" - split: test path: "data/physical_education_test.csv" - config_name: auditing data_files: - split: train path: "data/auditing_dev.csv" - split: validation path: "data/auditing_val.csv" - split: test path: "data/auditing_test.csv" - config_name: administrative_law data_files: - split: train path: "data/administrative_law_dev.csv" - split: validation path: "data/administrative_law_val.csv" - split: test path: "data/administrative_law_test.csv" - config_name: education_(profession_level) data_files: - split: train path: "data/education_(profession_level)_dev.csv" - split: validation path: "data/education_(profession_level)_val.csv" - split: test path: "data/education_(profession_level)_test.csv" - config_name: economics data_files: - split: train path: "data/economics_dev.csv" - split: validation path: "data/economics_val.csv" - split: test path: "data/economics_test.csv" - config_name: veterinary_pharmacology data_files: - split: train path: "data/veterinary_pharmacology_dev.csv" - split: validation path: "data/veterinary_pharmacology_val.csv" - split: test path: "data/veterinary_pharmacology_test.csv" - config_name: nautical_science data_files: - split: train path: "data/nautical_science_dev.csv" - split: validation path: "data/nautical_science_val.csv" - split: test path: "data/nautical_science_test.csv" - config_name: occupational_therapy_for_psychological_disorders data_files: - split: train path: "data/occupational_therapy_for_psychological_disorders_dev.csv" - split: validation path: "data/occupational_therapy_for_psychological_disorders_val.csv" - split: test path: "data/occupational_therapy_for_psychological_disorders_test.csv" - config_name: basic_medical_science data_files: - split: train path: "data/basic_medical_science_dev.csv" - split: validation path: "data/basic_medical_science_val.csv" - split: test path: "data/basic_medical_science_test.csv" - config_name: macroeconomics data_files: - split: train path: "data/macroeconomics_dev.csv" - split: validation path: "data/macroeconomics_val.csv" - split: test path: "data/macroeconomics_test.csv" - config_name: trade data_files: - split: train path: "data/trade_dev.csv" - split: validation path: "data/trade_val.csv" - split: test path: "data/trade_test.csv" - config_name: chinese_language_and_literature data_files: - split: train path: "data/chinese_language_and_literature_dev.csv" - split: validation path: "data/chinese_language_and_literature_val.csv" - split: test path: "data/chinese_language_and_literature_test.csv" - config_name: tve_design data_files: - split: train path: "data/tve_design_dev.csv" - split: validation path: "data/tve_design_val.csv" - split: test path: "data/tve_design_test.csv" - config_name: junior_science_exam data_files: - split: train path: "data/junior_science_exam_dev.csv" - split: validation path: "data/junior_science_exam_val.csv" - split: test path: "data/junior_science_exam_test.csv" - config_name: junior_math_exam data_files: - split: train path: "data/junior_math_exam_dev.csv" - split: validation path: "data/junior_math_exam_val.csv" - split: test path: "data/junior_math_exam_test.csv" - config_name: junior_chinese_exam data_files: - split: train path: "data/junior_chinese_exam_dev.csv" - split: validation path: "data/junior_chinese_exam_val.csv" - split: test path: "data/junior_chinese_exam_test.csv" - config_name: junior_social_studies data_files: - split: train path: "data/junior_social_studies_dev.csv" - split: validation path: "data/junior_social_studies_val.csv" - split: test path: "data/junior_social_studies_test.csv" - config_name: tve_mathematics data_files: - split: train path: "data/tve_mathematics_dev.csv" - split: validation path: "data/tve_mathematics_val.csv" - split: test path: "data/tve_mathematics_test.csv" - config_name: tve_chinese_language data_files: - split: train path: "data/tve_chinese_language_dev.csv" - split: validation path: "data/tve_chinese_language_val.csv" - split: test path: "data/tve_chinese_language_test.csv" - config_name: tve_natural_sciences data_files: - split: train path: "data/tve_natural_sciences_dev.csv" - split: validation path: "data/tve_natural_sciences_val.csv" - split: test path: "data/tve_natural_sciences_test.csv" - config_name: junior_chemistry data_files: - split: train path: "data/junior_chemistry_dev.csv" - split: validation path: "data/junior_chemistry_val.csv" - split: test path: "data/junior_chemistry_test.csv" - config_name: music data_files: - split: train path: "data/music_dev.csv" - split: validation path: "data/music_val.csv" - split: test path: "data/music_test.csv" - config_name: education data_files: - split: train path: "data/education_dev.csv" - split: validation path: "data/education_val.csv" - split: test path: "data/education_test.csv" - config_name: three_principles_of_people data_files: - split: train path: "data/three_principles_of_people_dev.csv" - split: validation path: "data/three_principles_of_people_val.csv" - split: test path: "data/three_principles_of_people_test.csv" - config_name: taiwanese_hokkien data_files: - split: train path: "data/taiwanese_hokkien_dev.csv" - split: validation path: "data/taiwanese_hokkien_val.csv" - split: test path: "data/taiwanese_hokkien_test.csv" --- # TMMLU+ : Large scale traditional chinese massive multitask language understanding <p align="center"> <img src="https://huggingface.co/datasets/ikala/tmmluplus/resolve/main/cover.png" alt="A close-up image of a neat paper note with a white background. The text 'TMMLU+' is written horizontally across the center of the note in bold, black. Join us to work in multimodal LLM : https://ikala.ai/recruit/" style="max-width: 400" width=400 /> </p> We present TMMLU+, a traditional Chinese massive multitask language understanding dataset. TMMLU+ is a multiple-choice question-answering dataset featuring 66 subjects, ranging from elementary to professional level. The TMMLU+ dataset is six times larger and contains more balanced subjects compared to its predecessor, [TMMLU](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval/data/TMMLU). We have included benchmark results in TMMLU+ from closed-source models and 20 open-weight Chinese large language models, with parameters ranging from 1.8B to 72B. The benchmark results show that Traditional Chinese variants still lag behind those trained on major Simplified Chinese models. ```python from datasets import load_dataset task_list = [ 'engineering_math', 'dentistry', 'traditional_chinese_medicine_clinical_medicine', 'clinical_psychology', 'technical', 'culinary_skills', 'mechanical', 'logic_reasoning', 'real_estate', 'general_principles_of_law', 'finance_banking', 'anti_money_laundering', 'ttqav2', 'marketing_management', 'business_management', 'organic_chemistry', 'advance_chemistry', 'physics', 'secondary_physics', 'human_behavior', 'national_protection', 'jce_humanities', 'politic_science', 'agriculture', 'official_document_management', 'financial_analysis', 'pharmacy', 'educational_psychology', 'statistics_and_machine_learning', 'management_accounting', 'introduction_to_law', 'computer_science', 'veterinary_pathology', 'accounting', 'fire_science', 'optometry', 'insurance_studies', 'pharmacology', 'taxation', 'trust_practice', 'geography_of_taiwan', 'physical_education', 'auditing', 'administrative_law', 'education_(profession_level)', 'economics', 'veterinary_pharmacology', 'nautical_science', 'occupational_therapy_for_psychological_disorders', 'basic_medical_science', 'macroeconomics', 'trade', 'chinese_language_and_literature', 'tve_design', 'junior_science_exam', 'junior_math_exam', 'junior_chinese_exam', 'junior_social_studies', 'tve_mathematics', 'tve_chinese_language', 'tve_natural_sciences', 'junior_chemistry', 'music', 'education', 'three_principles_of_people', 'taiwanese_hokkien' ] for task in task_list: val = load_dataset('ikala/tmmluplus', task)['validation'] dev = load_dataset('ikala/tmmluplus', task)['train'] test = load_dataset('ikala/tmmluplus', task)['test'] ``` For each dataset split ```python for row in test: print(row) break >> Dataset({ features: ['question', 'A', 'B', 'C', 'D', 'answer'], num_rows: 11 }) ``` Statistic on all four categories : STEM, Social Science, Humanities, Other | Category | Test | Dev | Validation | |----------------------------------|-------|------|------------| | STEM | 3458 | 70 | 385 | | Social Sciences | 5958 | 90 | 665 | | Humanities | 1763 | 35 | 197 | | Other (Business, Health, Misc.) | 8939 | 135 | 995 | | **Total** | 20118 | 330 | 2242 | ## Benchmark on direct prompting | model | STEM | Social Science | Humanities | Other | Average | |------------|------------|------------|------------|------------|------------| |Gemini-1.5-pro | 66.18|70.29|61.84|60.30|64.65| | [Qwen/Qwen-72B](https://huggingface.co/Qwen/Qwen-72B) | 61.12 | 71.65 | 63.00 | 61.31 |64.27| | gpt-4-0613 | 60.36 | 67.36 | 56.03 | 57.62 |60.34| | Qwen-max | 59.92 | 66.95 | 57.43 | 56.48 |60.20| | [Qwen/Qwen-72B-Chat](https://huggingface.co/Qwen/Qwen-72B-Chat) | 55.15 | 66.20 | 55.65 | 57.19 |58.55| | [Qwen/Qwen-14B](https://huggingface.co/Qwen/Qwen-14B) | 46.94 | 56.69 | 49.43 | 48.81 |50.47| | Gemini-pro | 45.38 | 57.29 | 48.80 | 48.21 |49.92| | [01-ai/Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) | 40.24 | 56.77 | 53.99 | 47.58 |49.64| | Gemini-1.5-flash |53.47|53.42|42.99|46.56|49.11| | [Reka Flash](https://www.reka.ai/)|45.26|52.91|46.31|43.76|47.06| | [Qwen/Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat) | 43.86 | 53.29 | 44.78 | 45.13 |46.77| | [Qwen/Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat)|39.65|52.76|43.90|44.95|45.31| | [01-ai/Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) | 39.62 | 50.24 | 44.44 | 44.26 |44.64| | Claude-1.3 | 42.65 | 49.33 | 42.16 | 44.14 |44.57| | [MediaTek-Research/Breeze-7B-Instruct-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1)| 36.46 | 48.38 |45.11 |40.75 | 42.67 | | gpt-3.5-turbo-0613 | 41.56 | 46.72 | 36.73 | 42.03 |41.76| | [CausalLM/14B](https://huggingface.co/CausalLM/14B) | 39.83 | 44.50 | 39.61 | 41.97 |41.48| | [Skywork/Skywork-13B-base](https://huggingface.co/Skywork/Skywork-13B-base) | 36.93 | 47.27 | 41.04 | 40.10 |41.33| | Claude-3-opus |42.95|45.49|35.79|40.24|41.12| | [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B) | 37.53 | 45.48 | 38.09 | 38.96 |40.01| | [meta-llama/Llama-3-70b-chat-hf](https://docs.together.ai/docs/inference-models) | 34.44 | 47.02 | 37.50 |39.51 | 39.62 | | [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) | 33.32 | 44.64 | 40.27 | 39.89 |39.53| | [vivo-ai/BlueLM-7B-Base](https://huggingface.co/vivo-ai/BlueLM-7B-Base) | 33.94 | 41.52 | 37.38 | 38.74 |37.90| | [baichuan-inc/Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) | 29.64 | 43.73 | 37.36 | 39.88 |37.65| | [Qwen/Qwen-1_8B](https://huggingface.co/Qwen/Qwen-1_8B) | 32.65 | 38.95 | 38.34 | 35.27 |36.30| | Claude-2 | 39.65 | 39.09 | 28.59 | 37.47 |36.20| | [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b) | 31.05 | 39.31 | 35.64 | 35.60 |35.40| | [deepseek-ai/deepseek-llm-7b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) | 29.82 | 42.29 | 34.24 | 34.31 |35.17| | [CausalLM/7B](https://huggingface.co/CausalLM/7B) | 31.03 | 38.17 | 35.87 | 35.39 |35.11| | [Azure99/blossom-v3_1-mistral-7b](https://huggingface.co/Azure99/blossom-v3_1-mistral-7b) | 32.80 | 36.91 | 32.36 | 34.53 |34.15| | [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) | 31.89 | 35.70 | 34.00 | 33.79 | 33.84 | | [Reka Edge](https://www.reka.ai/)|30.02|39.40|31.84|32.36|33.41| | [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) | 24.69 | 39.18 | 33.60 | 31.99 |32.37| | [Qwen/Qwen-1_8B-Chat](https://huggingface.co/Qwen/Qwen-1_8B-Chat) | 26.60 | 36.36 | 31.81 | 31.96 |31.68| | [meta-llama/Llama-3-8b-chat-hf](https://docs.together.ai/docs/inference-models) | 31.52 | 34.19 | 28.91 | 31.79 | 31.60 | | [TigerResearch/tigerbot-13b-chat-v3](https://huggingface.co/TigerResearch/tigerbot-13b-chat-v3) | 24.73 | 29.63 | 25.72 | 27.22 |26.82| | [hongyin/mistral-7b-80k](https://huggingface.co/hongyin/mistral-7b-80k) | 24.26 | 23.76 | 22.56 | 24.57 |23.79| | [deepseek-ai/deepseek-llm-67b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat) | 19.10 | 26.06 | 21.51 | 21.77 |22.11| | [yentinglin/Taiwan-LLM-13B-v2.0-chat](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-chat) | 18.53 | 27.65 | 17.77 | 21.49 |21.36| | [GeneZC/MiniChat-3B](https://huggingface.co/GeneZC/MiniChat-3B) | 17.66 | 23.35 | 22.71 | 20.34 |21.02| | [LinkSoul/Chinese-Llama-2-7b](https://huggingface.co/LinkSoul/Chinese-Llama-2-7b) | 16.55 | 18.39 | 12.97 | 16.13 |16.01| | [yentinglin/Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) | 14.99 | 16.23 | 15.00 | 16.22 |15.61| | Claude-instant-1 | 12.52 | 17.13 | 15.10 | 13.57 |14.58| | [FlagAlpha/Atom-7B](https://huggingface.co/FlagAlpha/Atom-7B) | 5.60 | 13.57 | 7.71 | 11.84 |9.68| Results via [ievals](https://github.com/iKala/ievals) ( settings : 0-shot direct answering ) # Citation ``` @article{ikala2024improved, title={An Improved Traditional Chinese Evaluation Suite for Foundation Model}, author={Tam, Zhi-Rui and Pai, Ya-Ting and Lee, Yen-Wei and Cheng, Sega and Shuai, Hong-Han}, journal={arXiv preprint arXiv:2403.01858}, year={2024} } ```

random123123/BrushData

random123123

--- license: apache-2.0 ---

NLPC-UOM/sentence_alignment_dataset-Sinhala-Tamil-English

NLPC-UOM

--- task_categories: - sentence-similarity - translation language: - si - ta - en --- ### **Dataset summary** This is a gold-standard benchmark dataset for sentence alignment, between Sinhala-English-Tamil languages. Data had been crawled from the following news websites. The aligned documents annotated in the dataset NLPC-UOM/document_alignment_dataset-Sinhala-Tamil-English had been considered to annotate the aligned sentences. | News Source | url | | ------------- |-----------------------------| | Army | https://www.army.lk/ | | Hiru | http://www.hirunews.lk | | ITN | https://www.newsfirst.lk | | Newsfirst | https://www.itnnews.lk | The aligned sentences have been manually annotated. ### **Dataset** The folder structure for each news source is as follows. ```python si-en |--army |--Sinhala |--English |--army.si-en |--hiru <br/> |--Sinhala |--English |--hiru.si-en |--itn |--Sinhala |--English |--itn.si-en |--newsfirst |--Sinhala |--English |--newsfirst.si-en ta-en si-ta ``` Sinhala/English/Tamil - contain the aligned documents in the two languages with respect to the news source. (army/hiru/itn/newsfirst) Aligned documents contain the same ID.<br/> army.si-en - golden aligned sentence alignment. Each sentence is referenced according to the languageprefix_fileid_sentenceId. <br/> ### **Citation Information** @article{fernando2022exploiting,<br/> title={Exploiting bilingual lexicons to improve multilingual embedding-based document and sentence alignment for low-resource languages},<br/> author={Fernando, Aloka and Ranathunga, Surangika and Sachintha, Dilan and Piyarathna, Lakmali and Rajitha, Charith},<br/> journal={Knowledge and Information Systems},<br/> pages={1--42},<br/> year={2022},<br/> publisher={Springer}<br/> }

Trelis/tiny-shakespeare

Trelis

--- task_categories: - text-generation language: - en tags: - fine-tuning - shakespeare size_categories: - n<1K --- # Data source Downloaded via Andrej Karpathy's nanogpt repo from this [link](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) # Data Format - The entire dataset is split into train (90%) and test (10%). - All rows are at most 1024 tokens, using the Llama 2 tokenizer. - All rows are split cleanly so that sentences are whole and unbroken.

