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--- pretty_name: OpenSLR annotations_creators: - found language_creators: - found language: - af - bn - ca - en - es - eu - gl - gu - jv - km - kn - ml - mr - my - ne - si - st - su - ta - te - tn - ve - xh - yo language_bcp47: - en-GB - en-IE - en-NG - es-CL - es-CO - es-PE - es-PR license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - automatic-speech-recognition task_ids: [] paperswithcode_id: null dataset_info: - config_name: SLR41 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2423902 num_examples: 5822 download_size: 1890792360 dataset_size: 2423902 - config_name: SLR42 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1427984 num_examples: 2906 download_size: 866086951 dataset_size: 1427984 - config_name: SLR43 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1074005 num_examples: 2064 download_size: 800375645 dataset_size: 1074005 - config_name: SLR44 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1776827 num_examples: 4213 download_size: 1472252752 dataset_size: 1776827 - config_name: SLR63 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2016587 num_examples: 4126 download_size: 1345876299 dataset_size: 2016587 - config_name: SLR64 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 810375 num_examples: 1569 download_size: 712155683 dataset_size: 810375 - config_name: SLR65 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2136447 num_examples: 4284 download_size: 1373304655 dataset_size: 2136447 - config_name: SLR66 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1898335 num_examples: 4448 download_size: 1035127870 dataset_size: 1898335 - config_name: SLR69 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1647263 num_examples: 4240 download_size: 1848659543 dataset_size: 1647263 - config_name: SLR35 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 73565374 num_examples: 185076 download_size: 18900105726 dataset_size: 73565374 - config_name: SLR36 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 88942337 num_examples: 219156 download_size: 22996553929 dataset_size: 88942337 - config_name: SLR70 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1339608 num_examples: 3359 download_size: 1213955196 dataset_size: 1339608 - config_name: SLR71 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1676273 num_examples: 4374 download_size: 1445365903 dataset_size: 1676273 - config_name: SLR72 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1876301 num_examples: 4903 download_size: 1612030532 dataset_size: 1876301 - config_name: SLR73 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2084052 num_examples: 5447 download_size: 1940306814 dataset_size: 2084052 - config_name: SLR74 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 237395 num_examples: 617 download_size: 214181314 dataset_size: 237395 - config_name: SLR75 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1286937 num_examples: 3357 download_size: 1043317004 dataset_size: 1286937 - config_name: SLR76 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2756507 num_examples: 7136 download_size: 3041125513 dataset_size: 2756507 - config_name: SLR77 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2217652 num_examples: 5587 download_size: 2207991775 dataset_size: 2217652 - config_name: SLR78 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2121986 num_examples: 4272 download_size: 1743222102 dataset_size: 2121986 - config_name: SLR79 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 2176539 num_examples: 4400 download_size: 1820919115 dataset_size: 2176539 - config_name: SLR80 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1308651 num_examples: 2530 download_size: 948181015 dataset_size: 1308651 - config_name: SLR86 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 1378801 num_examples: 3583 download_size: 907065562 dataset_size: 1378801 - config_name: SLR32 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 4544052380 num_examples: 9821 download_size: 3312884763 dataset_size: 4544052380 - config_name: SLR52 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 77369899 num_examples: 185293 download_size: 14676484074 dataset_size: 77369899 - config_name: SLR53 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 88073248 num_examples: 218703 download_size: 14630810921 dataset_size: 88073248 - config_name: SLR54 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 62735822 num_examples: 157905 download_size: 9328247362 dataset_size: 62735822 - config_name: SLR83 features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string splits: - name: train num_bytes: 7098985 num_examples: 17877 download_size: 7229890819 dataset_size: 7098985 config_names: - SLR32 - SLR35 - SLR36 - SLR41 - SLR42 - SLR43 - SLR44 - SLR52 - SLR53 - SLR54 - SLR63 - SLR64 - SLR65 - SLR66 - SLR69 - SLR70 - SLR71 - SLR72 - SLR73 - SLR74 - SLR75 - SLR76 - SLR77 - SLR78 - SLR79 - SLR80 - SLR83 - SLR86 --- # Dataset Card for openslr ## 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.openslr.org/ - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary OpenSLR is a site devoted to hosting speech and language resources, such as training corpora for speech recognition, and software related to speech recognition. Currently, following resources are available: #### SLR32: High quality TTS data for four South African languages (af, st, tn, xh). This data set contains multi-speaker high quality transcribed audio data for four languages of South Africa. The data set consists of wave files, and a TSV file transcribing the audio. In each folder, the file line_index.tsv contains a FileID, which in turn contains the UserID and the Transcription of audio in the file. The data set has had some quality checks, but there might still be errors. This data set was collected by as a collaboration between North West University and Google. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See https://github.com/google/language-resources#license for license information. Copyright 2017 Google, Inc. #### SLR35: Large Javanese ASR training data set. This data set contains transcribed audio data for Javanese (~185K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/35/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017 Google, Inc. #### SLR36: Large Sundanese ASR training data set. This data set contains transcribed audio data for Sundanese (~220K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/36/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017 Google, Inc. #### SLR41: High quality TTS data for Javanese. This data set contains high-quality transcribed audio data for Javanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Gadjah Mada University in Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/41/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR42: High quality TTS data for Khmer. This data set contains high-quality transcribed audio data for Khmer. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/42/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR43: High quality TTS data for Nepali. This data set contains high-quality transcribed audio data for Nepali. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in Nepal. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/43/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR44: High quality TTS data for Sundanese. This data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The data set has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Universitas Pendidikan Indonesia. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/44/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google LLC #### SLR52: Large Sinhala ASR training data set. This data set contains transcribed audio data for Sinhala (~185K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/52/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR53: Large Bengali ASR training data set. This data set contains transcribed audio data for Bengali (~196K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/53/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR54: Large Nepali ASR training data set. This data set contains transcribed audio data for Nepali (~157K utterances). The data set consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/54/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2016, 2017, 2018 Google, Inc. #### SLR63: Crowdsourced high-quality Malayalam multi-speaker speech data set This data set contains transcribed high-quality audio of Malayalam sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/63/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR64: Crowdsourced high-quality Marathi multi-speaker speech data set This data set contains transcribed high-quality audio of Marathi sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/64/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR65: Crowdsourced high-quality Tamil multi-speaker speech data set This data set contains transcribed high-quality audio of Tamil sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/65/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR66: Crowdsourced high-quality Telugu multi-speaker speech data set This data set contains transcribed high-quality audio of Telugu sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/66/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR69: Crowdsourced high-quality Catalan multi-speaker speech data set This data set contains transcribed high-quality audio of Catalan sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/69/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR70: Crowdsourced high-quality Nigerian English speech data set This data set contains transcribed high-quality audio of Nigerian English sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/70/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR71: Crowdsourced high-quality Chilean Spanish speech data set This data set contains transcribed high-quality audio of Chilean Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/71/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR72: Crowdsourced high-quality Colombian Spanish speech data set This data set contains transcribed high-quality audio of Colombian Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/72/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR73: Crowdsourced high-quality Peruvian Spanish speech data set This data set contains transcribed high-quality audio of Peruvian Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/73/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR74: Crowdsourced high-quality Puerto Rico Spanish speech data set This data set contains transcribed high-quality audio of Puerto Rico Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/74/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR75: Crowdsourced high-quality Venezuelan Spanish speech data set This data set contains transcribed high-quality audio of Venezuelan Spanish sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/75/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR76: Crowdsourced high-quality Basque speech data set This data set contains transcribed high-quality audio of Basque sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/76/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR77: Crowdsourced high-quality Galician speech data set This data set contains transcribed high-quality audio of Galician sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/77/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR78: Crowdsourced high-quality Gujarati multi-speaker speech data set This data set contains transcribed high-quality audio of Gujarati sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/78/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR79: Crowdsourced high-quality Kannada multi-speaker speech data set This data set contains transcribed high-quality audio of Kannada sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/79/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR80: Crowdsourced high-quality Burmese speech data set This data set contains transcribed high-quality audio of Burmese sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/80/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR83: Crowdsourced high-quality UK and Ireland English Dialect speech data set This data set contains transcribed high-quality audio of English sentences recorded by volunteers speaking different dialects of the language. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.csv contains a line id, an anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. The recordings from the Welsh English speakers were collected in collaboration with Cardiff University. The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/83/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019 Google, Inc. #### SLR86: Crowdsourced high-quality multi-speaker speech data set This data set contains transcribed high-quality audio of sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/86/LICENSE) file and https://github.com/google/language-resources#license for license information. Copyright 2018, 2019, 2020 Google, Inc. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages Javanese, Khmer, Nepali, Sundanese, Malayalam, Marathi, Tamil, Telugu, Catalan, Nigerian English, Chilean Spanish, Columbian Spanish, Peruvian Spanish, Puerto Rico Spanish, Venezuelan Spanish, Basque, Galician, Gujarati, Kannada, Afrikaans, Sesotho, Setswana and isiXhosa. ## Dataset Structure ### Data Instances A typical data point comprises the path to the audio file, called path and its sentence. #### SLR32, SLR35, SLR36, SLR41, SLR42, SLR43, SLR44, SLR52, SLR53, SLR54, SLR63, SLR64, SLR65, SLR66, SLR69, SLR70, SLR71, SLR72, SLR73, SLR74, SLR75, SLR76, SLR77, SLR78, SLR79, SLR80, SLR86 ``` { 'path': '/home/cahya/.cache/huggingface/datasets/downloads/extracted/4d9cf915efc21110199074da4d492566dee6097068b07a680f670fcec9176e62/su_id_female/wavs/suf_00297_00037352660.wav' 'audio': {'path': '/home/cahya/.cache/huggingface/datasets/downloads/extracted/4d9cf915efc21110199074da4d492566dee6097068b07a680f670fcec9176e62/su_id_female/wavs/suf_00297_00037352660.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'sentence': 'Panonton ting haruleng ningali Kelly Clarkson keur nyanyi di tipi', } ``` ### Data Fields - `path`: The path to the audio file. - `audio`: 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`: The sentence the user was prompted to speak. ### Data Splits There is only one "train" split for all configurations and the number of examples are: | | Number of examples | |:------|---------------------:| | SLR41 | 5822 | | SLR42 | 2906 | | SLR43 | 2064 | | SLR44 | 4213 | | SLR63 | 4126 | | SLR64 | 1569 | | SLR65 | 4284 | | SLR66 | 4448 | | SLR69 | 4240 | | SLR35 | 185076 | | SLR36 | 219156 | | SLR70 | 3359 | | SLR71 | 4374 | | SLR72 | 4903 | | SLR73 | 5447 | | SLR74 | 617 | | SLR75 | 3357 | | SLR76 | 7136 | | SLR77 | 5587 | | SLR78 | 4272 | | SLR79 | 4400 | | SLR80 | 2530 | | SLR86 | 3583 | | SLR32 | 9821 | | SLR52 | 185293 | | SLR53 | 218703 | | SLR54 | 157905 | | SLR83 | 17877 | ## 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 [Needs More Information] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Each dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License ([CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)). See https://github.com/google/language-resources#license or the resource page on [OpenSLR](https://openslr.org/resources.php) for more information. ### Citation Information #### SLR32 ``` @inproceedings{van-niekerk-etal-2017, title = {{Rapid development of TTS corpora for four South African languages}}, author = {Daniel van Niekerk and Charl van Heerden and Marelie Davel and Neil Kleynhans and Oddur Kjartansson and Martin Jansche and Linne Ha}, booktitle = {Proc. Interspeech 2017}, pages = {2178--2182}, address = {Stockholm, Sweden}, month = aug, year = {2017}, URL = {https://dx.doi.org/10.21437/Interspeech.2017-1139} } ``` #### SLR35, SLR36, SLR52, SLR53, SLR54 ``` @inproceedings{kjartansson-etal-sltu2018, title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali}}, author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha}, booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, year = {2018}, address = {Gurugram, India}, month = aug, pages = {52--55}, URL = {https://dx.doi.org/10.21437/SLTU.2018-11}, } ``` #### SLR41, SLR42, SLR43, SLR44 ``` @inproceedings{kjartansson-etal-tts-sltu2018, title = {{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Framework for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}}, author = {Keshan Sodimana and Knot Pipatsrisawat and Linne Ha and Martin Jansche and Oddur Kjartansson and Pasindu De Silva and Supheakmungkol Sarin}, booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)}, year = {2018}, address = {Gurugram, India}, month = aug, pages = {66--70}, URL = {https://dx.doi.org/10.21437/SLTU.2018-14} } ``` #### SLR63, SLR64, SLR65, SLR66, SLR78, SLR79 ``` @inproceedings{he-etal-2020-open, title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}}, author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, month = may, year = {2020}, address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, pages = {6494--6503}, url = {https://www.aclweb.org/anthology/2020.lrec-1.800}, ISBN = "{979-10-95546-34-4}, } ``` #### SLR69, SLR76, SLR77 ``` @inproceedings{kjartansson-etal-2020-open, title = {{Open-Source High Quality Speech Datasets for Basque, Catalan and Galician}}, author = {Kjartansson, Oddur and Gutkin, Alexander and Butryna, Alena and Demirsahin, Isin and Rivera, Clara}, booktitle = {Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)}, year = {2020}, pages = {21--27}, month = may, address = {Marseille, France}, publisher = {European Language Resources association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.sltu-1.3}, ISBN = {979-10-95546-35-1}, } ``` #### SLR70, SLR71, SLR72, SLR73, SLR74, SLR75 ``` @inproceedings{guevara-rukoz-etal-2020-crowdsourcing, title = {{Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech}}, author = {Guevara-Rukoz, Adriana and Demirsahin, Isin and He, Fei and Chu, Shan-Hui Cathy and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Gutkin, Alexander and Butryna, Alena and Kjartansson, Oddur}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, year = {2020}, month = may, address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.lrec-1.801}, pages = {6504--6513}, ISBN = {979-10-95546-34-4}, } ``` #### SLR80 ``` @inproceedings{oo-etal-2020-burmese, title = {{Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech}}, author = {Oo, Yin May and Wattanavekin, Theeraphol and Li, Chenfang and De Silva, Pasindu and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Jansche, Martin and Kjartansson, Oddur and Gutkin, Alexander}, booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)}, month = may, year = {2020}, pages = "6328--6339", address = {Marseille, France}, publisher = {European Language Resources Association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.lrec-1.777}, ISBN = {979-10-95546-34-4}, } ``` #### SLR86 ``` @inproceedings{gutkin-et-al-yoruba2020, title = {{Developing an Open-Source Corpus of Yoruba Speech}}, author = {Alexander Gutkin and I{\c{s}}{\i}n Demir{\c{s}}ahin and Oddur Kjartansson and Clara Rivera and K\d{\'o}lá Túb\d{\`o}sún}, booktitle = {Proceedings of Interspeech 2020}, pages = {404--408}, month = {October}, year = {2020}, address = {Shanghai, China}, publisher = {International Speech and Communication Association (ISCA)}, doi = {10.21437/Interspeech.2020-1096}, url = {https://dx.doi.org/10.21437/Interspeech.2020-1096}, } ``` ### Contributions Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.

Dahoas/full-hh-rlhf

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: 203150123 num_examples: 112052 - name: test num_bytes: 22606646 num_examples: 12451 download_size: 136150742 dataset_size: 225756769 --- # Dataset Card for "full-hh-rlhf" Anthropic's HH dataset reformatted into prompt, chosen, rejected samples.

superb

--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual size_categories: - unknown source_datasets: - original - extended|librispeech_asr - extended|other-librimix - extended|other-speech_commands task_categories: - automatic-speech-recognition - audio-classification task_ids: - keyword-spotting - speaker-identification - audio-intent-classification - audio-emotion-recognition pretty_name: SUPERB tags: - query-by-example-spoken-term-detection - audio-slot-filling - speaker-diarization - automatic-speaker-verification dataset_info: - config_name: asr features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string splits: - name: train num_bytes: 11852430 num_examples: 28539 - name: validation num_bytes: 897213 num_examples: 2703 - name: test num_bytes: 871234 num_examples: 2620 download_size: 7071899769 dataset_size: 13620877 - config_name: sd features: - name: record_id dtype: string - name: file dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: speakers list: - name: speaker_id dtype: string - name: start dtype: int64 - name: end dtype: int64 splits: - name: train num_bytes: 4622013 num_examples: 13901 - name: dev num_bytes: 860472 num_examples: 3014 - name: test num_bytes: 847803 num_examples: 3002 download_size: 7190370211 dataset_size: 6330288 - config_name: ks features: - name: file dtype: string - name: label dtype: class_label: names: '0': 'yes' '1': 'no' '2': up '3': down '4': left '5': right '6': 'on' '7': 'off' '8': stop '9': go '10': _silence_ '11': _unknown_ splits: - name: train num_bytes: 8467781 num_examples: 51094 - name: validation num_bytes: 1126476 num_examples: 6798 - name: test num_bytes: 510619 num_examples: 3081 download_size: 1560367713 dataset_size: 10104876 - config_name: ic features: - name: file dtype: string - name: speaker_id dtype: string - name: text dtype: string - name: action dtype: class_label: names: '0': activate '1': bring '2': change language '3': deactivate '4': decrease '5': increase - name: object dtype: class_label: names: '0': Chinese '1': English '2': German '3': Korean '4': heat '5': juice '6': lamp '7': lights '8': music '9': newspaper '10': none '11': shoes '12': socks '13': volume - name: location dtype: class_label: names: '0': bedroom '1': kitchen '2': none '3': washroom splits: - name: train num_bytes: 7071466 num_examples: 23132 - name: validation num_bytes: 953622 num_examples: 3118 - name: test num_bytes: 1158347 num_examples: 3793 download_size: 1544093324 dataset_size: 9183435 - config_name: si features: - name: file dtype: string - name: label dtype: class_label: names: '0': id10001 '1': id10002 '2': id10003 '3': id10004 '4': id10005 '5': id10006 '6': id10007 '7': id10008 '8': id10009 '9': id10010 '10': id10011 '11': id10012 '12': id10013 '13': id10014 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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 - **Homepage:** [http://superbbenchmark.org](http://superbbenchmark.org) - **Repository:** [https://github.com/s3prl/s3prl](https://github.com/s3prl/s3prl) - **Paper:** [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) - **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [Lewis Tunstall](mailto:[email protected]) and [Albert Villanova](mailto:[email protected]) ### Dataset Summary SUPERB is a leaderboard to benchmark the performance of a shared model across a wide range of speech processing tasks with minimal architecture changes and labeled data. ### Supported Tasks and Leaderboards The SUPERB leaderboard can be found here https://superbbenchmark.org/leaderboard and consists of the following tasks: #### pr Phoneme Recognition (PR) transcribes an utterance into the smallest content units. This task includes alignment modeling to avoid potentially inaccurate forced alignment. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/dev-clean/test-clean subsets are adopted in SUPERB for training/validation/testing. Phoneme transcriptions are obtained from the LibriSpeech official g2p-model-5 and the conversion script in Kaldi librispeech s5 recipe. The evaluation metric is phone error rate (PER). #### asr Automatic Speech Recognition (ASR) transcribes utterances into words. While PR analyzes the improvement in modeling phonetics, ASR reflects the significance of the improvement in a real-world scenario. [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) train-clean-100/devclean/test-clean subsets are used for training/validation/testing. The evaluation metric is word error rate (WER). #### ks Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used [Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. The evaluation metric is accuracy (ACC) ##### Example of usage: Use these auxillary functions to: - load the audio file into an audio data array - sample from long `_silence_` audio clips For other examples of handling long `_silence_` clips see the [S3PRL](https://github.com/s3prl/s3prl/blob/099ce807a6ffa6bf2482ceecfcaf83dea23da355/s3prl/downstream/speech_commands/dataset.py#L80) or [TFDS](https://github.com/tensorflow/datasets/blob/6b8cfdb7c3c0a04e731caaa8660ce948d0a67b1e/tensorflow_datasets/audio/speech_commands.py#L143) implementations. ```python def map_to_array(example): import soundfile as sf speech_array, sample_rate = sf.read(example["file"]) example["speech"] = speech_array example["sample_rate"] = sample_rate return example def sample_noise(example): # Use this function to extract random 1 sec slices of each _silence_ utterance, # e.g. inside `torch.utils.data.Dataset.__getitem__()` from random import randint if example["label"] == "_silence_": random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1) example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]] return example ``` #### qbe Query by Example Spoken Term Detection (QbE) detects a spoken term (query) in an audio database (documents) by binary discriminating a given pair of query and document into a match or not. The English subset in [QUESST 2014 challenge](https://github.com/s3prl/s3prl/tree/master/downstream#qbe-query-by-example-spoken-term-detection) is adopted since we focus on investigating English as the first step. The evaluation metric is maximum term weighted value (MTWV) which balances misses and false alarms. #### ic Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. SUPERB uses the [Fluent Speech Commands dataset](https://github.com/s3prl/s3prl/tree/master/downstream#ic-intent-classification---fluent-speech-commands), where each utterance is tagged with three intent labels: action, object, and location. The evaluation metric is accuracy (ACC). #### sf Slot Filling (SF) predicts a sequence of semantic slot-types from an utterance, like a slot-type FromLocation for a spoken word Taipei, which is known as a slot-value. Both slot-types and slot-values are essential for an SLU system to function. The evaluation metrics thus include slot-type F1 score and slotvalue