Dahoas/rm-static

Dahoas

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 113850006 num_examples: 76256 - name: test num_bytes: 7649255 num_examples: 5103 download_size: 73006535 dataset_size: 121499261 --- # Dataset Card for "rm-static" Split of [hh-static](https://huggingface.co/datasets/Dahoas/static-hh) used for training reward models after supervised fine-tuning.

hackercupai/hackercup

hackercupai

--- license: apache-2.0 pretty_name: Meta Hacker Cup 2011-2023 tags: - code configs: - config_name: default data_files: - split: sample path: data_preview_sample_10rows.parquet - split: full path: output_dataset.parquet --- # Data Preview The data available in this preview contains a 10 row dataset: - **Sample Dataset ("sample")**: This is a subset of the full dataset, containing data from 2023. To view full dataset, download `output_dataset.parquet`. This contains data from 2011 to 2023. ## Fields The dataset include the following fields: - `name` (string) - `year` (string) - `round` (string) - `statement` (string) - `input` (string) - `solution` (string) - `code` (string) - `sample_input` (string) - `sample_output` (string) - `images` (array of base64 image strings) This dataset contains every Facebook/Meta Hacker Cup problem from 2011 through 2023. For each problem, you'll find these files: * `<problem_name>.md`: The problem statement formatted in Markdown * `<problem_name>.in`: The full input file * `<problem_name>.out`: The full output file * Note that some problems accept multiple possible outputs, in which case the full output file is simply an example of an output that would be accepted * `<problem_name>_sample_input.txt`: The sample input provided by the problem statement * `<problem_name>_sample_output.txt`: The sample output provided by the problem statement Note that for problems from 2011 thorugh 2019, the problems were initially typeset in html. For those problems you can find: * `<problem_name>.html`: The problem statement formatted in HTML For these problems, the Markdown version of the statement (`<problem_name>.md`) was automatically generated from the HTML version and may contain errors. For some problems, written solutions/analyses are available: * `<problem_name>.sol.md` ** For some problem, code solutions are available: * `<problem_name>.(cpp|py|java)` Some problems contains references to images that look like this: * `{{PHOTO_ID:<photo_id>}}`, example: `{{PHOTO_ID:923060468192530}}` In the same folder as the problem statement, you can find `<photo_id>.jpg` or `<photo_id>.gif` ## Starter Kits Some quick start solutions for working with this data are available at [HackerCupAI Repo](https://github.com/HackerCupAI/starter-kits). Some frameworks include: - [Langchain](https://www.langchain.com/) - [AutoGen](https://github.com/microsoft/autogen) The samples show basic steps for data ingest, generating solutions, and evaluation. For an example of data ingest, check out [this example](https://github.com/HackerCupAI/starter-kits/blob/main/autogen/app/utils/utils.py) ## Notes - Solutions prior to 2019 do not contain markdown solution files. - The 2019 markdown solutions are not included in the dataset but can be found in `.cpp` files. ## Citation If you use this dataset, please cite it as follows: ```bibtex @misc{2024hackercupai, title = {2024 Hacker Cup Dataset}, author = {May, Wesley and Harmeyer, David and Hoak, Amber and Li, Margaret and Dymchenko, Sergii and Yang, Weiwei and Saroufim, Mark}, } ```

longvideobench/LongVideoBench

longvideobench

--- license: cc-by-nc-sa-4.0 extra_gated_prompt: >- The LongVideoBench dataset contains links to web videos for data collection purposes. LongVideoBench does not own the content linked within this dataset; all rights and copyright belong to the respective channel owners. Ensuring compliance with platform terms and conditions is the responsibility of these source channels. By accessing this dataset, you acknowledge and agree to the following terms: extra_gated_fields: I understand that LongVideoBench does not own the videos in this dataset: checkbox I understand that LongVideoBench is not the creator of the videos in this dataset: checkbox I understand that, LongVideoBench may modify/delete its contents subject to the requirements of the creators or source platforms: checkbox I agree to use this dataset for non-commercial use ONLY: checkbox I agree with the data license (CC-BY-NC-SA 4-0) for this dataset: checkbox task_categories: - multiple-choice - visual-question-answering language: - en tags: - long video understanding - long context - multimodal - neurips 2024 pretty_name: longvideobench ---  # Dataset Card for LongVideoBench <!-- Provide a quick summary of the dataset. --> Large multimodal models (LMMs) are handling increasingly longer and more complex inputs. However, few public benchmarks are available to assess these advancements. To address this, we introduce LongVideoBench, a question-answering benchmark with video-language interleaved inputs up to an hour long. It comprises 3,763 web-collected videos with subtitles across diverse themes, designed to evaluate LMMs on long-term multimodal understanding. The main challenge that LongVideoBench targets is to accurately retrieve and reason over detailed information from lengthy inputs. We present a novel task called referring reasoning, where questions contain a referring query that references related video contexts, requiring the model to reason over these details. LongVideoBench includes 6,678 human-annotated multiple-choice questions across 17 categories, making it one of the most comprehensive benchmarks for long-form video understanding. Evaluations show significant challenges even for advanced proprietary models (e.g., GPT-4o, Gemini-1.5-Pro, GPT-4-Turbo), with open-source models performing worse. Performance improves only when models process more frames, establishing LongVideoBench as a valuable benchmark for future long-context LMMs. ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** LongVideoBench Team - **Language(s) (NLP):** English - **License:** CC-BY-NC-SA 4.0 ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [https://github.com/longvideobench/LongVideoBench](https://github.com/longvideobench/LongVideoBench) - **Homepage:** [https://longvideobench.github.io](https://longvideobench.github.io) - **Leaderboard:** [https://huggingface.co/spaces/longvideobench/LongVideoBench](https://huggingface.co/spaces/longvideobench/LongVideoBench) ## Leaderboard (until Oct. 14, 2024) We rank models by Test Total Performance. | Model | Test Total (5341) | Test 8s-15s | Test 15s-60s | Test 180s-600s | Test 900s-3600s | Val Total (1337) | | --- | --- | --- | --- | --- | --- | --- | | [GPT-4o (0513) (256)](https://platform.openai.com/docs/models/gpt-4o) | 66.7 | 71.6 | 76.8 | 66.7 | 61.6 | 66.7 | | [Aria (256)](https://huggingface.co/rhymes-ai/Aria) | 65.0 | 69.4 | 76.6 | 64.6 | 60.1 | 64.2 | | [LLaVA-Video-72B-Qwen2 (128)](https://huggingface.co/lmms-lab/LLaVA-Video-72B-Qwen2) | 64.9 | 72.4 | 77.4 | 63.9 | 59.3 | 63.9 | | [Gemini-1.5-Pro (0514) (256)](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemini-1.5-pro-001) | 64.4 | 70.2 | 75.3 | 65.0 | 59.1 | 64.0 | | [LLaVA-OneVision-QWen2-72B-OV (32)](https://huggingface.co/lmms-lab/llava-onevision-qwen2-72b-ov) | 63.2 | 74.3 | 77.4 | 61.6 | 56.5 | 61.3 | | [LLaVA-Video-7B-Qwen2 (128)](https://huggingface.co/lmms-lab/LLaVA-Video-7B-Qwen2) | 62.7 | 69.7 | 76.5 | 62.1 | 56.6 | 61.1 | | [Gemini-1.5-Flash (0514) (256)](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemini-1.5-flash-001) | 62.4 | 66.1 | 73.1 | 63.1 | 57.3 | 61.6 | | [GPT-4-Turbo (0409) (256)](https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4) | 60.7 | 66.4 | 71.1 | 61.7 | 54.5 | 59.1 | | [InternVL2-40B (16)](https://huggingface.co/OpenGVLab/InternVL2-40B) | 60.6 | 71.4 | 76.6 | 57.5 | 54.4 | 59.3 | | [GPT-4o-mini (250)](https://platform.openai.com/docs/models/gpt-4o-mini) | 58.8 | 66.6 | 73.4 | 56.9 | 53.4 | 56.5 | | [MiniCPM-V-2.6 (64)](https://huggingface.co/openbmb/MiniCPM-V-2_6) | 57.7 | 62.5 | 69.1 | 54.9 | 49.8 | 54.9 | | [Qwen2-VL-7B (256)](https://huggingface.co/openbmb/MiniCPM-V-2_6) | 56.8 | 60.1 | 67.6 | 56.7 | 52.5 | 55.6 | | [Kangaroo (64)](https://huggingface.co/KangarooGroup/kangaroo) | 54.8 | 65.6 | 65.7 | 52.7 | 49.1 | 54.2 | | [PLLaVA-34B (32)](https://github.com/magic-research/PLLaVA) | 53.5 | 60.1 | 66.8 | 50.8 | 49.1 | 53.2 | | [InternVL-Chat-V1-5-26B (16)](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5) | 51.7 | 61.3 | 62.7 | 49.5 | 46.6 | 51.2 | | [LLaVA-Next-Video-34B (32)](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/) | 50.5 | 57.6 | 61.6 | 48.7 | 45.9 | 50.5 | | [Phi-3-Vision-Instruct (16)](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) | 49.9 | 58.3 | 59.6 | 48.4 | 45.1 | 49.6 | | [Idefics2 (16)](https://huggingface.co/HuggingFaceM4/idefics2-8b) | 49.4 | 57.4 | 60.4 | 47.3 | 44.7 | 49.7 | | [Mantis-Idefics2 (16)](https://huggingface.co/TIGER-Lab/Mantis-8B-Idefics2) | 47.6 | 56.1 | 61.4 | 44.6 | 42.5 | 47.0 | | [LLaVA-Next-Mistral-7B (8)](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) | 47.1 | 53.4 | 57.2 | 46.9 | 42.1 | 49.1 | | [PLLaVA-13B (32)](https://github.com/magic-research/PLLaVA) | 45.1 | 52.9 | 54.3 | 42.9 | 41.2 | 45.6 | | [InstructBLIP-T5-XXL (8)](https://github.com/salesforce/LAVIS/tree/main/projects/instructblip) | 43.8 | 48.1 | 50.1 | 44.5 | 40.0 | 43.3 | | [Mantis-BakLLaVA (16)](https://huggingface.co/TIGER-Lab/Mantis-bakllava-7b) | 43.7 | 51.3 | 52.7 | 41.1 | 40.1 | 43.7 | | [BLIP-2-T5-XXL (8)](https://github.com/salesforce/LAVIS/tree/main/projects/blip2) | 43.5 | 46.7 | 47.4 | 44.2 | 40.9 | 42.7 | | [LLaVA-Next-Video-M7B (32)](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/) | 43.5 | 50.9 | 53.1 | 42.6 | 38.9 | 43.5 | | [LLaVA-1.5-13B (8)](https://huggingface.co/llava-hf/llava-1.5-13b-hf) | 43.1 | 49.0 | 51.1 | 41.8 | 39.6 | 43.4 | | [ShareGPT4Video (16)](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4Video) | 41.8 | 46.9 | 50.1 | 40.0 | 38.7 | 39.7 | | [VideoChat2 (Mistral-7B) (16)](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2) | 41.2 | 49.3 | 49.3 | 39.0 | 37.5 | 39.3 | | [LLaVA-1.5-7B (8)](https://huggingface.co/llava-hf/llava-1.5-7b-hf) | 40.4 | 45.0 | 47.4 | 40.1 | 37.0 | 40.3 | | [mPLUG-Owl2 (8)](https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2) | 39.4 | 49.4 | 47.3 | 38.7 | 34.3 | 39.1 | | [PLLaVA-7B (32)](https://github.com/magic-research/PLLaVA) | 39.2 | 45.3 | 47.3 | 38.5 | 35.2 | 40.2 | | [VideoLLaVA (8)](https://github.com/PKU-YuanGroup/Video-LLaVA/) | 37.6 | 43.1 | 44.6 | 36.4 | 34.4 | 39.1 | | [VideoChat2 (Vicuna 7B) (16)](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2) | 35.1 | 38.1 | 40.5 | 33.5 | 33.6 | 36.0 | ## Uses <!-- Address questions around how the dataset is intended to be used. --> 1. Download the dataset via Hugging Face Client: ```shell huggingface-cli download longvideobench/LongVideoBench --repo-type dataset --local-dir LongVideoBench --local-dir-use-symlinks False ``` 2. Extract from the `.tar` files: ```shell cat videos.tar.part.* > videos.tar tar -xvf videos.tar tar -xvf subtitles.tar ``` 3. Use the [LongVideoBench] dataloader to load the data from raw MP4 files and subtitles: - (a) Install the dataloader: ```shell git clone https://github.com/LongVideoBench/LongVideoBench.git cd LongVideoBench pip install -e . ``` - (b) Load the dataset in python scripts: ```python from longvideobench import LongVideoBenchDataset # validation dataset = LongVideoBenchDataset(YOUR_DATA_PATH, "lvb_val.json", max_num_frames=64) # test dataset = LongVideoBenchDataset(YOUR_DATA_PATH, "lvb_test_wo_gt.json", max_num_frames=64) print(dataset[0]["inputs"]) # A list consisting of PIL.Image and strings. ``` The "inputs" are interleaved video frames and text subtitles, followed by questions and option prompts. You can then convert them to the format that your LMMs can accept. ### Direct Use <!-- This section describes suitable use cases for the dataset. --> This dataset is meant to evaluate LMMs on video understanding and long-context understanding abilities. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> We do not advise to use this dataset for training. ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> - `lvb_val.json`: Validation set annotations. - `lvb_test_wo_gt.json`: Test set annotations. Correct choice is not provided. - `videos.tar.*`: Links to Videos. - `subtitles.tar`: Links to Subtitles. ## Dataset Card Contact [email protected] ``` @misc{wu2024longvideobenchbenchmarklongcontextinterleaved, title={LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding}, author={Haoning Wu and Dongxu Li and Bei Chen and Junnan Li}, year={2024}, eprint={2407.15754}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.15754}, } ```

code-search-net/code_search_net

code-search-net

--- annotations_creators: - no-annotation language_creators: - machine-generated language: - code license: - other multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: codesearchnet pretty_name: CodeSearchNet dataset_info: - config_name: all features: - name: repository_name dtype: string - name: func_path_in_repository dtype: string - name: func_name dtype: string - name: whole_func_string dtype: string - name: language dtype: string - name: func_code_string dtype: string - name: func_code_tokens sequence: string - name: func_documentation_string dtype: string - name: func_documentation_tokens sequence: string - name: split_name dtype: string - name: func_code_url dtype: string splits: - name: train num_bytes: 5850604083 num_examples: 1880853 - name: test num_bytes: 308626333 num_examples: 100529 - name: validation num_bytes: 274564382 num_examples: 89154 download_size: 5117370511 dataset_size: 6433794798 - config_name: java features: - name: repository_name dtype: string - name: func_path_in_repository dtype: string - name: func_name dtype: string - name: whole_func_string dtype: string - name: language dtype: string - name: func_code_string dtype: string - name: func_code_tokens sequence: string - name: func_documentation_string dtype: string - name: func_documentation_tokens sequence: string - name: split_name dtype: string - name: func_code_url dtype: string splits: - name: train num_bytes: 1429272535 num_examples: 454451 - name: test num_bytes: 82377246 num_examples: 26909 - name: validation num_bytes: 42358315 num_examples: 15328 download_size: 1060569153 dataset_size: 1554008096 - config_name: go features: - name: repository_name dtype: string - name: func_path_in_repository dtype: string - name: func_name dtype: string - name: whole_func_string dtype: string - name: language dtype: string - name: func_code_string dtype: string - name: func_code_tokens sequence: string - name: func_documentation_string dtype: string - name: func_documentation_tokens sequence: string - name: split_name dtype: string - name: func_code_url dtype: string splits: - name: train num_bytes: 738153234 num_examples: 317832 - name: test num_bytes: 32286998 num_examples: 14291 - name: validation num_bytes: 26888527 num_examples: 14242 download_size: 487525935 dataset_size: 797328759 - config_name: python features: - name: repository_name dtype: string - name: func_path_in_repository dtype: string - name: func_name dtype: string - name: whole_func_string dtype: string - name: language dtype: string - name: func_code_string dtype: string - name: func_code_tokens sequence: string - name: func_documentation_string dtype: string - name: func_documentation_tokens sequence: string - name: split_name dtype: string - name: func_code_url dtype: string splits: - name: train num_bytes: 1559645310 num_examples: 412178 - name: test num_bytes: 84342064 num_examples: 22176 - name: validation num_bytes: 92154786 num_examples: 23107 download_size: 940909997 dataset_size: 1736142160 - config_name: javascript features: - name: repository_name dtype: string - name: func_path_in_repository dtype: string - name: func_name dtype: string - name: whole_func_string dtype: string - name: language dtype: string - name: func_code_string dtype: string - name: func_code_tokens sequence: string - name: func_documentation_string dtype: string - name: func_documentation_tokens sequence: string - name: split_name dtype: string - name: func_code_url dtype: string splits: - name: train num_bytes: 480286523 num_examples: 123889 - name: test num_bytes: 24056972 num_examples: 6483 - name: validation num_bytes: 30168242 num_examples: 8253 download_size: 1664713350 dataset_size: 534511737 - config_name: ruby features: - name: repository_name dtype: string - name: func_path_in_repository dtype: string - name: func_name dtype: string - name: whole_func_string dtype: string - name: language dtype: string - name: func_code_string dtype: string - name: func_code_tokens sequence: string - name: func_documentation_string dtype: string - name: func_documentation_tokens sequence: string - name: split_name dtype: string - name: func_code_url dtype: string splits: - name: train num_bytes: 110681715 num_examples: 48791 - name: test num_bytes: 5359280 num_examples: 2279 - name: validation num_bytes: 4830744 num_examples: 2209 download_size: 111758028 dataset_size: 120871739 - config_name: php features: - name: repository_name dtype: string - name: func_path_in_repository dtype: string - name: func_name dtype: string - name: whole_func_string dtype: string - name: language dtype: string - name: func_code_string dtype: string - name: func_code_tokens sequence: string - name: func_documentation_string dtype: string - name: func_documentation_tokens sequence: string - name: split_name dtype: string - name: func_code_url dtype: string splits: - name: train num_bytes: 1532564870 num_examples: 523712 - name: test num_bytes: 80203877 num_examples: 28391 - name: validation num_bytes: 78163924 num_examples: 26015 download_size: 851894048 dataset_size: 1690932671 config_names: - all - go - java - javascript - php - python - ruby --- # Dataset Card for CodeSearchNet corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://wandb.ai/github/CodeSearchNet/benchmark - **Repository:** https://github.com/github/CodeSearchNet - **Paper:** https://arxiv.org/abs/1909.09436 - **Data:** https://doi.org/10.5281/zenodo.7908468 - **Leaderboard:** https://wandb.ai/github/CodeSearchNet/benchmark/leaderboard ### Dataset Summary CodeSearchNet corpus is a dataset of 2 milllion (comment, code) pairs from opensource libraries hosted on GitHub. It contains code and documentation for several programming languages. CodeSearchNet corpus was gathered to support the [CodeSearchNet challenge](https://wandb.ai/github/CodeSearchNet/benchmark), to explore the problem of code retrieval using natural language. ### Supported Tasks and Leaderboards - `language-modeling`: The dataset can be used to train a model for modelling programming languages, which consists in building language models for programming languages. ### Languages - Go **programming** language - Java **programming** language - Javascript **programming** language - PHP **programming** language - Python **programming** language - Ruby **programming** language ## Dataset Structure ### Data Instances A data point consists of a function code along with its documentation. Each data point also contains meta data on the function, such as the repository it was extracted from. ``` { 'id': '0', 'repository_name': 'organisation/repository', 'func_path_in_repository': 'src/path/to/file.py', 'func_name': 'func', 'whole_func_string': 'def func(args):\n"""Docstring"""\n [...]', 'language': 'python', 'func_code_string': '[...]', 'func_code_tokens': ['def', 'func', '(', 'args', ')', ...], 'func_documentation_string': 'Docstring', 'func_documentation_string_tokens': ['Docstring'], 'split_name': 'train', 'func_code_url': 'https://github.com/<org>/<repo>/blob/<hash>/src/path/to/file.py#L111-L150' } ``` ### Data Fields - `id`: Arbitrary number - `repository_name`: name of the GitHub repository - `func_path_in_repository`: tl;dr: path to the file which holds the function in the repository - `func_name`: name of the function in the file - `whole_func_string`: Code + documentation of the function - `language`: Programming language in whoch the function is written - `func_code_string`: Function code - `func_code_tokens`: Tokens yielded by Treesitter - `func_documentation_string`: Function documentation - `func_documentation_string_tokens`: Tokens yielded by Treesitter - `split_name`: Name of the split to which the example belongs (one of train, test or valid) - `func_code_url`: URL to the function code on Github ### Data Splits Three splits are available: - train - test - valid ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization All information can be retrieved in the [original technical review](https://arxiv.org/pdf/1909.09436.pdf) **Corpus collection**: Corpus has been collected from publicly available open-source non-fork GitHub repositories, using libraries.io to identify all projects which are used by at least one other project, and sort them by “popularity” as indicated by the number of stars and forks. Then, any projects that do not have a license or whose license does not explicitly permit the re-distribution of parts of the project were removed. Treesitter - GitHub's universal parser - has been used to then tokenize all Go, Java, JavaScript, Python, PHP and Ruby functions (or methods) using and, where available, their respective documentation text using a heuristic regular expression. **Corpus filtering**: Functions without documentation are removed from the corpus. This yields a set of pairs ($c_i$, $d_i$) where ci is some function documented by di. Pairs ($c_i$, $d_i$) are passed through the folllowing preprocessing tasks: - Documentation $d_i$ is truncated to the first full paragraph to remove in-depth discussion of function arguments and return values - Pairs in which $d_i$ is shorter than three tokens are removed - Functions $c_i$ whose implementation is shorter than three lines are removed - Functions whose name contains the substring “test” are removed - Constructors and standard extenion methods (eg `__str__` in Python or `toString` in Java) are removed - Duplicates and near duplicates functions are removed, in order to keep only one version of the function #### Who are the source language producers? OpenSource contributors produced the code and documentations. The dataset was gatherered and preprocessed automatically. ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Each example in the dataset has is extracted from a GitHub repository, and each repository has its own license. Example-wise license information is not (yet) included in this dataset: you will need to find out yourself which license the code is using. ### Citation Information @article{husain2019codesearchnet, title={{CodeSearchNet} challenge: Evaluating the state of semantic code search}, author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc}, journal={arXiv preprint arXiv:1909.09436}, year={2019} } ### Contributions Thanks to [@SBrandeis](https://github.com/SBrandeis) for adding this dataset.

microsoft/wiki_qa

microsoft

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - open-domain-qa paperswithcode_id: wikiqa pretty_name: WikiQA dataset_info: features: - name: question_id dtype: string - name: question dtype: string - name: document_title dtype: string - name: answer dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: test num_bytes: 1333261 num_examples: 6165 - name: validation num_bytes: 589765 num_examples: 2733 - name: train num_bytes: 4453862 num_examples: 20360 download_size: 2861208 dataset_size: 6376888 configs: - config_name: default data_files: - split: test path: data/test-* - split: validation path: data/validation-* - split: train path: data/train-* --- # Dataset Card for "wiki_qa" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://www.microsoft.com/en-us/download/details.aspx?id=52419](https://www.microsoft.com/en-us/download/details.aspx?id=52419) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [WikiQA: A Challenge Dataset for Open-Domain Question Answering](https://aclanthology.org/D15-1237/) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 7.10 MB - **Size of the generated dataset:** 6.40 MB - **Total amount of disk used:** 13.50 MB ### Dataset Summary Wiki Question Answering corpus from Microsoft. The WikiQA corpus is a publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 7.10 MB - **Size of the generated dataset:** 6.40 MB - **Total amount of disk used:** 13.50 MB An example of 'train' looks as follows. ``` { "answer": "Glacier caves are often called ice caves , but this term is properly used to describe bedrock caves that contain year-round ice.", "document_title": "Glacier cave", "label": 0, "question": "how are glacier caves formed?", "question_id": "Q1" } ``` ### Data Fields The data fields are the same among all splits. #### default - `question_id`: a `string` feature. - `question`: a `string` feature. - `document_title`: a `string` feature. - `answer`: a `string` feature. - `label`: a classification label, with possible values including `0` (0), `1` (1). ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|20360| 2733|6165| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information MICROSOFT RESEARCH DATA LICENSE AGREEMENT FOR MICROSOFT RESEARCH WIKIQA CORPUS These license terms are an agreement between Microsoft Corporation (or based on where you live, one of its affiliates) and you. Please read them. They apply to the data associated with this license above, which includes the media on which you received it, if any. The terms also apply to any Microsoft: - updates, - supplements, - Internet-based services, and - support services for this data, unless other terms accompany those items. If so, those terms apply. BY USING THE DATA, YOU ACCEPT THESE TERMS. IF YOU DO NOT ACCEPT THEM, DO NOT USE THE DATA. If you comply with these license terms, you have the rights below. 1. SCOPE OF LICENSE. a. You may use, copy, modify, create derivative works, and distribute the Dataset: i. for research and technology development purposes only. Examples of research and technology development uses are teaching, academic research, public demonstrations and experimentation ; and ii. to publish (or present papers or articles) on your results from using such Dataset. b. The data is licensed, not sold. This agreement only gives you some rights to use the data. Microsoft reserves all other rights. Unless applicable law gives you more rights despite this limitation, you may use the data only as expressly permitted in this agreement. In doing so, you must comply with any technical limitations in the data that only allow you to use it in certain ways. You may not - work around any technical limitations in the data; - reverse engineer, decompile or disassemble the data, except and only to the extent that applicable law expressly permits, despite this limitation; - rent, lease or lend the data; - transfer the data or this agreement to any third party; or - use the data directly in a commercial product without Microsoft’s permission. 2. DISTRIBUTION REQUIREMENTS: a. If you distribute the Dataset or any derivative works of the Dataset, you will distribute them under the same terms and conditions as in this Agreement, and you will not grant other rights to the Dataset or derivative works that are different from those provided by this Agreement. b. If you have created derivative works of the Dataset, and distribute such derivative works, you will cause the modified files to carry prominent notices so that recipients know that they are not receiving Page 1 of 3the original Dataset. Such notices must state: (i) that you have changed the Dataset; and (ii) the date of any changes. 3. DISTRIBUTION RESTRICTIONS. You may not: (a) alter any copyright, trademark or patent notice in the Dataset; (b) use Microsoft’s trademarks in a way that suggests your derivative works or modifications come from or are endorsed by Microsoft; (c) include the Dataset in malicious, deceptive or unlawful programs. 4. OWNERSHIP. Microsoft retains all right, title, and interest in and to any Dataset provided to you under this Agreement. You acquire no interest in the Dataset you may receive under the terms of this Agreement. 5. LICENSE TO MICROSOFT. 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Outside the United States. If you acquired the software in any other country, the laws of that country apply. 11. LEGAL EFFECT. This Agreement describes certain legal rights. You may have other rights under the laws of your country. You may also have rights with respect to the party from whom you acquired the Dataset. This Agreement does not change your rights under the laws of your country if the laws of your country do not permit it to do so. 12. DISCLAIMER OF WARRANTY. The Dataset is licensed “as-is.” You bear the risk of using it. Microsoft gives no express warranties, guarantees or conditions. You may have additional consumer rights or statutory guarantees under your local laws which this agreement cannot change. To the extent permitted under your local laws, Microsoft excludes the implied warranties of merchantability, fitness for a particular purpose and non- infringement. 13. LIMITATION ON AND EXCLUSION OF REMEDIES AND DAMAGES. YOU CAN RECOVER FROM MICROSOFT AND ITS SUPPLIERS ONLY DIRECT DAMAGES UP TO U.S. $5.00. YOU CANNOT RECOVER ANY OTHER DAMAGES, INCLUDING CONSEQUENTIAL, LOST PROFITS, SPECIAL, INDIRECT OR INCIDENTAL DAMAGES. This limitation applies to - anything related to the software, services, content (including code) on third party Internet sites, or third party programs; and Page 2 of 3 - claims for breach of contract, breach of warranty, guarantee or condition, strict liability, negligence, or other tort to the extent permitted by applicable law. It also applies even if Microsoft knew or should have known about the possibility of the damages. The above limitation or exclusion may not apply to you because your country may not allow the exclusion or limitation of incidental, consequential or other damages. ### Citation Information ``` @inproceedings{yang-etal-2015-wikiqa, title = "{W}iki{QA}: A Challenge Dataset for Open-Domain Question Answering", author = "Yang, Yi and Yih, Wen-tau and Meek, Christopher", booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2015", address = "Lisbon, Portugal", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/D15-1237", doi = "10.18653/v1/D15-1237", pages = "2013--2018", } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

Gustavosta/Stable-Diffusion-Prompts

Gustavosta

--- license: - unknown annotations_creators: - no-annotation language_creators: - found language: - en source_datasets: - original --- # Stable Diffusion Dataset This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: "[Lexica.art](https://lexica.art/)". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare. If you want to test the model with a demo, you can go to: "[spaces/Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/spaces/Gustavosta/MagicPrompt-Stable-Diffusion)". If you want to see the model, go to: "[Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion)".

jp1924/AudioCaps

jp1924

--- dataset_info: features: - name: audiocap_id dtype: int32 - name: youtube_id dtype: string - name: start_time dtype: int32 - name: audio dtype: audio: sampling_rate: 48000 - name: caption dtype: string splits: - name: train num_bytes: 2012866216147.6 num_examples: 45087 - name: validation num_bytes: 94570191869 num_examples: 2230 - name: test num_bytes: 187871958256.0 num_examples: 4400 download_size: 431887334157 dataset_size: 282442150125.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

WenhaoWang/VidProM

WenhaoWang

--- license: cc-by-nc-4.0 task_categories: - text-to-video - text-to-image language: - en pretty_name: VidProM size_categories: - 1M<n<10M source_datasets: - original tags: - prompts - text-to-video - text-to-image - Pika - VideoCraft2 - Text2Video-Zero - ModelScope - Video Generative Model Evaluation - Text-to-Video Diffusion Model Development - Text-to-Video Prompt Engineering - Efficient Video Generation - Fake Video Detection - Video Copy Detection for Diffusion Models configs: - config_name: VidProM_unique data_files: VidProM_unique.csv --- <p align="center"> <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/teasor.png" width="800"> </p> # Summary This is the dataset proposed in our paper [**VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models**](https://arxiv.org/abs/2403.06098) (NeurIPS 2024). VidProM is the first dataset featuring 1.67 million unique text-to-video prompts and 6.69 million videos generated from 4 different state-of-the-art diffusion models. It inspires many exciting new research areas, such as Text-to-Video Prompt Engineering, Efficient Video Generation, Fake Video Detection, and Video Copy Detection for Diffusion Models. # Directory ``` *DATA_PATH *VidProM_unique.csv *VidProM_semantic_unique.csv *VidProM_embed.hdf5 *original_files *generate_1_ori.html *generate_2_ori.html ... *pika_videos *pika_videos_1.tar *pika_videos_2.tar ... *vc2_videos *vc2_videos_1.tar *vc2_videos_2.tar ... *t2vz_videos *t2vz_videos_1.tar *t2vz_videos_2.tar ... *ms_videos *ms_videos_1.tar *ms_videos_2.tar ... *example ``` # Download ### Automatical Install the [datasets](https://huggingface.co/docs/datasets/v1.15.1/installation.html) library first, by: ``` pip install datasets ``` Then it can be downloaded automatically with ```python import numpy as np from datasets import load_dataset dataset = load_dataset('WenhaoWang/VidProM') ``` ### Manual You can also download each file by ```wget```, for instance: ``` wget https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/VidProM_unique.csv ``` ### Users from China For users from China, we cooperate with [Wisemodel](https://wisemodel.cn/home), and you can download them faster from [here](https://wisemodel.cn/datasets/WenhaoWang/VidProM). # Explanation ``VidProM_unique.csv`` contains the UUID, prompt, time, and 6 NSFW probabilities. It can easily be read by ```python import pandas df = pd.read_csv("VidProM_unique.csv") ``` Below are three rows from ``VidProM_unique.csv``: | uuid | prompt | time | toxicity | obscene | identity_attack | insult | threat | sexual_explicit | |--------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------|----------|---------|-----------------|---------|---------|-----------------| | 6a83eb92-faa0-572b-9e1f-67dec99b711d | Flying among clouds and stars, kitten Max discovered a world full of winged friends. Returning home, he shared his stories and everyone smiled as they imagined flying together in their dreams. | Sun Sep 3 12:27:44 2023 | 0.00129 | 0.00016 | 7e-05 | 0.00064 | 2e-05 | 2e-05 | | 3ba1adf3-5254-59fb-a13e-57e6aa161626 | Use a clean and modern font for the text "Relate Reality 101." Add a small, stylized heart icon or a thought bubble above or beside the text to represent emotions and thoughts. Consider using a color scheme that includes warm, inviting colors like deep reds, soft blues, or soothing purples to evoke feelings of connection and intrigue. | Wed Sep 13 18:15:30 2023 | 0.00038 | 0.00013 | 8e-05 | 0.00018 | 3e-05 | 3e-05 | | 62e5a2a0-4994-5c75-9976-2416420526f7 | zoomed out, sideview of an Grey Alien sitting at a computer desk | Tue Oct 24 20:24:21 2023 | 0.01777 | 0.00029 | 0.00336 | 0.00256 | 0.00017 | 5e-05 | ``VidProM_semantic_unique.csv`` is a semantically unique version of ``VidProM_unique.csv``. ``VidProM_embed.hdf5`` is the 3072-dim embeddings of our prompts. They are embedded by text-embedding-3-large, which is the latest text embedding model of OpenAI. It can easily be read by ```python import numpy as np import h5py def read_descriptors(filename): hh = h5py.File(filename, "r") descs = np.array(hh["embeddings"]) names = np.array(hh["uuid"][:], dtype=object).astype(str).tolist() return names, descs uuid, features = read_descriptors('VidProM_embed.hdf5') ``` ``original_files`` are the HTML files from [official Pika Discord](https://discord.com/invite/pika) collected by [DiscordChatExporter](https://github.com/Tyrrrz/DiscordChatExporter). You can do whatever you want with it under [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en). ``pika_videos``, ``vc2_videos``, ``t2vz_videos``, and ``ms_videos`` are the generated videos by 4 state-of-the-art text-to-video diffusion models. Each contains 30 tar files. ``example`` is a subfolder which contains 10,000 datapoints. # Datapoint <p align="center"> <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/datapoint.png" width="800"> </p> # Comparison with DiffusionDB <p align="center"> <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/compare_table.jpg" width="800"> </p> <p align="center"> <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/compare_visual.png" width="800"> </p> <p align="center"> <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/WizMap_V_D.jpg" width="800"> </p> Click the [WizMap](https://poloclub.github.io/wizmap/?dataURL=https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/data_vidprom_diffusiondb.ndjson&gridURL=https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/grid_vidprom_diffusiondb.json) (and wait for 5 seconds) for an interactive visualization of our 1.67 million prompts. Above is a thumbnail. Please check our paper for a detailed comparison. # Curators VidProM is created by [Wenhao Wang](https://wangwenhao0716.github.io/) and Professor [Yi Yang](https://scholar.google.com/citations?user=RMSuNFwAAAAJ&hl=zh-CN). # License The prompts and videos generated by [Pika](https://discord.com/invite/pika) in our VidProM are licensed under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en). Additionally, similar to their original repositories, the videos from [VideoCraft2](https://github.com/AILab-CVC/VideoCrafter), [Text2Video-Zero](https://github.com/Picsart-AI-Research/Text2Video-Zero), and [ModelScope](https://huggingface.co/ali-vilab/modelscope-damo-text-to-video-synthesis) are released under the [Apache license](https://www.apache.org/licenses/LICENSE-2.0), the [CreativeML Open RAIL-M license](https://github.com/Picsart-AI-Research/Text2Video-Zero/blob/main/LICENSE), and the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en), respectively. Our code is released under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en). # Citation ``` @article{wang2024vidprom, title={VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models}, author={Wang, Wenhao and Yang, Yi}, booktitle={Thirty-eighth Conference on Neural Information Processing Systems}, year={2024}, url={https://openreview.net/forum?id=pYNl76onJL} } ``` # Contact If you have any questions, feel free to contact Wenhao Wang ([email protected]).