CER. [Audio SNIPS](https://github.com/s3prl/s3prl/tree/master/downstream#sf-end-to-end-slot-filling) is adopted, which synthesized multi-speaker utterances for SNIPS. Following the standard split in SNIPS, US-accent speakers are further selected for training, and others are for validation/testing. #### si Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class classification, where speakers are in the same predefined set for both training and testing. The widely used [VoxCeleb1 dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) is adopted, and the evaluation metric is accuracy (ACC). #### asv Automatic Speaker Verification (ASV) verifies whether the speakers of a pair of utterances match as a binary classification, and speakers in the testing set may not appear in the training set. Thus, ASV is more challenging than SID. VoxCeleb1 is used without VoxCeleb2 training data and noise augmentation. The evaluation metric is equal error rate (EER). #### sd Speaker Diarization (SD) predicts *who is speaking when* for each timestamp, and multiple speakers can speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be able to represent mixtures of signals. [LibriMix](https://github.com/s3prl/s3prl/tree/master/downstream#sd-speaker-diarization) is adopted where LibriSpeech train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing. We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER). ##### Example of usage Use these auxiliary functions to: - load the audio file into an audio data array - generate the label array ```python def load_audio_file(example, frame_shift=160): import soundfile as sf example["array"], example["sample_rate"] = sf.read( example["file"], start=example["start"] * frame_shift, stop=example["end"] * frame_shift ) return example def generate_label(example, frame_shift=160, num_speakers=2, rate=16000): import numpy as np start = example["start"] end = example["end"] frame_num = end - start speakers = sorted({speaker["speaker_id"] for speaker in example["speakers"]}) label = np.zeros((frame_num, num_speakers), dtype=np.int32) for speaker in example["speakers"]: speaker_index = speakers.index(speaker["speaker_id"]) start_frame = np.rint(speaker["start"] * rate / frame_shift).astype(int) end_frame = np.rint(speaker["end"] * rate / frame_shift).astype(int) rel_start = rel_end = None if start <= start_frame < end: rel_start = start_frame - start if start < end_frame <= end: rel_end = end_frame - start if rel_start is not None or rel_end is not None: label[rel_start:rel_end, speaker_index] = 1 example["label"] = label return example ``` #### er Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset [IEMOCAP](https://github.com/s3prl/s3prl/tree/master/downstream#er-emotion-recognition) is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion classes to leave the final four classes with a similar amount of data points and cross-validates on five folds of the standard splits. The evaluation metric is accuracy (ACC). ### Languages The language data in SUPERB is in English (BCP-47 `en`) ## Dataset Structure ### Data Instances #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr An example from each split looks like: ```python {'chapter_id': 1240, 'file': 'path/to/file.flac', 'audio': {'path': 'path/to/file.flac', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'id': '103-1240-0000', 'speaker_id': 103, 'text': 'CHAPTER ONE MISSUS RACHEL LYNDE IS SURPRISED MISSUS RACHEL LYNDE ' 'LIVED JUST WHERE THE AVONLEA MAIN ROAD DIPPED DOWN INTO A LITTLE ' 'HOLLOW FRINGED WITH ALDERS AND LADIES EARDROPS AND TRAVERSED BY A ' 'BROOK'} ``` #### ks An example from each split looks like: ```python { 'file': '/path/yes/af7a8296_nohash_1.wav', 'audio': {'path': '/path/yes/af7a8296_nohash_1.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'label': 0 # 'yes' } ``` #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic ```python { 'file': "/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav", 'audio': {'path': '/path/wavs/speakers/2BqVo8kVB2Skwgyb/063aa8f0-4479-11e9-a9a5-5dbec3b8816a.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'speaker_id': '2BqVo8kVB2Skwgyb', 'text': 'Turn the bedroom lights off', 'action': 3, # 'deactivate' 'object': 7, # 'lights' 'location': 0 # 'bedroom' } ``` #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si ```python { 'file': '/path/wav/id10003/na8-QEFmj44/00003.wav', 'audio': {'path': '/path/wav/id10003/na8-QEFmj44/00003.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'label': 2 # 'id10003' } ``` #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd An example from each split looks like: ```python { 'record_id': '1578-6379-0038_6415-111615-0009', 'file': 'path/to/file.wav', 'audio': {'path': 'path/to/file.wav', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 16000}, 'start': 0, 'end': 1590, 'speakers': [ {'speaker_id': '1578', 'start': 28, 'end': 657}, {'speaker_id': '6415', 'start': 28, 'end': 1576} ] } ``` #### er [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Data Fields ####Note abouth the `audio` fields 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]`. #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `text` (`string`): The transcription of the audio file. - `speaker_id` (`integer`): A unique ID of the speaker. The same speaker id can be found for multiple data samples. - `chapter_id` (`integer`): ID of the audiobook chapter which