m-a-p/PIN-100M

m-a-p

--- license: apache-2.0 language: - en - zh configs: - config_name: pin data_files: - split: train path: - data/DocLayNet/DocLayNet.jsonl tags: - multimodal - interleaved size_categories: - 100B<n<1T pretty_name: pin-100m --- # PIN-100M The full version of the dataset, related to the paper "PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents" Paper: https://arxiv.org/abs/2406.13923 This dataset contains 100M samples with PIN format. **Please note that the required storage space exceeds 150TB!!** 🚀 News [ 2024.12.20 ] !NEW! 🔥The currently available version is not the complete version; this project is still ongoing! (It has been released early because we reached the private storage limit on Hugging Face.) <img src="assets/intro.png"> ## 0 Usage Download ALL files ```bash huggingface-cli download m-a-p/PIN-100M --repo-type=dataset --resume-download --local-dir "your_local_path" ``` Download ONLY **Jsonl** files ```bash huggingface-cli download m-a-p/PIN-100M --repo-type=dataset --resume-download --include "*.jsonl" --local-dir "your_local_path" ``` Decompression ```bash cat data.tar.part* > data.tar tar -xvf data.tar ``` ## 1 Dataset statistics **Working** Storage space statistics may have some error, so these values are for reference only. ## 2 Data Structure ### 2.1 Subsets We process 8 subsets, including PIN-PMC, DocLayNet, Linux-CN, chinese-markdown, OBELICS, MMC4, leetcode, and PG19. <img src="assets/dataset-example.png"> Note: We do not release the PIN-arXiv subset in the preview version. ### 2.2 Folder Structure The directory `content images` holds the images mentioned within the markdown text, and `overall images` display the overall visual representation of the markdown files. Moreover, the `JSONL` file encapsulate the textual content along with associated data details. An example subset: ``` example_dataset/ │ ├── content_image/ ├── overall_image/ └── example_dataset.jsonl ``` A subset with multiple parts: ``` example_dataset/ │ ├── part00/ │ ├── content_image/ │ ├── overall_image/ │ └── part00.jsonl │ ├── part01/ │ ├── content_image/ │ ├── overall_image/ │ └── part01.jsonl │ ... - More similar parts ``` ### 2.3 content_image Folder This folder contains all the content images used in the markdown files. Note: All images need to be converted to PNG format. The filename should be unique within the folder. ``` content_image/ │ ├── 1.png ├── 2.png ... ``` ### 2.4 overall_image Folder This folder contains all the overall images for each sample. Note: All images need to be converted to PNG format. The filename should be unique within the folder. ``` overall_image/ │ ├── 1.png ├── 2.png ... ``` #### 2.5 JSON Lines Format we provide a detailed example of the annotations included with each data entry. ``` { "id": 1919, "meta": { "language": "en", "oi_exist": true, "oi_source": "compiling", "source_dataset": "example_source (e.g. OBELICS)", "ori_meta": { "document_url": "https://www.example.com/2022/02/21/example/", ... } }, "doc_id": 1997, "page_id": 0, "date_download": "2024-03-01" }, "license": "CC-BY-4.0", "quality_signals": { "doc_length": 100, ... }, "content_image": [ "content_image/1997-0.png", "content_image/1997-1.png" ], "md": "<img src='content_image/1997-0.png'>\n\nThis is a fake sample data line, just for show.\n\nThis is a fake sample data line, just for show.\n\n<img src='content_image/1997-1.png'>\n\nThis is a fake sample data line, just for show.", "overall_image": "overall_image/1997.png" } ``` Field Descriptions: **Field Descriptions:** - **id**: Unique identifier for each entry. - **meta**: Metadata for each multimodal document entry. - **language**: The document's language, such as Chinese (zh) or English (en). - **source_dataset**: If the document is converted from another dataset, the original dataset name is noted here; otherwise, it is None. - **doc_id**: A unique document identifier providing name and other details. - **page_id**: A unique page identifier indicating the document's page number. If there is only one page, this is None. Page IDs are usually numbered starting from 1 in multi-page documents. - **date_download**: date (download), the date the document was downloaded. - **ori_meta**: Original metadata from the dataset, if available; otherwise, None. - **oi_exist**: Indicates whether an overall image exists. True or False. - **oi_source**: Source of the overall image; 'ori' for images taken from the original dataset and 'compiling' for images generated through code compilation. If this tag is missing, the image is likely compiled. - ... - **quality_signals**: Quality indicators inspired by the design of redpajama v2. - **doc_length**: Length of the document. - ... - **content_image**: List of images mentioned in the document; None if no images are present. - **overall_image**: Path to the corresponding overall image. (A list or a single path) - **md**: Contains the markdown content. - **license**: License information for the current sample. ## 3 Examples of jsonl files We selected samples consisting of short markdown documents. ### 3.1 An example of DocLynet Notably, the dataset's overall images are converted from the original dataset's PDFs into PNG format. ```json { "id": 0, "meta": { "language": "en", "oi_exist": true, "oi_source": "ori", "source_dataset": "DocLayNet", "ori_meta": null, "doc_id": "NYSE_F_2004.pdf", "page_id": "0", "date_download": "2024-3-24" }, "quality_signals": null, "license": "https://cdla.io/permissive-1-0/", "content_image": [ "content_image/34102.jpg" ], "overall_image": "overall_image/3562e47265520f7a72f3eac73aadfe19a78531698c3b50d7670b8ad9b214106b.png", "md": "<img src='content_image/34102.jpg'>\n\n# Ford Motor Company / 2004 Annual Report \n\n# R W A R D F O R W A R D \n\n" } ``` ### 3.2 An example of OBELICS ```json { "id": 466502, "meta": { "language": "en", "oi_exist": true, "oi_source": "compiling", "source_dataset": "OBELICS", "ori_meta": { "document_url": "https://www.donegaldaily.com/2022/02/21/watch-incredible-storm-surge-at-portsalon-golf-club/", "unformatted_src": "https://www.donegaldaily.com/wp-content/uploads/2022/02/Screenshot-2022-02-21-at-17.54.30.jpg", "src": "https://www.donegaldaily.com/wp-content/uploads/2022/02/Screenshot-2022-02-21-at-17.54.30.jpg", "formatted_filename": "Screenshot at", "rendered_width": 817, "rendered_height": 419, "original_width": 817, "original_height": 419, "format": "jpeg", "general_meta": { "url": "https://www.donegaldaily.com/2022/02/21/watch-incredible-storm-surge-at-portsalon-golf-club/", "warc_filename": "crawl-data/CC-MAIN-2022-27/segments/1656103271864.14/warc/CC-MAIN-20220626192142-20220626222142-00308.warc.gz", "warc_record_offset": 795020636, "warc_record_length": 31271 } }, "doc_id": 98496, "page_id": 0, "date_download": "2024-4-22" }, "md": "<img src='content_image/98496-0.png'>\n\nThe golf course at Portsalon Golf Club took a battering today as a result of Storm Franklin.\n\nDonegal had been left battered and bruised overnight after Storm Franklin ripped across the county.\n\nThere were trees down on the approach roads to Donegal Town and in Gartan.\n\nThere were also trees down in Inishowen while there is also heavy water reported along the sides of roads with motorists asked to slow down and not put themselves in danger.\n\nDonegal’s coastline took a huge impact with massive waves reported along the coastline around the county.\n\nThe video, taken by Johnny Shields was taken from the tee box of the third hole.", "license": "CC-BY-4.0", "quality_signals": null, "content_image": [ "content_image/98496-0.png" ], "overall_image": "overall_image/98496-0.png" } ``` ### 3.3 An example of chinese-markdown ```json { "id": 7, "meta": { "language": "zh", "oi_exist": true, "oi_source": "compiling", "source_dataset": "chinese-markdown", "ori_meta": null, "doc_id": 7, "page_id": null, "date_download": "2024-04-30" }, "md": "---\ntitle: 常见问题 QA\ncategory: 其它\norder: 1\n---\n\n> 持续更新中...\n> 如有问题可以到 <https://github.com/alibaba/ice/issues/new> 反馈\n\n## ICE 的浏览器兼容策略是什么\n\n由于 ICE 优先使用 React 16+，其需要的最低 IE 版本为 11，如果您需要在以下的版本使用，您可能需要引入一些 polyfill 来支持 `Map`, `Set` 等特性。参考[React 官网说明](https://reactjs.org/blog/2017/09/26/react-v16.0.html#javascript-environment-requirements)。\n\n以下代码可以帮助你在低版本 IE 下自动跳转到我们提供的提示浏览器升级页面。当然您也可以使用自定义的浏览器升级页面。\n\n```\n<!--[if lt IE 11]>\n<script>location.href = \"//www.taobao.com/markets/tbhome/ali-page-updater\"; </script>\n<![endif]-->\n```\n\n添加如上代码后，如果使用 IE11 及以下浏览器访问页面，则会自动跳转到统一引导升级浏览器的页面。\n\n## WebStorm/IDEA 编辑器卡顿现象\n\n由于项目在安装依赖后，产生文件夹 `node_modules` 含有较多的碎小文件，编辑器在索引文件引起的卡顿。\nWebStorm 中尤为明显，可通过 exclude `node_modules` 目录，不需要检索该文件夹下的内容。\n\n## 如何设置网页在浏览器 Tab 上面的 Icon (favicon)\n\n细心的同学可能会看到页面在浏览器 Tab 上面会有自定义的 Icon：\n\n\n\n如果你想要在自己站点上面加上这个 Icon 可以按照如下步骤添加：\n\n1. 准备一个 Icon，文件格式可以为 `.png` 或者 `.ico`，正方形，分辨率可以是 32x32px 或者 64x64px 文件体积要求尽可能小。\n2. 上传 CDN 拿到一个 url 或者在自己服务器配置静态资源服务\n3. 在 HTML 页面 `<head>` 标签里面添加如下代码：`<link rel=\"shortcut icon\" href=\"your-icon-url\">`\n \n\n这样就添加成功啦！\n\n## 如何在页面显示原始的 HTML 内容\n\n出于安全方面的考虑，React 默认会将节点中 html 代码进行转义，比如：\n\n```jsx\nclass Demo extends Component {\n render() {\n const content = 'hello <span>world</span>';\n return <div>{content}</div>;\n }\n}\n\n// 输出 hello <span>world</span>\n```\n\n如上，`<span>` 标签并不会在页面上被解析，而是被当成字符串输出了。React 提供了 `dangerouslySetInnerHTML` 属性帮助我们进行类似 `innerHTML` 的操作：\n\n```jsx\nclass Demo extends Component {\n render() {\n const content = 'hello <span>world</span>';\n return <div dangerouslySetInnerHTML={{ __html: content }} />;\n }\n}\n\n// 输出 hello world\n```\n\n更多内容请参考 [Dangerously Set innerHTML](https://reactjs.org/docs/dom-elements.html#dangerouslysetinnerhtml)\n\n## 之前创建的项目，遇到如下报错怎么办\n\n\n\n这是由于 ES6 Modules 的标准在物料中不兼容导致的。您可以把 `src/navs.js` 中最后一行修改为：\n\n```js\nexport const headerNavs = transform([\n ...autoGenHeaderNavs,\n ...customHeaderNavs,\n]);\n\nexport const asideNavs = transform([...autoGenAsideNavs, ...customAsideNavs]);\n```", "license": "MIT", "quality_signals": null, "content_image": [ "content_image/7-0.png" ], "overall_image": "overall_image/7.png" } ``` ### 3.4 An example of leetcode ```json { "id": 1, "meta": { "language": "en", "doc_id": 1, "page_id": null, "oi_exist": true, "oi_source": "compiling", "source_dataset": "leetcode", "date_download": "2024-05-05", "ori_meta": { "slug": "two-sum", "difficulty": "Easy" } }, "quality_signals": null, "license": "MIT", "content_image": null, "md": "# Two Sum\n\n- slug: two-sum\n- difficulty: Easy\n\nGiven an array of integers `nums` and an integer `target`, return _indices of the two numbers such that they add up to `target`_.\n\nYou may assume that each input would have **_exactly_ one solution**, and you may not use the _same_ element twice.\n\nYou can return the answer in any order.\n\n**Example 1:**\n\n**Input:** nums = \\[2,7,11,15\\], target = 9\n**Output:** \\[0,1\\]\n**Explanation:** Because nums\\[0\\] + nums\\[1\\] == 9, we return \\[0, 1\\].\n\n**Example 2:**\n\n**Input:** nums = \\[3,2,4\\], target = 6\n**Output:** \\[1,2\\]\n\n**Example 3:**\n\n**Input:** nums = \\[3,3\\], target = 6\n**Output:** \\[0,1\\]\n\n**Constraints:**\n\n* `2 <= nums.length <= 104`\n* `-109 <= nums[i] <= 109`\n* `-109 <= target <= 109`\n* **Only one valid answer exists.**\n\n**Follow-up:** Can you come up with an algorithm that is less than `O(n2)` time complexity?\n\n## A solution in Java\n\n```java\nimport java.util.HashMap;\nimport java.util.Map;\n\npublic int[] twoSum(int[] nums, int target) {\n Map<Integer, Integer> map = new HashMap<>();\n for (int i = 0; i < nums.length; i++) {\n int complement = target - nums[i];\n if (map.containsKey(complement)) {\n return new int[]{map.get(complement), i};\n }\n map.put(nums[i], i);\n }\n throw new IllegalArgumentException(\"No two sum solution\");\n}\n```\nThe algorithm leverages a hash map (unordered_map in C++, HashMap in Java, dictionary in Python, and Map in JavaScript). It iterates through the given 'nums' array and calculates the complementary value (target - current value). If the complementary value is already in the hash map, it means that we found a solution, and we return those indices. If the complement is not in the hash map, we store the current element in the hash map with its index. If the algorithm doesn't find the solution, it returns an empty array or throws an exception (in Java).\n\nThis approach has a time complexity of O(n) and a space complexity of O(n) as well.\n \n\n## A solution in C++\n\n```cpp\n#include <vector>\n#include <unordered_map>\n\nstd::vector<int> twoSum(std::vector<int>& nums, int target) {\n std::unordered_map<int, int> map;\n for (int i = 0; i < nums.size(); i++) {\n int complement = target - nums[i];\n if (map.find(complement) != map.end()) {\n return {map[complement], i};\n }\n map[nums[i]] = i;\n }\n return {};\n}\n```\nThe algorithm leverages a hash map (unordered_map in C++, HashMap in Java, dictionary in Python, and Map in JavaScript). It iterates through the given 'nums' array and calculates the complementary value (target - current value). If the complementary value is already in the hash map, it means that we found a solution, and we return those indices. If the complement is not in the hash map, we store the current element in the hash map with its index. If the algorithm doesn't find the solution, it returns an empty array or throws an exception (in Java).\n\nThis approach has a time complexity of O(n) and a space complexity of O(n) as well.\n \n\n## A solution in Python\n\n```python\ndef twoSum(nums, target):\n map = {}\n for i, num in enumerate(nums):\n complement = target - num\n if complement in map:\n return [map[complement], i]\n map[num] = i\n return []\n```\nThe algorithm leverages a hash map (unordered_map in C++, HashMap in Java, dictionary in Python, and Map in JavaScript). It iterates through the given 'nums' array and calculates the complementary value (target - current value). If the complementary value is already in the hash map, it means that we found a solution, and we return those indices. If the complement is not in the hash map, we store the current element in the hash map with its index. If the algorithm doesn't find the solution, it returns an empty array or throws an exception (in Java).\n\nThis approach has a time complexity of O(n) and a space complexity of O(n) as well.\n \n\n## A solution in Javascript\n\n```javascript\nfunction twoSum(nums, target) {\n const map = new Map();\n for (let i = 0; i < nums.length; i++) {\n const complement = target - nums[i];\n if (map.has(complement)) {\n return [map.get(complement), i];\n }\n map.set(nums[i], i);\n }\n return [];\n}\n```\nThe algorithm leverages a hash map (unordered_map in C++, HashMap in Java, dictionary in Python, and Map in JavaScript). It iterates through the given 'nums' array and calculates the complementary value (target - current value). If the complementary value is already in the hash map, it means that we found a solution, and we return those indices. If the complement is not in the hash map, we store the current element in the hash map with its index. If the algorithm doesn't find the solution, it returns an empty array or throws an exception (in Java).\n\nThis approach has a time complexity of O(n) and a space complexity of O(n) as well.\n \n", "overall_image": "overall_image/1.png" } ``` ### 3.5 An example of linux-cn ```json { "id": 8, "meta": { "language": "zh", "doc_id": 134, "page_id": null, "oi_exist": true, "oi_source": "compiling", "source_dataset": "linux-cn", "date_download": "2024-05-06", "ori_meta": { "title": "Ubuntu 11.04正式发布！", "author": "", "fromurl": "", "summary": "刚才接到的消息，Ubuntu 11.04已经正式发布！\r\n\r\n超快！易用！免费！\r\nUbuntu操作系统为世界上数以百万计的电脑、上网本和服务器提供了动力！\r\nUbuntu可以为你完成各种工作，管理你的文件、打印机、摄像头和MP3！并且它 ...", "pic": "/data/attachment/album/201104/28/193933lnqqwwwn8l64wbn1.jpg.thumb.jpg", "largepic": "/data/attachment/album/201104/28/193933lnqqwwwn8l64wbn1.jpg", "titlepic": false, "thumb": false, "islctt": false, "selector": "", "translator": "", "reviewer": "", "editorchoice": false, "tags": [ "Ubuntu 11.04", "发布" ], "category": "新闻", "count": { "commentnum": 0, "favtimes": 0, "likes": 0, "sharetimes": 1, "viewnum": 6165 }, "comments_data": [ ], "related": [ ], "excerpt": "刚才接到的消息，Ubuntu 11.04已经正式发布！\r\n\r\n超快！易用！免费！\r\nUbuntu操作系统为世界上数以百万计的电脑、上网本和服务器提供了动力！\r\nUbuntu可以为你完成各种工作，管理你的文件、打印机、摄像头和MP3！并且它 ...", "date": "2011-05-09 13:24:00", "updated": "2011-05-09 13:24:00", "id": 134, "permalink": "/article-134-1.html" } }, "quality_signals": null, "license": "CC-BY-NC-4.0", "content_image": [ "content_image/album_201104_28_193933lnqqwwwn8l64wbn1.jpg", "content_image/album_201104_28_193935sy4l3bh4bh1ycbbc.jpg", "content_image/album_201104_28_193936lyvc36fwv91l1359.jpg", "content_image/album_201104_28_19393800rpr8pf0s8p8w0s.jpg" ], "md": "# Ubuntu 11.04正式发布！\n\n刚才接到的消息，Ubuntu 11.04已经正式发布！ \n \n 超快！易用！免费！ \n Ubuntu操作系统为世界上数以百万计的电脑、上网本和服务器提供了动力！ \n Ubuntu可以为你完成各种工作，管理你的文件、打印机、摄像头和MP3！并且它还带有数千个免费程序。 \n \n <img src=\"content_image/album_201104_28_193933lnqqwwwn8l64wbn1.jpg\" alt=\"\" title=\"\"> \n **数千个免费程序** \n \n <img src=\"content_image/album_201104_28_193935sy4l3bh4bh1ycbbc.jpg\" alt=\"\" title=\"\"> \n **终生免费升级** \n \n <img src=\"content_image/album_201104_28_193936lyvc36fwv91l1359.jpg\" alt=\"\" title=\"\"> \n **内建的病毒防护** \n \n <img src=\"content_image/album_201104_28_19393800rpr8pf0s8p8w0s.jpg\" alt=\"\" title=\"\"> \n **云中的音乐** \n \n 下载地址：\n\n\n\n\n> 列表： \n> <http://releases.ubuntu.com/11.04/> \n> 桌面版： \n> <http://www.ubuntu.com/download/ubuntu/download> \n> 服务器版： \n> <http://www.ubuntu.com/download/server/download>\n\n\n\n \n BT种子地址：\n\n\n\n\n> \n> * [ubuntu-11.04-alternate-amd64.iso.torrent](http://releases.ubuntu.com/11.04/ubuntu-11.04-alternate-amd64.iso.torrent)\n> * [ubuntu-11.04-alternate-i386.iso.torrent](http://releases.ubuntu.com/11.04/ubuntu-11.04-alternate-i386.iso.torrent)\n> * [ubuntu-11.04-desktop-amd64.iso.torrent](http://releases.ubuntu.com/11.04/ubuntu-11.04-desktop-amd64.iso.torrent)\n> * [ubuntu-11.04-desktop-i386.iso.torrent](http://releases.ubuntu.com/11.04/ubuntu-11.04-desktop-i386.iso.torrent)\n> * [ubuntu-11.04-netbook-i386.iso.torrent](http://releases.ubuntu.com/11.04/ubuntu-11.04-netbook-i386.iso.torrent)\n> * [ubuntu-11.04-server-amd64.iso.torrent](http://releases.ubuntu.com/11.04/ubuntu-11.04-server-amd64.iso.torrent)\n> * [ubuntu-11.04-server-i386.iso.torrent](http://releases.ubuntu.com/11.04/ubuntu-11.04-server-i386.iso.torrent)\n> \n> \n> \n\n\n\n \n 当前尚无DVD版本出现 \n \n \n \n 该贴已经同步到 [wxy的微博](http://api.t.sina.com.cn/1747813575/statuses/9786340397) \n \n \n \n\n\n \n\n\n*[本文内容由 wxy 提供](thread-7135-1-1.html)*\n \n\n\n\n 已同步至 [wxy的微博](http://api.t.sina.com.cn/1747813575/statuses/10347235925)", "overall_image": "overall_image/134.png" } ``` ### 3.6 An example of mmc-core-ff ```json { "meta": { "language": "en", "oi_exist": true, "oi_source": "compiling", "doc_id": 11, "page_id": 0, "source_dataset": "mmc4-core-ff", "source_jsonl": "mmc4-core-ff/docs_no_face_shard_10375_v3.jsonl", "ori_meta": { "url": "http://position-light.blogspot.com/2015/06/whats-up-with-reading-and-northern.html", "text_list": [ "The Position Light: What's Up with the Reading and Northern?", "The Reading and Northern has been a rare bright spot in the world of signaling.", "A commitment to its Reading heritage has resulted in numerous signaling structures being preserved along with attempts to install \"classic\" signaling where new signaling is being installed on its mostly unsignaled territory.", "The R&N also controls the former Conrail Lehigh Line and for one reason or another has decided not to touch the surviving LVRR signaling along that route.", "Still, I am still not completely clear on the full extent of the R&N's signal preservation efforts as hinted at in a number of photos I have come across.", "We begin near the town of Mach Chunk where the R&N runs a tourist operation in the Lehigh Gorge.", "i have bicycles along the right of way a number of time and I never noticed this cantilever mast and its freshly painted (albeit turned) signals.", "Is this a sign of a new interlocking or signaling project?", "Pottsville is the location of some preserved Reading signal bridges and a tower.", "Both have been out of service for decades, but then I find a photo showing what appears to be a lit Reading US&S three headed signal displaying a restricting indication.", "Could be that the photographer is having some fun with Photoshoppe, or it could be another R&N instance of an \"island\" interlocking designed to eliminate the need for crews to hand throw switches.", "Clearly I need to take another field trip to the area, but if anyone has any information (or photos) please let me know.", "Yes, that dual Signal Cantilever was taken from Schuylkill Haven and refurbished and placed into service as part of the new CP COAL Interlocking aptly named for the nearby town of Coalport.", "This new interlocking controls R&N connector feed track and switch from Nesquehoning Jct onto the NS Lehigh Line.", "Be aware, that R&N is constructing a new Y connector bridge over the Lehigh River.", "The switch at Nesquehoning Jct as well at the Y connecting point northwest along the old CNJ into Nesquehoning and the other apex connecting point at the old Lehigh Valley overpass will make up the new Y along with the new bridge.", "Expect the R&N to make all 3 points new CP Interlockings as NS will also use the new route to get to Reading & Philadelphia directly off the Lehigh Line.", "Coming attractions for 2016.", "Also, R&N is talking about a new signaled controlled passing track siding midway between Port Clinton and Reading.", "Believe they will leverage the siding that's already in place (don't know name of that area, but, between two grade crossings).", "Could see even more new R&N signaling if Distants are added to the mix as well.", "Thank you for the information!", "I knew something was up with them.", "Mike - Have updates with pics for R&N.", "Can share them with you but not sure of best way via e-mail or blog address.", "Can you provide and I can forward what I have?", "You can drop a line to [email protected] Thanks!" ], "image_info": [ { "face_detections": null, "image_id": "11-0.png", "image_name": "338146395110.jpg", "matched_sim": 0.2532651722, "matched_text_index": 12, "raw_url": "http://www.railpictures.net/images/d2/6/0/1/6601.1425352225.jpg" }, { "face_detections": null, "image_id": "11-1.png", "image_name": "75dca5908f72.jpg", "matched_sim": 0.2665729225, "matched_text_index": 18, "raw_url": "http://www.railpictures.net/images/d2/0/3/5/5035.1411414707.jpg" } ], "similarity_matrix": [ [ 0.2208167017, 0.2216126323, 0.2174896896, 0.2322429568, 0.1835552454, 0.1933521628, 0.1114124805, 0.1734878719, 0.1712893993, 0.1681747884, 0.2151062787, 0.1558438838, 0.2532651722, 0.2029514462, 0.1683746874, 0.1972030103, 0.2269551754, 0.1497862041, 0.2076308429, 0.1459720433, 0.1406365782, 0.1131924018, 0.0637710392, 0.1748069972, 0.1665924788, 0.1288469583, 0.1271829307 ], [ 0.2275835425, 0.2447894663, 0.2326766551, 0.2530837059, 0.197981596, 0.1727618128, 0.1842465401, 0.2053450346, 0.2174785137, 0.2176187485, 0.216365099, 0.152155906, 0.2394197732, 0.2332755029, 0.2077463269, 0.2373518944, 0.2454088479, 0.1549753994, 0.2665729225, 0.2099550366, 0.163154155, 0.1208794788, 0.0917887241, 0.1707040668, 0.1544941813, 0.1439596266, 0.1319040358 ] ], "could_have_url_duplicate": 0 }, "date_download": "2024-05-11" }, "md": "The Position Light: What's Up with the Reading and Northern? The Reading and Northern has been a rare bright spot in the world of signaling. A commitment to its Reading heritage has resulted in numerous signaling structures being preserved along with attempts to install \"classic\" signaling where new signaling is being installed on its mostly unsignaled territory. The R&N also controls the former Conrail Lehigh Line and for one reason or another has decided not to touch the surviving LVRR signaling along that route. Still, I am still not completely clear on the full extent of the R&N's signal preservation efforts as hinted at in a number of photos I have come across. We begin near the town of Mach Chunk where the R&N runs a tourist operation in the Lehigh Gorge. i have bicycles along the right of way a number of time and I never noticed this cantilever mast and its freshly painted (albeit turned) signals. Is this a sign of a new interlocking or signaling project? Pottsville is the location of some preserved Reading signal bridges and a tower. Both have been out of service for decades, but then I find a photo showing what appears to be a lit Reading US&S three headed signal displaying a restricting indication. Could be that the photographer is having some fun with Photoshoppe, or it could be another R&N instance of an \"island\" interlocking designed to eliminate the need for crews to hand throw switches. Clearly I need to take another field trip to the area, but if anyone has any information (or photos) please let me know. Yes, that dual Signal Cantilever was taken from Schuylkill Haven and refurbished and placed into service as part of the new CP COAL Interlocking aptly named for the nearby town of Coalport.\n\n\n\n<img src='content_image/11-0.png'>\n\nThis new interlocking controls R&N connector feed track and switch from Nesquehoning Jct onto the NS Lehigh Line. Be aware, that R&N is constructing a new Y connector bridge over the Lehigh River. The switch at Nesquehoning Jct as well at the Y connecting point northwest along the old CNJ into Nesquehoning and the other apex connecting point at the old Lehigh Valley overpass will make up the new Y along with the new bridge. Expect the R&N to make all 3 points new CP Interlockings as NS will also use the new route to get to Reading & Philadelphia directly off the Lehigh Line. Coming attractions for 2016. Also, R&N is talking about a new signaled controlled passing track siding midway between Port Clinton and Reading.\n\n\n\n<img src='content_image/11-1.png'>\n\nBelieve they will leverage the siding that's already in place (don't know name of that area, but, between two grade crossings). Could see even more new R&N signaling if Distants are added to the mix as well. Thank you for the information! I knew something was up with them. Mike - Have updates with pics for R&N. Can share them wi", "license": "ODC-BY", "quality_signals": null, "content_image": [ "content_image/11-0.png", "content_image/11-1.png" ], "overall_image": "overall_image/11-0.png" } ``` ### 3.7 An example of PG19 ```json { "meta": { "language": "en", "oi_exist": true, "oi_source": "compiling", "doc_id": 871, "page_id": 0, "source_dataset": "pg19", "split": "train", "ori_meta": { "url": "http://www.gutenberg.org/ebooks/9304", "short_book_title": "Initiation into Philosophy by Emile Faguet", "publication_date": 1914 }, "date_download": "2024-05-10" }, "md": "# Initiation into Philosophy by Emile Faguet \n\n Produced by Ted Garvin, Thomas Hutchinson and PG Distributed Proofreaders \n\n \n\n \n\n \n\n \n\n INITIATION INTO PHILOSOPHY \n\n \nBy Emile Faguet \n\n Of the French Academy \n\n \nAuthor of \"The Cult Of Incompetence,\" \"Initiation Into Literature,\" etc. \n\n \nTranslated from the French by Sir Homer Gordon, Bart. \n\n 1914 \n\n \n\n \nPREFACE \n\n This volume, as indicated by the title, is designed to show the way to the beginner, to satisfy and more espec ially to excite his initial curiosity. It affords an adequate idea of the march of facts and of ideas. The rea der is led, somewhat rapidly, from the remote origins to the most recent efforts of the human mind. \n\n It should be a convenient repertory to which the mind may revert in order to see broadly the general opinion o f an epoch--and what connected it with those that followed or preceded it. It aims above all at being _a frame _ in which can conveniently be inscribed, in the course of further studies, new conceptions more detailed and more thoroughly examined. \n\n It will have fulfilled its design should it incite to research and meditation, and if it prepares for them cor rectly. \n\n E. FAGUET. \n\n \n\n \nCONTENTS \n\n \nPART I ANTIQUITY \n\n \nCHAPTER I BEFORE SOCRATES \n\n Philosophical Interpreters of the Universe, of the Creation and Constitution of the World. \n\n \nCHAPTER II THE SOPHISTS \n\n Logicians and Professors of Logic, and of the Analysis of Ideas, and of Discussion. \n\n \nCHAPTER III SOCRATES \n\n Philosophy Entirely Reduced to Morality, and Morality Considered as the End of all Intellectual Activity. \n\n \nCHAPTER IV PLATO \n\n Plato, like Socrates, is Pre-eminently a Moralist, but he Reverts to General Consideration of the Universe, an d Deals with Politics and Legislation. \n\n \nCHAPTER V ARISTOTLE", "license": "Apache 2.0", "quality_signals": null, "content_image": null, "overall_image": "overall_image/871-0.png" } ``` ### 3.8 An example of PIN-PMC ```json { "meta": { "language": "en", "doc_id": "PMC3015258", "oi_exist": true, "oi_source": "ori", "source_dataset": "PIN-PMC", "ori_meta": null, "page_id": null, "date_download": "2024-05-28" }, "md": "# A Simple Stereoscopic Endoscope\n\n## Abstract\n\nA very simple method is described for producing and viewing stereoscopic endoscopic images.\nThe addition of two simple prisms to the end of a conventional television-monitored endoscope with a simple viewing device produces a stereoscopic endoscope which appears to be suitable for surgical use......", "license": [ "https://www.ncbi.nlm.nih.gov/pmc/tools/textmining/" ], "quality_signals": { "doc_length": 8269 }, "content_image": [ "content_image/PMC3015258/jsls-2-1-67-g03.jpg", "content_image/PMC3015258/jsls-2-1-67-g04.jpg", "content_image/PMC3015258/jsls-2-1-67-g01.jpg", "content_image/PMC3015258/jsls-2-1-67-g02.jpg", "content_image/PMC3015258/jsls-2-1-67-g05.jpg" ], "overall_image": [ "overall_image/PMC3015258/jsls-2-1-67_3.png", "overall_image/PMC3015258/jsls-2-1-67_0.png", "overall_image/PMC3015258/jsls-2-1-67_1.png", "overall_image/PMC3015258/jsls-2-1-67_2.png" ], "id": 60827 } ``` ## 4 License For data generated or produced by us, please adhere to the Apache 2.0 License. For data sourced from third parties, compliance with the respective third-party licenses is required. ## Citation ``` @misc{2406.13923, Author = {Junjie Wang and Yin Zhang and Yatai Ji and Yuxiang Zhang and Chunyang Jiang and Yubo Wang and Kang Zhu and Zekun Wang and Tiezhen Wang and Wenhao Huang and Jie Fu and Bei Chen and Qunshu Lin and Minghao Liu and Ge Zhang and Wenhu Chen}, Title = {PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents}, Year = {2024}, Eprint = {arXiv:2406.13923}, } ```