includes the transcription. - `id` (`string`): A unique ID of the data sample. #### ks - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label of the spoken command. Possible values: - `0: "yes", 1: "no", 2: "up", 3: "down", 4: "left", 5: "right", 6: "on", 7: "off", 8: "stop", 9: "go", 10: "_silence_", 11: "_unknown_"` #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `speaker_id` (`string`): ID of the speaker. - `text` (`string`): Transcription of the spoken command. - `action` (`ClassLabel`): Label of the command's action. Possible values: - `0: "activate", 1: "bring", 2: "change language", 3: "deactivate", 4: "decrease", 5: "increase"` - `object` (`ClassLabel`): Label of the command's object. Possible values: - `0: "Chinese", 1: "English", 2: "German", 3: "Korean", 4: "heat", 5: "juice", 6: "lamp", 7: "lights", 8: "music", 9: "newspaper", 10: "none", 11: "shoes", 12: "socks", 13: "volume"` - `location` (`ClassLabel`): Label of the command's location. Possible values: - `0: "bedroom", 1: "kitchen", 2: "none", 3: "washroom"` #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label (ID) of the speaker. Possible values: - `0: "id10001", 1: "id10002", 2: "id10003", ..., 1250: "id11251"` #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd The data fields in all splits are: - `record_id` (`string`): ID of the record. - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `start` (`integer`): Start frame of the audio. - `end` (`integer`): End frame of the audio. - `speakers` (`list` of `dict`): List of speakers in the audio. Each item contains the fields: - `speaker_id` (`string`): ID of the speaker. - `start` (`integer`): Frame when the speaker starts speaking. - `end` (`integer`): Frame when the speaker stops speaking. #### er - `file` (`string`): Path to the WAV audio file. - `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. - `label` (`ClassLabel`): Label of the speech emotion. Possible values: - `0: "neu", 1: "hap", 2: "ang", 3: "sad"` ### Data Splits #### pr [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### asr | | train | validation | test | |-----|------:|-----------:|-----:| | asr | 28539 | 2703 | 2620 | #### ks | | train | validation | test | |----|------:|-----------:|-----:| | ks | 51094 | 6798 | 3081 | #### qbe [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### ic | | train | validation | test | |----|------:|-----------:|-----:| | ic | 23132 | 3118 | 3793 | #### sf [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### si | | train | validation | test | |----|-------:|-----------:|-----:| | si | 138361 | 6904 | 8251 | #### asv [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### sd The data is split into "train", "dev" and "test" sets, each containing the following number of examples: | | train | dev | test | |----|------:|-----:|-----:| | sd | 13901 | 3014 | 3002 | #### er The data is split into 5 sets intended for 5-fold cross-validation: | | session1 | session2 | session3 | session4 | session5 | |----|---------:|---------:|---------:|---------:|---------:| | er | 1085 | 1023 | 1151 | 1031 | 1241 | ## 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 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 Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information #### pr and asr The license for Librispeech is the Creative Commons Attribution 4.0 International license ((CC-BY-4.0)[https://creativecommons.org/licenses/by/4.0/]). #### ks The license for Speech Commands is [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) #### qbe The license for QUESST 2014 is not known. #### ic The license for Fluent Speech Commands dataset is the [Fluent Speech Commands Public License](https://fluent.ai/wp-content/uploads/2021/04/Fluent_Speech_Commands_Public_License.pdf) #### sf The license for Audio SNIPS dataset is not known. #### si and asv The license for VoxCeleb1 dataset is the Creative Commons Attribution 4.0 International license ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)). #### sd LibriMix is based on the LibriSpeech (see above) and Wham! noises datasets. The Wham! noises dataset is distributed under the Attribution-NonCommercial 4.0 International ([CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)) license. #### er The IEMOCAP license is ditributed under [its own license](https://sail.usc.edu/iemocap/Data_Release_Form_IEMOCAP.pdf). ### Citation Information ``` @article{DBLP:journals/corr/abs-2105-01051, author = {Shu{-}Wen Yang and Po{-}Han Chi and Yung{-}Sung Chuang and Cheng{-}I Jeff Lai and Kushal Lakhotia and Yist Y. Lin and Andy T. Liu and Jiatong Shi and Xuankai Chang and Guan{-}Ting Lin and Tzu{-}Hsien Huang and Wei{-}Cheng Tseng and Ko{-}tik Lee and Da{-}Rong Liu and Zili Huang and Shuyan Dong and Shang{-}Wen Li and Shinji Watanabe and Abdelrahman Mohamed and Hung{-}yi Lee}, title = {{SUPERB:} Speech processing Universal PERformance Benchmark}, journal = {CoRR}, volume = {abs/2105.01051}, year = {2021}, url = {https://arxiv.org/abs/2105.01051}, archivePrefix = {arXiv}, eprint = {2105.01051}, timestamp = {Thu, 01 Jul 2021 13:30:22 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } Note that each SUPERB dataset has its own citation. Please see the source to see the correct citation for each contained dataset. ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova) and [@anton-l](https://github.com/anton-l) for adding this dataset.