lmms-lab/LMMs-Eval-Lite

lmms-lab

--- dataset_info: - config_name: ai2d features: - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: image dtype: image splits: - name: lite num_bytes: 90543302.1658031 num_examples: 500 download_size: 81458737 dataset_size: 90543302.1658031 - config_name: chartqa features: - name: type dtype: string - name: question dtype: string - name: answer dtype: string - name: image dtype: image splits: - name: lite num_bytes: 23170424.2 num_examples: 500 download_size: 23219432 dataset_size: 23170424.2 - config_name: coco2017_cap_val features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answer sequence: string - name: id dtype: int64 - name: license dtype: int8 - name: file_name dtype: string - name: coco_url dtype: string - name: height dtype: int32 - name: width dtype: int32 - name: date_captured dtype: string splits: - name: lite num_bytes: 81724646.1 num_examples: 500 download_size: 81036195 dataset_size: 81724646.1 - config_name: docvqa_val features: - name: questionId dtype: string - name: question dtype: string - name: question_types sequence: string - name: image dtype: image - name: docId dtype: int64 - name: ucsf_document_id dtype: string - name: ucsf_document_page_no dtype: string - name: answers sequence: string - name: data_split dtype: string splits: - name: lite num_bytes: 334538449.19872874 num_examples: 500 download_size: 249349131 dataset_size: 334538449.19872874 - config_name: flickr30k_test features: - name: image dtype: image - name: caption sequence: string - name: sentids sequence: string - name: img_id dtype: string - name: filename dtype: string splits: - name: lite num_bytes: 69689341.17644653 num_examples: 500 download_size: 66621555 dataset_size: 69689341.17644653 - config_name: gqa features: - name: id dtype: string - name: imageId dtype: string - name: question dtype: string - name: answer dtype: string - name: fullAnswer dtype: string - name: isBalanced dtype: bool - name: groups struct: - name: global dtype: string - name: local dtype: string - name: entailed dtype: string - name: equivalent dtype: string - name: types struct: - name: structural dtype: string - name: semantic dtype: string - name: detailed dtype: string - name: annotations sequence: - name: question struct: - name: objectId dtype: string - name: value dtype: string - name: answer struct: - name: objectId dtype: string - name: value dtype: string - name: fullAnswer struct: - name: objectId dtype: string - name: value dtype: string - name: semantic list: - name: operation dtype: string - name: argument dtype: string - name: dependencies sequence: int32 - name: semanticStr dtype: string splits: - name: lite num_bytes: 243022.3008427413 num_examples: 500 download_size: 107530 dataset_size: 243022.3008427413 - config_name: infovqa_val features: - name: questionId dtype: string - name: question dtype: string - name: answers sequence: string - name: answer_type sequence: string - name: image dtype: image - name: image_url dtype: string - name: operation/reasoning sequence: string - name: ocr dtype: string - name: data_split dtype: string splits: - name: lite num_bytes: 304765105.6765441 num_examples: 500 download_size: 233689969 dataset_size: 304765105.6765441 - config_name: mmbench_cn_dev features: - name: index dtype: int64 - name: question dtype: string - name: hint dtype: string - name: answer dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: category dtype: string - name: image dtype: image - name: source dtype: string - name: L2-category dtype: string - name: comment dtype: string - name: split dtype: string splits: - name: lite num_bytes: 11861120.35112035 num_examples: 500 download_size: 12795903 dataset_size: 11861120.35112035 - config_name: mmbench_en_dev features: - name: index dtype: int64 - name: question dtype: string - name: hint dtype: string - name: answer dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: category dtype: string - name: image dtype: image - name: source dtype: string - name: L2-category dtype: string - name: comment dtype: string - name: split dtype: string splits: - name: lite num_bytes: 11871291.175791176 num_examples: 500 download_size: 12524588 dataset_size: 11871291.175791176 - config_name: nocaps_val features: - name: image dtype: image - name: image_coco_url dtype: string - name: image_date_captured dtype: string - name: image_file_name dtype: string - name: image_height dtype: int32 - name: image_width dtype: int32 - name: image_id dtype: int32 - name: image_license dtype: int8 - name: image_open_images_id dtype: string - name: annotations_ids sequence: int32 - name: annotations_captions sequence: string splits: - name: lite num_bytes: 157984760.66666666 num_examples: 500 download_size: 155545761 dataset_size: 157984760.66666666 - config_name: ok_vqa_val2014 features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answers sequence: string - name: question_type dtype: string - name: answer_type dtype: string splits: - name: lite num_bytes: 82607924.29647246 num_examples: 500 download_size: 80223931 dataset_size: 82607924.29647246 - config_name: refcoco_bbox_val features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answer sequence: string - name: segmentation sequence: float32 - name: bbox sequence: float32 - name: iscrowd dtype: int8 - name: file_name dtype: string splits: - name: lite num_bytes: 87885477.24435365 num_examples: 500 download_size: 88424601 dataset_size: 87885477.24435365 - config_name: seedbench features: - name: answer dtype: string - name: choice_a dtype: string - name: choice_b dtype: string - name: choice_c dtype: string - name: choice_d dtype: string - name: data_id dtype: string - name: data_type dtype: string - name: question dtype: string - name: question_id dtype: string - name: question_type_id dtype: int16 - name: image sequence: image - name: segment sequence: int64 splits: - name: lite num_bytes: 755921749.3379655 num_examples: 500 download_size: 181839440 dataset_size: 755921749.3379655 - config_name: textcaps_val features: - name: question_id dtype: string - name: question dtype: string - name: image dtype: image - name: image_id dtype: string - name: image_classes sequence: string - name: flickr_original_url dtype: string - name: flickr_300k_url dtype: string - name: image_width dtype: int64 - name: image_height dtype: int64 - name: set_name dtype: string - name: image_name dtype: string - name: image_path dtype: string - name: caption_id sequence: int64 - name: caption_str sequence: string - name: reference_strs sequence: string splits: - name: lite num_bytes: 145274544.53569174 num_examples: 500 download_size: 135721574 dataset_size: 145274544.53569174 - config_name: textvqa_val features: - name: image_id dtype: string - name: question_id dtype: int32 - name: question dtype: string - name: question_tokens sequence: string - name: image dtype: image - name: image_width dtype: int32 - name: image_height dtype: int32 - name: flickr_original_url dtype: string - name: flickr_300k_url dtype: string - name: answers sequence: string - name: image_classes sequence: string - name: set_name dtype: string - name: ocr_tokens sequence: string splits: - name: lite num_bytes: 143485382.6 num_examples: 500 download_size: 139843809 dataset_size: 143485382.6 - config_name: vizwiz_vqa_val features: - name: question_id dtype: string - name: image dtype: image - name: question dtype: string - name: answers sequence: string - name: category dtype: string splits: - name: lite num_bytes: 242880108.01111367 num_examples: 500 download_size: 232689462 dataset_size: 242880108.01111367 - config_name: vqav2_val features: - name: question_type dtype: string - name: multiple_choice_answer dtype: string - name: answers list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: image_id dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: image dtype: image splits: - name: lite num_bytes: 79046522.98300941 num_examples: 500 download_size: 78981610 dataset_size: 79046522.98300941 configs: - config_name: ai2d data_files: - split: lite path: ai2d/lite-* - config_name: chartqa data_files: - split: lite path: chartqa/lite-* - config_name: coco2017_cap_val data_files: - split: lite path: coco2017_cap_val/lite-* - config_name: docvqa_val data_files: - split: lite path: docvqa_val/lite-* - config_name: flickr30k_test data_files: - split: lite path: flickr30k_test/lite-* - config_name: gqa data_files: - split: lite path: gqa/lite-* - config_name: infovqa_val data_files: - split: lite path: infovqa_val/lite-* - config_name: mmbench_cn_dev data_files: - split: lite path: mmbench_cn_dev/lite-* - config_name: mmbench_en_dev data_files: - split: lite path: mmbench_en_dev/lite-* - config_name: nocaps_val data_files: - split: lite path: nocaps_val/lite-* - config_name: ok_vqa_val2014 data_files: - split: lite path: ok_vqa_val2014/lite-* - config_name: refcoco_bbox_val data_files: - split: lite path: refcoco_bbox_val/lite-* - config_name: seedbench data_files: - split: lite path: seedbench/lite-* - config_name: textcaps_val data_files: - split: lite path: textcaps_val/lite-* - config_name: textvqa_val data_files: - split: lite path: textvqa_val/lite-* - config_name: vizwiz_vqa_val data_files: - split: lite path: vizwiz_vqa_val/lite-* - config_name: vqav2_val data_files: - split: lite path: vqav2_val/lite-* ---

mhardalov/exams

mhardalov

--- annotations_creators: - found language_creators: - found language: - ar - bg - de - es - fr - hr - hu - it - lt - mk - pl - pt - sq - sr - tr - vi license: - cc-by-sa-4.0 multilinguality: - monolingual - multilingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: exams pretty_name: EXAMS config_names: - alignments - crosslingual_bg - crosslingual_hr - crosslingual_hu - crosslingual_it - crosslingual_mk - crosslingual_pl - crosslingual_pt - crosslingual_sq - crosslingual_sr - crosslingual_test - crosslingual_tr - crosslingual_vi - crosslingual_with_para_bg - crosslingual_with_para_hr - crosslingual_with_para_hu - crosslingual_with_para_it - crosslingual_with_para_mk - crosslingual_with_para_pl - crosslingual_with_para_pt - crosslingual_with_para_sq - crosslingual_with_para_sr - crosslingual_with_para_test - crosslingual_with_para_tr - crosslingual_with_para_vi - multilingual - multilingual_with_para dataset_info: - config_name: alignments features: - name: source_id dtype: string - name: target_id_list sequence: string splits: - name: full num_bytes: 1265256 num_examples: 10834 download_size: 184096 dataset_size: 1265256 - config_name: crosslingual_bg features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 1077329 num_examples: 2344 - name: validation num_bytes: 281771 num_examples: 593 download_size: 514922 dataset_size: 1359100 - config_name: crosslingual_hr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 807104 num_examples: 2341 - name: validation num_bytes: 176594 num_examples: 538 download_size: 450090 dataset_size: 983698 - config_name: crosslingual_hu features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 677535 num_examples: 1731 - name: validation num_bytes: 202012 num_examples: 536 download_size: 401455 dataset_size: 879547 - config_name: crosslingual_it features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 399312 num_examples: 1010 - name: validation num_bytes: 93175 num_examples: 246 download_size: 226376 dataset_size: 492487 - config_name: crosslingual_mk features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 825702 num_examples: 1665 - name: validation num_bytes: 204318 num_examples: 410 download_size: 394548 dataset_size: 1030020 - config_name: crosslingual_pl features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 573410 num_examples: 1577 - name: validation num_bytes: 141633 num_examples: 394 download_size: 341925 dataset_size: 715043 - config_name: crosslingual_pt features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 374798 num_examples: 740 - name: validation num_bytes: 87714 num_examples: 184 download_size: 208021 dataset_size: 462512 - config_name: crosslingual_sq features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 423744 num_examples: 1194 - name: validation num_bytes: 110093 num_examples: 311 download_size: 247052 dataset_size: 533837 - config_name: crosslingual_sr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 649560 num_examples: 1323 - name: validation num_bytes: 145724 num_examples: 314 download_size: 327466 dataset_size: 795284 - config_name: crosslingual_test features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: test num_bytes: 8402575 num_examples: 19736 download_size: 3438526 dataset_size: 8402575 - config_name: crosslingual_tr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 717599 num_examples: 1571 - name: validation num_bytes: 182730 num_examples: 393 download_size: 440914 dataset_size: 900329 - config_name: crosslingual_vi features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 953167 num_examples: 1955 - name: validation num_bytes: 231976 num_examples: 488 download_size: 462940 dataset_size: 1185143 - config_name: crosslingual_with_para_bg features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 47066808 num_examples: 2344 - name: validation num_bytes: 11916026 num_examples: 593 download_size: 15794611 dataset_size: 58982834 - config_name: crosslingual_with_para_hr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 24889604 num_examples: 2341 - name: validation num_bytes: 5695066 num_examples: 538 download_size: 9839452 dataset_size: 30584670 - config_name: crosslingual_with_para_hu features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 19035663 num_examples: 1731 - name: validation num_bytes: 6043265 num_examples: 536 download_size: 9263625 dataset_size: 25078928 - config_name: crosslingual_with_para_it features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 16409235 num_examples: 1010 - name: validation num_bytes: 4018329 num_examples: 246 download_size: 6907617 dataset_size: 20427564 - config_name: crosslingual_with_para_mk features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 38445894 num_examples: 1665 - name: validation num_bytes: 9673574 num_examples: 410 download_size: 12878474 dataset_size: 48119468 - config_name: crosslingual_with_para_pl features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 16373781 num_examples: 1577 - name: validation num_bytes: 4158832 num_examples: 394 download_size: 6539172 dataset_size: 20532613 - config_name: crosslingual_with_para_pt features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 12185383 num_examples: 740 - name: validation num_bytes: 3093712 num_examples: 184 download_size: 4956969 dataset_size: 15279095 - config_name: crosslingual_with_para_sq features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 17341277 num_examples: 1194 - name: validation num_bytes: 4449952 num_examples: 311 download_size: 7112236 dataset_size: 21791229 - config_name: crosslingual_with_para_sr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 24575845 num_examples: 1323 - name: validation num_bytes: 5772509 num_examples: 314 download_size: 8035415 dataset_size: 30348354 - config_name: crosslingual_with_para_test features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: test num_bytes: 207974374 num_examples: 13510 download_size: 62878029 dataset_size: 207974374 - config_name: crosslingual_with_para_tr features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 18597131 num_examples: 1571 - name: validation num_bytes: 4763097 num_examples: 393 download_size: 7346658 dataset_size: 23360228 - config_name: crosslingual_with_para_vi features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 40882999 num_examples: 1955 - name: validation num_bytes: 10260374 num_examples: 488 download_size: 13028078 dataset_size: 51143373 - config_name: multilingual features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 3381837 num_examples: 7961 - name: validation num_bytes: 1141687 num_examples: 2672 - name: test num_bytes: 5746781 num_examples: 13510 download_size: 4323915 dataset_size: 10270305 - config_name: multilingual_with_para features: - name: id dtype: string - name: question struct: - name: stem dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: para dtype: string - name: answerKey dtype: string - name: info struct: - name: grade dtype: int32 - name: subject dtype: string - name: language dtype: string splits: - name: train num_bytes: 127294567 num_examples: 7961 - name: validation num_bytes: 42711689 num_examples: 2672 - name: test num_bytes: 207974374 num_examples: 13510 download_size: 112597818 dataset_size: 377980630 configs: - config_name: alignments data_files: - split: full path: alignments/full-* - config_name: crosslingual_bg data_files: - split: train path: crosslingual_bg/train-* - split: validation path: crosslingual_bg/validation-* - config_name: crosslingual_hr data_files: - split: train path: crosslingual_hr/train-* - split: validation path: crosslingual_hr/validation-* - config_name: crosslingual_hu data_files: - split: train path: crosslingual_hu/train-* - split: validation path: crosslingual_hu/validation-* - config_name: crosslingual_it data_files: - split: train path: crosslingual_it/train-* - split: validation path: crosslingual_it/validation-* - config_name: crosslingual_mk data_files: - split: train path: crosslingual_mk/train-* - split: validation path: crosslingual_mk/validation-* - config_name: crosslingual_pl data_files: - split: train path: crosslingual_pl/train-* - split: validation path: crosslingual_pl/validation-* - config_name: crosslingual_pt data_files: - split: train path: crosslingual_pt/train-* - split: validation path: crosslingual_pt/validation-* - config_name: crosslingual_sq data_files: - split: train path: crosslingual_sq/train-* - split: validation path: crosslingual_sq/validation-* - config_name: crosslingual_sr data_files: - split: train path: crosslingual_sr/train-* - split: validation path: crosslingual_sr/validation-* - config_name: crosslingual_test data_files: - split: test path: crosslingual_test/test-* - config_name: crosslingual_tr data_files: - split: train path: crosslingual_tr/train-* - split: validation path: crosslingual_tr/validation-* - config_name: crosslingual_vi data_files: - split: train path: crosslingual_vi/train-* - split: validation path: crosslingual_vi/validation-* - config_name: crosslingual_with_para_bg data_files: - split: train path: crosslingual_with_para_bg/train-* - split: validation path: crosslingual_with_para_bg/validation-* - config_name: crosslingual_with_para_hr data_files: - split: train path: crosslingual_with_para_hr/train-* - split: validation path: crosslingual_with_para_hr/validation-* - config_name: crosslingual_with_para_hu data_files: - split: train path: crosslingual_with_para_hu/train-* - split: validation path: crosslingual_with_para_hu/validation-* - config_name: crosslingual_with_para_it data_files: - split: train path: crosslingual_with_para_it/train-* - split: validation path: crosslingual_with_para_it/validation-* - config_name: crosslingual_with_para_mk data_files: - split: train path: crosslingual_with_para_mk/train-* - split: validation path: crosslingual_with_para_mk/validation-* - config_name: crosslingual_with_para_pl data_files: - split: train path: crosslingual_with_para_pl/train-* - split: validation path: crosslingual_with_para_pl/validation-* - config_name: crosslingual_with_para_pt data_files: - split: train path: crosslingual_with_para_pt/train-* - split: validation path: crosslingual_with_para_pt/validation-* - config_name: crosslingual_with_para_sq data_files: - split: train path: crosslingual_with_para_sq/train-* - split: validation path: crosslingual_with_para_sq/validation-* - config_name: crosslingual_with_para_sr data_files: - split: train path: crosslingual_with_para_sr/train-* - split: validation path: crosslingual_with_para_sr/validation-* - config_name: crosslingual_with_para_test data_files: - split: test path: crosslingual_with_para_test/test-* - config_name: crosslingual_with_para_tr data_files: - split: train path: crosslingual_with_para_tr/train-* - split: validation path: crosslingual_with_para_tr/validation-* - config_name: crosslingual_with_para_vi data_files: - split: train path: crosslingual_with_para_vi/train-* - split: validation path: crosslingual_with_para_vi/validation-* - config_name: multilingual data_files: - split: train path: multilingual/train-* - split: validation path: multilingual/validation-* - split: test path: multilingual/test-* - config_name: multilingual_with_para data_files: - split: train path: multilingual_with_para/train-* - split: validation path: multilingual_with_para/validation-* - split: test path: multilingual_with_para/test-* default: true --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/mhardalov/exams-qa - **Paper:** [EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering](https://arxiv.org/abs/2011.03080) - **Point of Contact:** [hardalov@@fmi.uni-sofia.bg](hardalov@@fmi.uni-sofia.bg) ### Dataset Summary EXAMS is a benchmark dataset for multilingual and cross-lingual question answering from high school examinations. It consists of more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - ar - bg - de - es - fr - hr - hu - it - lt - mk - pl - pt - sq - sr - tr - vi ## Dataset Structure ### Data Instances An example of a data instance (with support paragraphs, in Bulgarian) is: ``` {'answerKey': 'C', 'id': '35dd6b52-7e71-11ea-9eb1-54bef70b159e', 'info': {'grade': 12, 'language': 'Bulgarian', 'subject': 'Biology'}, 'question': {'choices': {'label': ['A', 'B', 'C', 'D'], 'para': ['Това води до наследствени изменения между организмите. Мирновременните вождове са наследствени. Черният, сивият и кафявият цвят на оцветяване на тялото се определя от пигмента меланин и възниква в резултат на наследствени изменения. Тези различия, според Монтескьо, не са наследствени. Те са и важни наследствени вещи в клана. Те са били наследствени архонти и управляват демократично. Реликвите са исторически, религиозни, семейни (наследствени) и технически. Общо са направени 800 изменения. Не всички наследствени аномалии на хемоглобина са вредни, т.е. Моногенните наследствени болести, които водят до мигрена, са редки. Няма наследствени владетели. Повечето от тях са наследствени и се предават на потомството. Всичките синове са ерцхерцози на всичките наследствени земи и претенденти. През 1509 г. Фраунбергите са издигнати на наследствени имперски графове. Фамилията Валдбург заради постиженията са номинирани на „наследствени имперски трушсеси“. Фамилията Валдбург заради постиженията са номинирани на „наследствени имперски трушсеси“. Описани са единични наследствени случаи, но по-често липсва фамилна обремененост. Позициите им са наследствени и се предават в рамките на клана. Внесени са изменения в конструкцията на веригите. и са направени изменения в ходовата част. На храма са правени лоши архитектурни изменения. Изменения са предприети и вътре в двореца. Имало двама наследствени вождове. Имало двама наследствени вождове. Годишният календар, „компасът“ и биологичния часовник са наследствени и при много бозайници.', 'Постепенно задълбочаващите се функционални изменения довеждат и до структурни изменения. Те се дължат както на растягането на кожата, така и на въздействието на хормоналните изменения върху кожната тъкан. тези изменения се долавят по-ясно. Впоследствие, той претърпява изменения. Ширината остава без изменения. След тяхното издаване се налагат изменения в първоначалния Кодекс, защото не е съобразен с направените в Дигестите изменения. Еволюционният преход се характеризира със следните изменения: Наблюдават се и сезонни изменения в теглото. Приемат се изменения и допълнения към Устава. Тук се размножават и предизвикват възпалителни изменения. Общо са направени 800 изменения. Бронирането не претърпява съществени изменения. При животните се откриват изменения при злокачествената форма. Срещат се и дегенеративни изменения в семенните каналчета. ТАВКР „Баку“ се строи по изменения проект 1143.4. Трансът се съпровожда с определени изменения на мозъчната дейност. На изменения е подложен и Светия Синод. Внесени са изменения в конструкцията на веригите. На храма са правени лоши архитектурни изменения. Оттогава стиховете претърпяват изменения няколко пъти. Настъпват съществени изменения в музикалната култура. По-късно той претърпява леки изменения. Настъпват съществени изменения в музикалната култура. Претърпява сериозни изменения само носовата надстройка. Хоризонталното брониране е оставено без изменения.', 'Модификациите са обратими. Тези реакции са обратими. В началните стадии тези натрупвания са обратими. Всички такива ефекти са временни и обратими. Много от реакциите са обратими и идентични с тези при гликолизата. Ако в обращение има книжни пари, те са обратими в злато при поискване . Общо са направени 800 изменения. Непоследователността е представена от принципа на "симетрия", при който взаимоотношенията са разглеждани като симетрични или обратими. Откакто формулите в клетките на електронната таблица не са обратими, тази техника е с ограничена стойност. Ефектът на Пелтие-Зеебек и ефектът Томсън са обратими (ефектът на Пелтие е обратен на ефекта на Зеебек). Плазмолизата протича в три етапа, в зависимост от силата и продължителността на въздействието:\n\nПървите два етапа са обратими. Внесени са изменения в конструкцията на веригите. и са направени изменения в ходовата част. На храма са правени лоши архитектурни изменения. Изменения са предприети и вътре в двореца. Оттогава насетне екипите не са претърпявали съществени изменения. Изменения са направени и в колесника на машината. Тези изменения са обявени през октомври 1878 година. Последните изменения са внесени през януари 2009 година. В процеса на последващото проектиране са внесени някои изменения. Сериозните изменения са в края на Втората световна война. Внесени са изменения в конструкцията на погребите и подемниците. Внесени са изменения в конструкцията на погребите и подемниците. Внесени са изменения в конструкцията на погребите и подемниците. Постепенно задълбочаващите се функционални изменения довеждат и до структурни изменения.', 'Ерозионни процеси от масов характер липсват. Обновлението в редиците на партията приема масов характер. Тя обаче няма масов характер поради спецификата на формата. Движението против десятъка придобива масов характер и в Балчишка околия. Понякога екзекутирането на „обсебените от Сатана“ взимало невероятно масов характер. Укриването на дължими като наряд продукти в селата придобива масов характер. Периодичните миграции са в повечето случаи с масов характер и са свързани със сезонните изменения в природата, а непериодичните са премествания на животни, които настъпват след пожари, замърсяване на средата, висока численост и др. Имат необратим характер. Именно по време на двувековните походи на западните рицари използването на гербовете придобива масов характер. След присъединяването на Южен Кавказ към Русия, изселването на азербайджанци от Грузия придобива масов характер. Те имат нормативен характер. Те имат установителен характер. Освобождаването на работна сила обикновено има масов характер, защото обхваща големи контингенти от носителите на труд. Валежите имат подчертано континентален характер. Имат най-често издънков характер. Приливите имат предимно полуденонощен характер. Някои от тях имат мистериален характер. Тези сведения имат случаен, епизодичен характер. Те имат сезонен или годишен характер. Временните обезпечителни мерки имат временен характер. Други имат пожелателен характер (Здравко, Слава). Ловът и събирачеството имат спомагателен характер. Фактически успяват само малко да усилят бронирането на артилерийските погреби, другите изменения носят само частен характер. Някои карикатури имат само развлекателен характер, докато други имат политически нюанси. Поемите на Хезиод имат по-приложен характер.'], 'text': ['дължат се на фенотипни изменения', 'имат масов характер', 'са наследствени', 'са обратими']}, 'stem': 'Мутационите изменения:'}} ``` ### Data Fields A data instance contains the following fields: - `id`: A question ID, unique across the dataset - `question`: the question contains the following: - `stem`: a stemmed representation of the question textual - `choices`: a set of 3 to 5 candidate answers, which each have: - `text`: the text of the answers - `label`: a label in `['A', 'B', 'C', 'D', 'E']` used to match to the `answerKey` - `para`: (optional) a supported paragraph from Wikipedia in the same language as the question and answer - `answerKey`: the key corresponding to the right answer's `label` - `info`: some additional information on the question including: - `grade`: the school grade for the exam this question was taken from - `subject`: a free text description of the academic subject - `language`: the English name of the language for this question ### Data Splits Depending on the configuration, the dataset have different splits: - "alignments": a single "full" split - "multilingual" and "multilingual_with_para": "train", "validation" and "test" splits - "crosslingual_test" and "crosslingual_with_para_test": a single "test" split - the rest of crosslingual configurations: "train" and "validation" splits ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Eχαµs was collected from official state exams prepared by the ministries of education of various countries. These exams are taken by students graduating from high school, and often require knowledge learned through the entire course. The questions cover a large variety of subjects and material based on the country’s education system. They cover major school subjects such as Biology, Chemistry, Geography, History, and Physics, but we also highly specialized ones such as Agriculture, Geology, Informatics, as well as some applied and profiled studies. Some countries allow students to take official examinations in several languages. This dataset provides 9,857 parallel question pairs spread across seven languages coming from Croatia (Croatian, Serbian, Italian, Hungarian), Hungary (Hungarian, German, French, Spanish, Croatian, Serbian, Italian), and North Macedonia (Macedonian, Albanian, Turkish). For all languages in the dataset, the first step in the process of data collection was to download the PDF files per year, per subject, and per language (when parallel languages were available in the same source), convert the PDF files to text, and select those that were well formatted and followed the document structure. Then, Regular Expressions (RegEx) were used to parse the questions, their corresponding choices and the correct answer choice. In order to ensure that all our questions are answerable using textual input only, questions that contained visual information were removed, as selected by using curated list of words such as map, table, picture, graph, etc., in the corresponding language. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information The dataset, which contains paragraphs from Wikipedia, is licensed under CC-BY-SA 4.0. The code in this repository is licensed according the [LICENSE file](https://raw.githubusercontent.com/mhardalov/exams-qa/main/LICENSE). ### Citation Information ``` @inproceedings{hardalov-etal-2020-exams, title = "{EXAMS}: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering", author = "Hardalov, Momchil and Mihaylov, Todor and Zlatkova, Dimitrina and Dinkov, Yoan and Koychev, Ivan and Nakov, Preslav", editor = "Webber, Bonnie and Cohn, Trevor and He, Yulan and Liu, Yang", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.438", doi = "10.18653/v1/2020.emnlp-main.438", pages = "5427--5444", } ``` ### Contributions Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset.

open-web-math/open-web-math

open-web-math

--- dataset_info: features: - name: url dtype: string - name: text dtype: string - name: date dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 56651995057 num_examples: 6315233 download_size: 16370689925 dataset_size: 56651995057 license: odc-by task_categories: - text-generation language: - en pretty_name: OpenWebMath size_categories: - 10B<n<100B --- <img src="imgs/OpenWebMath-left.png" width="300"> [Keiran Paster](https://keirp.com)\*, [Marco Dos Santos](https://marco-dossantos.github.io/)\*, [Zhangir Azerbayev](https://zhangir-azerbayev.github.io/), [Jimmy Ba](https://jimmylba.github.io/) [GitHub ](https://github.com/keirp/OpenWebMath) | [ArXiv](https://arxiv.org/abs/2310.06786) | [PDF](https://arxiv.org/pdf/2310.06786.pdf) **OpenWebMath** is a dataset containing the majority of the high-quality, mathematical text from the internet. It is filtered and extracted from over 200B HTML files on Common Crawl down to a set of **6.3 million documents** containing a total of **14.7B tokens**. OpenWebMath is intended for use in _pretraining_ and _finetuning_ large language models. You can download the dataset using Hugging Face: ```python from datasets import load_dataset ds = load_dataset("open-web-math/open-web-math") ``` # OpenWebMath Contents The dataset is structured as follows: ```python { "text": ..., # document text. "url": ..., # document url. "date": ..., # date the page was crawled. "metadata": ..., # JSON containing information from the extraction process. } ``` OpenWebMath contains documents from over 130k different domains, including data from forums, educational pages, and blogs. The dataset contains documents covering mathematics, physics, statistics, computer science, and more. The following table shows the most common domains in OpenWebMath by character count. | Domain | # Characters | % Characters | | ----------------- | ------------- | ------------ | | stackexchange.com | 4,655,132,784 | 9.55% | | nature.com | 1,529,935,838 | 3.14% | | wordpress.com | 1,294,166,938 | 2.66% | | physicsforums.com | 1,160,137,919 | 2.38% | | github.io | 725,689,722 | 1.49% | | zbmath.org | 620,019,503 | 1.27% | | wikipedia.org | 618,024,754 | 1.27% | | groundai.com | 545,214,990 | 1.12% | | blogspot.com | 520,392,333 | 1.07% | | mathoverflow.net | 499,102,560 | 1.02% | # OpenWebMath Pipeline <img src="imgs/pipeline.png" alt="Overview of the OpenWebMath Pipeline"> OpenWebMath builds on the massive [Common Crawl](https://commoncrawl.org/) dataset, which contains over 200B HTML documents. We filtered the data to only include documents that are: (1) in English, (2) contain mathematical content, and (3) are of high quality. We also put a strong emphasis on extracting LaTeX content from the HTML documents as well as reducing boilerplate in comparison to other web datasets. The OpenWebMath pipeline consists of five steps: 1. **Prefiltering HTML Documents**: - We apply a simple prefilter to all HTML documents in Common Crawl in order to skip documents without mathematical content to unnecessary processing time. 2. **Text Extraction**: - Extract text, including LaTeX content, from the HTML documents while removing boilerplate. 3. **Content Classification and Filtering**: - Apply a [FastText language identification model](https://fasttext.cc/docs/en/language-identification.html) to keep only English documents. - Filter high perplexity documents using a [KenLM](https://github.com/kpu/kenlm) model trained on [Proof-Pile](https://huggingface.co/datasets/hoskinson-center/proof-pile). - Filter non-mathematical documents using our own _MathScore_ model. 4. **Deduplication**: - Deduplicate the dataset using SimHash in [text-dedup](https://github.com/ChenghaoMou/text-dedup). 5. **Manual Inspection**: - Inspect the documents gathered from previous steps and remove low quality pages. For a detailed discussion on the processing pipeline, please refer to our paper. # License OpenWebMath is made available under an ODC-By 1.0 license; users should also abide by the CommonCrawl ToU: [https://commoncrawl.org/terms-of-use/](https://commoncrawl.org/terms-of-use/). We do not alter the license of any of the underlying data. # Citation Information ``` @misc{paster2023openwebmath, title={OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text}, author={Keiran Paster and Marco Dos Santos and Zhangir Azerbayev and Jimmy Ba}, year={2023}, eprint={2310.06786}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

Voxel51/Coursera_homework_dataset_train

Voxel51

--- annotations_creators: [] language: en size_categories: - 10K<n<100K task_categories: - object-detection task_ids: [] pretty_name: homework_dataset_train tags: - fiftyone - image - object-detection dataset_summary: ' This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 18287 samples. ## Installation If you haven''t already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include ''max_samples'', etc dataset = fouh.load_from_hub("Voxel51/Coursera_homework_dataset_train") # Launch the App session = fo.launch_app(dataset) ``` ' --- # Dataset Card for Homework Training Set for Coursera MOOC - Hands Data Centric Visual AI This dataset is the **training dataset for the homework assignments** of the Hands-on Data Centric AI Coursera course. This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 18287 samples. ## Installation If you haven't already, install FiftyOne: ```bash pip install -U fiftyone ``` ## Usage ```python import fiftyone as fo import fiftyone.utils.huggingface as fouh # Load the dataset # Note: other available arguments include 'max_samples', etc dataset = fouh.load_from_hub("Voxel51/Coursera_homework_dataset_train") # Launch the App session = fo.launch_app(dataset) ``` ## Dataset Details ### Dataset Description This dataset is a modified subset of the [LVIS dataset](https://www.lvisdataset.org/). The dataset here only contains detections, some of which have been artificially perturbed and altered to demonstrate data centric AI techniques and methodologies for the course. This dataset has the following labels: - 'bolt' - 'knob' - 'tag' - 'button' - 'bottle_cap' - 'belt' - 'strap' - 'necktie' - 'shirt' - 'sweater' - 'streetlight' - 'pole' - 'reflector' - 'headlight' - 'taillight' - 'traffic_light' - 'rearview_mirror' ### Dataset Sources - **Repository:** https://www.lvisdataset.org/ - **Paper:** https://arxiv.org/abs/1908.03195 ## Uses The labels in this dataset have been perturbed to illustrate data centric AI techniques for the Hands-on Data Centric AI Coursera MOOC. ## Dataset Structure Each image in the dataset comes with detailed annotations in FiftyOne detection format. A typical annotation looks like this: ```python <Detection: { 'id': '66a2f24cce2f9d11d98d3a21', 'attributes': {}, 'tags': [], 'label': 'shirt', 'bounding_box': [ 0.25414, 0.35845238095238097, 0.041960000000000004, 0.051011904761904765, ], 'mask': None, 'confidence': None, 'index': None, }> ``` ## Dataset Creation ### Curation Rationale The selected labels for this dataset is because these objects can be confusing to a model. Thus, making them a great choice for demonstrating data centric AI techniques. ### Source Data This is a subset of the [LVIS dataset.](https://www.lvisdataset.org/) ## Citation **BibTeX:** ```bibtex @inproceedings{gupta2019lvis, title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation}, author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross}, booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition}, year={2019} } ```

bigstupidhats/openai_MMMLU_arb

bigstupidhats

--- dataset_info: - config_name: abstract_algebra features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 72350.44153254523 num_examples: 100 download_size: 11499 dataset_size: 72350.44153254523 - config_name: anatomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 97673.09606893605 num_examples: 135 download_size: 22742 dataset_size: 97673.09606893605 - config_name: astronomy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 109972.67112946874 num_examples: 152 download_size: 33903 dataset_size: 109972.67112946874 - config_name: business_ethics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 72350.44153254523 num_examples: 100 download_size: 24427 dataset_size: 72350.44153254523 - config_name: clinical_knowledge features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 191728.67006124483 num_examples: 265 download_size: 46922 dataset_size: 191728.67006124483 - config_name: college_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 104184.63580686512 num_examples: 144 download_size: 38338 dataset_size: 104184.63580686512 - config_name: college_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 72350.44153254523 num_examples: 100 download_size: 20462 dataset_size: 72350.44153254523 - config_name: college_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 72350.44153254523 num_examples: 100 download_size: 33391 dataset_size: 72350.44153254523 - config_name: college_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 72350.44153254523 num_examples: 100 download_size: 18878 dataset_size: 72350.44153254523 - config_name: college_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 125166.26385130323 num_examples: 173 download_size: 54143 dataset_size: 125166.26385130323 - config_name: college_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 73797.45036319613 num_examples: 102 download_size: 20769 dataset_size: 73797.45036319613 - config_name: computer_security features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 72350.44153254523 num_examples: 100 download_size: 24068 dataset_size: 72350.44153254523 - config_name: conceptual_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 170023.53760148128 num_examples: 235 download_size: 29074 dataset_size: 170023.53760148128 - config_name: econometrics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 82479.50334710155 num_examples: 114 download_size: 29766 dataset_size: 82479.50334710155 - config_name: electrical_engineering features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 104908.14022219057 num_examples: 145 download_size: 20777 dataset_size: 104908.14022219057 - config_name: elementary_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 273484.66899302095 num_examples: 378 download_size: 51448 dataset_size: 273484.66899302095 - config_name: formal_logic features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 91161.55633100698 num_examples: 126 download_size: 25601 dataset_size: 91161.55633100698 - config_name: global_facts features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 72350.44153254523 num_examples: 100 download_size: 13796 dataset_size: 72350.44153254523 - config_name: high_school_biology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 224286.3687508902 num_examples: 310 download_size: 78285 dataset_size: 224286.3687508902 - config_name: high_school_chemistry features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 146871.3963110668 num_examples: 203 download_size: 40014 dataset_size: 146871.3963110668 - config_name: high_school_computer_science features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 72350.44153254523 num_examples: 100 download_size: 32202 dataset_size: 72350.44153254523 - config_name: high_school_european_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 119378.22852869962 num_examples: 165 download_size: 182784 dataset_size: 119378.22852869962 - config_name: high_school_geography features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 143253.87423443954 num_examples: 198 download_size: 32130 dataset_size: 143253.87423443954 - config_name: high_school_government_and_politics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 139636.35215781227 num_examples: 193 download_size: 48784 dataset_size: 139636.35215781227 - config_name: high_school_macroeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 282166.7219769264 num_examples: 390 download_size: 70007 dataset_size: 282166.7219769264 - config_name: high_school_mathematics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 195346.1921378721 num_examples: 270 download_size: 38633 dataset_size: 195346.1921378721 - config_name: high_school_microeconomics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 172194.05084745763 num_examples: 238 download_size: 47850 dataset_size: 172194.05084745763 - config_name: high_school_physics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 109249.16671414328 num_examples: 151 download_size: 38837 dataset_size: 109249.16671414328 - config_name: high_school_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 394309.90635237144 num_examples: 545 download_size: 113613 dataset_size: 394309.90635237144 - config_name: high_school_statistics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 156276.95371029768 num_examples: 216 download_size: 76494 dataset_size: 156276.95371029768 - config_name: high_school_us_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 147594.90072639225 num_examples: 204 download_size: 205628 dataset_size: 147594.90072639225 - config_name: high_school_world_history features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 171470.54643213216 num_examples: 237 download_size: 254857 dataset_size: 171470.54643213216 - config_name: human_aging features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 161341.48461757586 num_examples: 223 download_size: 38982 dataset_size: 161341.48461757586 - config_name: human_sexuality features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 94779.07840763424 num_examples: 131 download_size: 26030 dataset_size: 94779.07840763424 - config_name: international_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 87544.03425437972 num_examples: 121 download_size: 35229 dataset_size: 87544.03425437972 - config_name: jurisprudence features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 78138.47685514884 num_examples: 108 download_size: 26611 dataset_size: 78138.47685514884 - config_name: logical_fallacies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 117931.2196980487 num_examples: 163 download_size: 26662 dataset_size: 117931.2196980487 - config_name: machine_learning features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 81032.49451645065 num_examples: 112 download_size: 24490 dataset_size: 81032.49451645065 - config_name: management features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 74520.95477852158 num_examples: 103 download_size: 16412 dataset_size: 74520.95477852158 - config_name: marketing features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 169300.0331861558 num_examples: 234 download_size: 44445 dataset_size: 169300.0331861558 - config_name: medical_genetics features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 72350.44153254523 num_examples: 100 download_size: 17991 dataset_size: 72350.44153254523 - config_name: miscellaneous features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 566503.957199829 num_examples: 783 download_size: 118897 dataset_size: 566503.957199829 - config_name: moral_disputes features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 250332.52770260646 num_examples: 346 download_size: 75135 dataset_size: 250332.52770260646 - config_name: moral_scenarios features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 647536.4517162797 num_examples: 895 download_size: 130719 dataset_size: 647536.4517162797 - config_name: nutrition features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 221392.35108958837 num_examples: 306 download_size: 66880 dataset_size: 221392.35108958837 - config_name: philosophy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 225009.87316621564 num_examples: 311 download_size: 57405 dataset_size: 225009.87316621564 - config_name: prehistory features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 234415.43056544653 num_examples: 324 download_size: 67197 dataset_size: 234415.43056544653 - config_name: professional_accounting features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 204028.24512177752 num_examples: 282 download_size: 89142 dataset_size: 204028.24512177752 - config_name: professional_law features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 1109855.7731092437 num_examples: 1534 download_size: 1293223 dataset_size: 1109855.7731092437 - config_name: professional_medicine features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 196793.20096852302 num_examples: 272 download_size: 160070 dataset_size: 196793.20096852302 - config_name: professional_psychology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 442784.70217917673 num_examples: 612 download_size: 158926 dataset_size: 442784.70217917673 - config_name: public_relations features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 79585.48568579974 num_examples: 110 download_size: 23042 dataset_size: 79585.48568579974 - config_name: security_studies features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 177258.5817547358 num_examples: 245 download_size: 144246 dataset_size: 177258.5817547358 - config_name: sociology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 145424.3874804159 num_examples: 201 download_size: 51479 dataset_size: 145424.3874804159 - config_name: us_foreign_policy features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 72350.44153254523 num_examples: 100 download_size: 22041 dataset_size: 72350.44153254523 - config_name: virology features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 120101.73294402506 num_examples: 166 download_size: 32006 dataset_size: 120101.73294402506 - config_name: world_religions features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 splits: - name: test num_bytes: 123719.25502065233 num_examples: 171 download_size: 20673 dataset_size: 123719.25502065233 configs: - config_name: abstract_algebra data_files: - split: test path: abstract_algebra/test-* - config_name: anatomy data_files: - split: test path: anatomy/test-* - config_name: astronomy data_files: - split: test path: astronomy/test-* - config_name: business_ethics data_files: - split: test path: business_ethics/test-* - config_name: clinical_knowledge data_files: - split: test path: clinical_knowledge/test-* - config_name: college_biology data_files: - split: test path: college_biology/test-* - config_name: college_chemistry data_files: - split: test path: college_chemistry/test-* - config_name: college_computer_science data_files: - split: test path: college_computer_science/test-* - config_name: college_mathematics data_files: - split: test path: college_mathematics/test-* - config_name: college_medicine data_files: - split: test path: college_medicine/test-* - config_name: college_physics data_files: - split: test path: college_physics/test-* - config_name: computer_security data_files: - split: test path: computer_security/test-* - config_name: conceptual_physics data_files: - split: test path: conceptual_physics/test-* - config_name: econometrics data_files: - split: test path: econometrics/test-* - config_name: electrical_engineering data_files: - split: test path: electrical_engineering/test-* - config_name: elementary_mathematics data_files: - split: test path: elementary_mathematics/test-* - config_name: formal_logic data_files: - split: test path: formal_logic/test-* - config_name: global_facts data_files: - split: test path: global_facts/test-* - config_name: high_school_biology data_files: - split: test path: high_school_biology/test-* - config_name: high_school_chemistry data_files: - split: test path: high_school_chemistry/test-* - config_name: high_school_computer_science data_files: - split: test path: high_school_computer_science/test-* - config_name: high_school_european_history data_files: - split: test path: high_school_european_history/test-* - config_name: high_school_geography data_files: - split: test path: high_school_geography/test-* - config_name: high_school_government_and_politics data_files: - split: test path: high_school_government_and_politics/test-* - config_name: high_school_macroeconomics data_files: - split: test path: high_school_macroeconomics/test-* - config_name: high_school_mathematics data_files: - split: test path: high_school_mathematics/test-* - config_name: high_school_microeconomics data_files: - split: test path: high_school_microeconomics/test-* - config_name: high_school_physics data_files: - split: test path: high_school_physics/test-* - config_name: high_school_psychology data_files: - split: test path: high_school_psychology/test-* - config_name: high_school_statistics data_files: - split: test path: high_school_statistics/test-* - config_name: high_school_us_history data_files: - split: test path: high_school_us_history/test-* - config_name: high_school_world_history data_files: - split: test path: high_school_world_history/test-* - config_name: human_aging data_files: - split: test path: human_aging/test-* - config_name: human_sexuality data_files: - split: test path: human_sexuality/test-* - config_name: international_law data_files: - split: test path: international_law/test-* - config_name: jurisprudence data_files: - split: test path: jurisprudence/test-* - config_name: logical_fallacies data_files: - split: test path: logical_fallacies/test-* - config_name: machine_learning data_files: - split: test path: machine_learning/test-* - config_name: management data_files: - split: test path: management/test-* - config_name: marketing data_files: - split: test path: marketing/test-* - config_name: medical_genetics data_files: - split: test path: medical_genetics/test-* - config_name: miscellaneous data_files: - split: test path: miscellaneous/test-* - config_name: moral_disputes data_files: - split: test path: moral_disputes/test-* - config_name: moral_scenarios data_files: - split: test path: moral_scenarios/test-* - config_name: nutrition data_files: - split: test path: nutrition/test-* - config_name: philosophy data_files: - split: test path: philosophy/test-* - config_name: prehistory data_files: - split: test path: prehistory/test-* - config_name: professional_accounting data_files: - split: test path: professional_accounting/test-* - config_name: professional_law data_files: - split: test path: professional_law/test-* - config_name: professional_medicine data_files: - split: test path: professional_medicine/test-* - config_name: professional_psychology data_files: - split: test path: professional_psychology/test-* - config_name: public_relations data_files: - split: test path: public_relations/test-* - config_name: security_studies data_files: - split: test path: security_studies/test-* - config_name: sociology data_files: - split: test path: sociology/test-* - config_name: us_foreign_policy data_files: - split: test path: us_foreign_policy/test-* - config_name: virology data_files: - split: test path: virology/test-* - config_name: world_religions data_files: - split: test path: world_religions/test-* ---

tau/scrolls

tau

--- language: - en task_categories: - question-answering - summarization - text-generation task_ids: - multiple-choice-qa - natural-language-inference paperswithcode_id: scrolls configs: - gov_report - summ_screen_fd - qmsum - qasper - narrative_qa - quality - contract_nli tags: - query-based-summarization - long-texts --- ## Dataset Description - **Homepage:** [SCROLLS](https://www.scrolls-benchmark.com/) - **Repository:** [SCROLLS Github repository](https://github.com/tau-nlp/scrolls) - **Paper:** [SCROLLS: Standardized CompaRison Over Long Language Sequences ](https://arxiv.org/pdf/2201.03533.pdf) - **Leaderboard:** [Leaderboard](https://www.scrolls-benchmark.com/leaderboard) - **Point of Contact:** [[email protected]]([email protected]) # Dataset Card for SCROLLS ## Overview SCROLLS is a suite of datasets that require synthesizing information over long texts. The benchmark includes seven natural language tasks across multiple domains, including summarization, question answering, and natural language inference. ## Leaderboard The SCROLLS benchmark leaderboard can be found [here](https://www.scrolls-benchmark.com/leaderboard). ## Tasks SCROLLS comprises the following tasks: #### GovReport ([Huang et al., 2021](https://arxiv.org/pdf/2104.02112.pdf)) GovReport is a summarization dataset of reports addressing various national policy issues published by the Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary. The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets; for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively. #### SummScreenFD ([Chen et al., 2021](https://arxiv.org/pdf/2104.07091.pdf)) SummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones). Given a transcript of a specific episode, the goal is to produce the episode's recap. The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts. For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows, making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows. Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze. #### QMSum ([Zhong et al., 2021](https://arxiv.org/pdf/2104.05938.pdf)) QMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains. The corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control, and committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues. Annotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions, while ensuring that the relevant text for answering each query spans at least 200 words or 10 turns. #### NarrativeQA ([Kočiský et al., 2018](https://arxiv.org/pdf/1712.07040.pdf)) NarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites. Annotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs, resulting in about 30 questions and answers for each of the 1,567 books and scripts. They were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast. Each question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical). #### Qasper ([Dasigi et al., 2021](https://arxiv.org/pdf/2105.03011.pdf)) Qasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC). Questions were written by NLP practitioners after reading only the title and abstract of the papers, while another set of NLP practitioners annotated the answers given the entire document. Qasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones. #### QuALITY ([Pang et al., 2021](https://arxiv.org/pdf/2112.08608.pdf)) QuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg, the Open American National Corpus, and more. Experienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them, human annotators must read large portions of the given document. Reference answers were then calculated using the majority vote between of the annotators and writer's answers. To measure the difficulty of their questions, Pang et al. conducted a speed validation process, where another set of annotators were asked to answer questions given only a short period of time to skim through the document. As a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer. #### ContractNLI ([Koreeda and Manning, 2021](https://arxiv.org/pdf/2110.01799.pdf)) Contract NLI is a natural language inference dataset in the legal domain. Given a non-disclosure agreement (the premise), the task is to predict whether a particular legal statement (the hypothesis) is entailed, not entailed (neutral), or cannot be entailed (contradiction) from the contract. The NDAs were manually picked after simple filtering from the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR) and Google. The dataset contains a total of 607 contracts and 17 unique hypotheses, which were combined to produce the dataset's 10,319 examples. ## Data Fields All the datasets in the benchmark are in the same input-output format - `input`: a `string` feature. The input document. - `output`: a `string` feature. The target. - `id`: a `string` feature. Unique per input. - `pid`: a `string` feature. Unique per input-output pair (can differ from 'id' in NarrativeQA and Qasper, where there is more then one valid target). ## Citation If you use the SCROLLS data, **please make sure to cite all of the original dataset papers.** [[bibtex](https://scrolls-tau.s3.us-east-2.amazonaws.com/scrolls_datasets.bib)] ``` @inproceedings{shaham-etal-2022-scrolls, title = "{SCROLLS}: Standardized {C}ompa{R}ison Over Long Language Sequences", author = "Shaham, Uri and Segal, Elad and Ivgi, Maor and Efrat, Avia and Yoran, Ori and Haviv, Adi and Gupta, Ankit and Xiong, Wenhan and Geva, Mor and Berant, Jonathan and Levy, Omer", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.823", pages = "12007--12021", } ```

andstor/methods2test

andstor

--- language: - en license: mit task_categories: - text-generation configs: - config_name: fm data_files: - split: train path: data/fm/train-* - split: test path: data/fm/test-* - split: validation path: data/fm/validation-* - config_name: fm_indented data_files: - split: train path: data/fm_indented/train-* - split: test path: data/fm_indented/test-* - split: validation path: data/fm_indented/validation-* - config_name: fm+t data_files: - split: train path: data/fm+t/train-* - split: test path: data/fm+t/test-* - split: validation path: data/fm+t/validation-* - config_name: fm+fc data_files: - split: train path: data/fm+fc/train-* - split: test path: data/fm+fc/test-* - split: validation path: data/fm+fc/validation-* - config_name: fm+fc+t+tc data_files: - split: train path: data/fm+fc+t+tc/train-* - split: test path: data/fm+fc+t+tc/test-* - split: validation path: data/fm+fc+t+tc/validation-* - config_name: fm+fc+c data_files: - split: train path: data/fm+fc+c/train-* - split: test path: data/fm+fc+c/test-* - split: validation path: data/fm+fc+c/validation-* - config_name: fm+fc+c+t+tc data_files: - split: train path: data/fm+fc+c+t+tc/train-* - split: test path: data/fm+fc+c+t+tc/test-* - split: validation path: data/fm+fc+c+t+tc/validation-* - config_name: fm+fc+c+m data_files: - split: train path: data/fm+fc+c+m/train-* - split: test path: data/fm+fc+c+m/test-* - split: validation path: data/fm+fc+c+m/validation-* - config_name: fm+fc+c+m+t+tc data_files: - split: train path: data/fm+fc+c+m+t+tc/train-* - split: test path: data/fm+fc+c+m+t+tc/test-* - split: validation path: data/fm+fc+c+m+t+tc/validation-* - config_name: fm+fc+c+m+f data_files: - split: train path: data/fm+fc+c+m+f/train-* - split: test path: data/fm+fc+c+m+f/test-* - split: validation path: data/fm+fc+c+m+f/validation-* - config_name: fm+fc+c+m+f+t+tc data_files: - split: train path: data/fm+fc+c+m+f+t+tc/train-* - split: test path: data/fm+fc+c+m+f+t+tc/test-* - split: validation path: data/fm+fc+c+m+f+t+tc/validation-* - config_name: t data_files: - split: train path: data/t/train-* - split: test path: data/t/test-* - split: validation path: data/t/validation-* - config_name: t_indented data_files: - split: train path: data/t_indented/train-* - split: test path: data/t_indented/test-* - split: validation path: data/t_indented/validation-* - config_name: t+tc data_files: - split: train path: data/t+tc/train-* - split: test path: data/t+tc/test-* - split: validation path: data/t+tc/validation-* dataset_info: - config_name: fm features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 440444124 num_examples: 624022 - name: test num_bytes: 59407291 num_examples: 78388 - name: validation num_bytes: 57170315 num_examples: 78534 download_size: 99172217 dataset_size: 557021730 - config_name: fm+fc features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 506130678 num_examples: 624022 - name: test num_bytes: 68407490 num_examples: 78388 - name: validation num_bytes: 65318956 num_examples: 78534 download_size: 109141139 dataset_size: 639857124 - config_name: fm+fc+c features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 569209100 num_examples: 624022 - name: test num_bytes: 75552573 num_examples: 78388 - name: validation num_bytes: 73101169 num_examples: 78534 download_size: 117996353 dataset_size: 717862842 - config_name: fm+fc+c+m features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 1354004338 num_examples: 624022 - name: test num_bytes: 187724929 num_examples: 78388 - name: validation num_bytes: 184349299 num_examples: 78534 download_size: 222922572 dataset_size: 1726078566 - config_name: fm+fc+c+m+f features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 1476073209 num_examples: 624022 - name: test num_bytes: 201686811 num_examples: 78388 - name: validation num_bytes: 201259950 num_examples: 78534 download_size: 240405885 dataset_size: 1879019970 - config_name: fm+fc+c+m+f+t+tc features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 2019918359 num_examples: 624022 - name: test num_bytes: 269021331 num_examples: 78388 - name: validation num_bytes: 272958781 num_examples: 78534 download_size: 371500476 dataset_size: 2561898471 - config_name: fm+fc+c+m+t+tc features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 1897682665 num_examples: 624022 - name: test num_bytes: 255053799 num_examples: 78388 - name: validation num_bytes: 256030595 num_examples: 78534 download_size: 360175965 dataset_size: 2408767059 - config_name: fm+fc+c+t+tc features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 1109827485 num_examples: 624022 - name: test num_bytes: 142558255 num_examples: 78388 - name: validation num_bytes: 144523616 num_examples: 78534 download_size: 251861137 dataset_size: 1396909356 - config_name: fm+fc+t+tc features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 1046592848 num_examples: 624022 - name: test num_bytes: 135403379 num_examples: 78388 - name: validation num_bytes: 136729952 num_examples: 78534 download_size: 243052074 dataset_size: 1318726179 - config_name: fm+t features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 868034154 num_examples: 624022 - name: test num_bytes: 114371187 num_examples: 78388 - name: validation num_bytes: 112688219 num_examples: 78534 download_size: 217267853 dataset_size: 1095093560 - config_name: fm_indented features: - name: id dtype: string - name: text dtype: string splits: - name: train num_bytes: 473170158 num_examples: 624022 - name: test num_bytes: 64280367 num_examples: 78388 - name: validation num_bytes: 61093848 num_examples: 78534 download_size: 103174190 dataset_size: 598544373 - config_name: t features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 380207303 num_examples: 624022 - name: test num_bytes: 47993188 num_examples: 78388 - name: validation num_bytes: 49808813 num_examples: 78534 download_size: 113820250 dataset_size: 478009304 - config_name: t+tc features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 550955294 num_examples: 624022 - name: test num_bytes: 68323462 num_examples: 78388 - name: validation num_bytes: 72740770 num_examples: 78534 download_size: 136767271 dataset_size: 692019526 - config_name: t_indented features: - name: id dtype: string - name: source dtype: string - name: target dtype: string splits: - name: train num_bytes: 405853738 num_examples: 624022 - name: test num_bytes: 51457514 num_examples: 78388 - name: validation num_bytes: 52970428 num_examples: 78534 download_size: 117732776 dataset_size: 510281680 tags: - unit test - java - code --- ## Dataset Description Microsoft created the methods2test dataset, consisting of Java Junit test cases with its corresponding focal methods. It contains 780k pairs of JUnit test cases and focal methods which were extracted from a total of 91K Java open source project hosted on GitHub. This is an assembled version of the methods2test dataset. It provides convenient access to the different context levels based on the raw source code (e.g. newlines are preserved). The test cases and associated classes are also made available. The mapping between test case and focal methods are based heuristics rules and Java developer's best practice. More information could be found here: - [methods2test Github repo](https://github.com/microsoft/methods2test) - [Methods2Test: A dataset of focal methods mapped to test cases](https://arxiv.org/pdf/2203.12776.pdf) ## Dataset Schema ``` t: <TEST_CASE> t_tc: <TEST_CASE> <TEST_CLASS_NAME> fm: <FOCAL_METHOD> fm_fc: <FOCAL_CLASS_NAME> <FOCAL_METHOD> fm_fc_c: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> fm_fc_c_m: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> fm_fc_c_m_f: <FOCAL_CLASS_NAME> <FOCAL_METHOD> <CONTRSUCTORS> <METHOD_SIGNATURES> <FIELDS> ``` ## Focal Context - fm: this representation incorporates exclusively the source code of the focal method. Intuitively, this contains the most important information for generating accurate test cases for the given method. - fm+fc: this representations adds the focal class name, which can provide meaningful semantic information to the model. - fm+fc+c: this representation adds the signatures of the constructor methods of the focal class. The idea behind this augmentation is that the test case may require instantiating an object of the focal class in order to properly test the focal method. - fm+fc+c+m: this representation adds the signatures of the other public methods in the focal class. The rationale which motivated this inclusion is that the test case may need to invoke other auxiliary methods within the class (e.g., getters, setters) to set up or tear down the testing environment. - fm+fc+c+m+f : this representation adds the public fields of the focal class. The motivation is that test cases may need to inspect the status of the public fields to properly test a focal method.  The different levels of focal contexts are the following: ``` T: test case T_TC: test case + test class name FM: focal method FM_FC: focal method + focal class name FM_FC_C: focal method + focal class name + constructor signatures FM_FC_C_M: focal method + focal class name + constructor signatures + public method signatures FM_FC_C_M_F: focal method + focal class name + constructor signatures + public method signatures + public fields ``` ## Limitations The original authors validate the heuristics by inspecting a statistically significant sample (confidence level of 95% within 10% margin of error) of 97 samples from the training set. Two authors independently evaluated the sample, then met to discuss the disagreements. We found that 90.72% of the samples have a correct link between the test case and the corresponding focal method ## Contribution All thanks to the original authors.

japanese-asr/whisper_transcriptions.mls.wer_10.0

japanese-asr

--- dataset_info: - config_name: subset_0 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29741913577.241814 num_examples: 62101 download_size: 28406057868 dataset_size: 29741913577.241814 - config_name: subset_1 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29815585138.73427 num_examples: 62323 download_size: 28488972470 dataset_size: 29815585138.73427 - config_name: subset_10 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29831443458.675167 num_examples: 62172 download_size: 28490041949 dataset_size: 29831443458.675167 - config_name: subset_100 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29740102232.58974 num_examples: 62114 download_size: 28402573685 dataset_size: 29740102232.58974 - config_name: subset_101 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29804666990.485275 num_examples: 62225 download_size: 28477636147 dataset_size: 29804666990.485275 - config_name: subset_102 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29847859656.366245 num_examples: 62219 download_size: 28508104461 dataset_size: 29847859656.366245 - config_name: subset_103 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29776271336.13424 num_examples: 62248 download_size: 28453790146 dataset_size: 29776271336.13424 - config_name: subset_104 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29864954995.718533 num_examples: 62348 download_size: 28540369174 dataset_size: 29864954995.718533 - config_name: subset_105 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29845768222.852547 num_examples: 62287 download_size: 28508203679 dataset_size: 29845768222.852547 - config_name: subset_106 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29868915195.73696 num_examples: 62355 download_size: 28531446961 dataset_size: 29868915195.73696 - config_name: subset_107 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29833296511.762436 num_examples: 62252 download_size: 28502966117 dataset_size: 29833296511.762436 - config_name: subset_108 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29846554379.21017 num_examples: 62398 download_size: 28521313998 dataset_size: 29846554379.21017 - config_name: subset_109 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29808600165.9863 num_examples: 62240 download_size: 28473663596 dataset_size: 29808600165.9863 - config_name: subset_11 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29807895865.53131 num_examples: 62230 download_size: 28470625940 dataset_size: 29807895865.53131 - 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name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29837744714.346436 num_examples: 62233 download_size: 28501403452 dataset_size: 29837744714.346436 - config_name: subset_94 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29839780119.8114 num_examples: 62323 download_size: 28513940315 dataset_size: 29839780119.8114 - config_name: subset_95 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29748188964.62823 num_examples: 62172 download_size: 28413924658 dataset_size: 29748188964.62823 - config_name: subset_96 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29800727262.69699 num_examples: 62260 download_size: 28475125160 dataset_size: 29800727262.69699 - config_name: subset_97 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29767717411.338116 num_examples: 62148 download_size: 28440311229 dataset_size: 29767717411.338116 - config_name: subset_98 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 4303888.0 num_examples: 9 download_size: 4144170 dataset_size: 4303888.0 - config_name: subset_99 features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription sequence: int64 - name: transcription/ja_gpt3.5 sequence: int64 - name: whisper_transcription sequence: int64 - name: whisper_transcription/ja_gpt3.5 sequence: int64 - name: input_length dtype: int64 splits: - name: train num_bytes: 29787160440.723938 num_examples: 62157 download_size: 28445447346 dataset_size: 29787160440.723938 configs: - config_name: subset_0 data_files: - 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