clue

--- annotations_creators: - other language_creators: - other language: - zh license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification - multiple-choice task_ids: - topic-classification - semantic-similarity-scoring - natural-language-inference - multiple-choice-qa paperswithcode_id: clue pretty_name: 'CLUE: Chinese Language Understanding Evaluation benchmark' tags: - coreference-nli - qa-nli dataset_info: - config_name: afqmc features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' - name: idx dtype: int32 splits: - name: test num_bytes: 378726 num_examples: 3861 - name: train num_bytes: 3396535 num_examples: 34334 - name: validation num_bytes: 426293 num_examples: 4316 download_size: 1195044 dataset_size: 4201554 - config_name: tnews features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': '100' '1': '101' '2': '102' '3': '103' '4': '104' '5': '106' '6': '107' '7': '108' '8': '109' '9': '110' '10': '112' '11': '113' '12': '114' '13': '115' '14': '116' - name: idx dtype: int32 splits: - name: test num_bytes: 810974 num_examples: 10000 - name: train num_bytes: 4245701 num_examples: 53360 - name: validation num_bytes: 797926 num_examples: 10000 download_size: 5123575 dataset_size: 5854601 - config_name: iflytek features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' '7': '7' '8': '8' '9': '9' '10': '10' '11': '11' '12': '12' '13': '13' '14': '14' '15': '15' '16': '16' '17': '17' '18': '18' '19': '19' '20': '20' '21': '21' '22': '22' '23': '23' '24': '24' '25': '25' '26': '26' '27': '27' '28': '28' '29': '29' '30': '30' '31': '31' '32': '32' '33': '33' '34': '34' '35': '35' '36': '36' '37': '37' '38': '38' '39': '39' '40': '40' '41': '41' '42': '42' '43': '43' '44': '44' '45': '45' '46': '46' '47': '47' '48': '48' '49': '49' '50': '50' '51': '51' '52': '52' '53': '53' '54': '54' '55': '55' '56': '56' '57': '57' '58': '58' '59': '59' '60': '60' '61': '61' '62': '62' '63': '63' '64': '64' '65': '65' '66': '66' '67': '67' '68': '68' '69': '69' '70': '70' '71': '71' '72': '72' '73': '73' '74': '74' '75': '75' '76': '76' '77': '77' '78': '78' '79': '79' '80': '80' '81': '81' '82': '82' '83': '83' '84': '84' '85': '85' '86': '86' '87': '87' '88': '88' '89': '89' '90': '90' '91': '91' '92': '92' '93': '93' '94': '94' '95': '95' '96': '96' '97': '97' '98': '98' '99': '99' '100': '100' '101': '101' '102': '102' '103': '103' '104': '104' '105': '105' '106': '106' '107': '107' '108': '108' '109': '109' '110': '110' '111': '111' '112': '112' '113': '113' '114': '114' '115': '115' '116': '116' '117': '117' '118': '118' - name: idx dtype: int32 splits: - name: test num_bytes: 2105688 num_examples: 2600 - name: train num_bytes: 10028613 num_examples: 12133 - name: validation num_bytes: 2157123 num_examples: 2599 download_size: 6505938 dataset_size: 14291424 - config_name: cmnli features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': neutral '1': entailment '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 2386837 num_examples: 13880 - name: train num_bytes: 67685309 num_examples: 391783 - name: validation num_bytes: 2051845 num_examples: 12241 download_size: 31404066 dataset_size: 72123991 - config_name: cluewsc2020 features: - name: idx dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': 'true' '1': 'false' - name: target struct: - name: span1_text dtype: string - name: span2_text dtype: string - name: span1_index dtype: int32 - name: span2_index dtype: int32 splits: - name: test num_bytes: 645649 num_examples: 2574 - name: train num_bytes: 288828 num_examples: 1244 - name: validation num_bytes: 72682 num_examples: 304 download_size: 281384 dataset_size: 1007159 - config_name: csl features: - name: idx dtype: int32 - name: corpus_id dtype: int32 - name: abst dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' - name: keyword sequence: string splits: - name: test num_bytes: 2463740 num_examples: 3000 - name: train num_bytes: 16478914 num_examples: 20000 - name: validation num_bytes: 2464575 num_examples: 3000 download_size: 3234594 dataset_size: 21407229 - config_name: cmrc2018 features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: test num_bytes: 3112066 num_examples: 2000 - name: train num_bytes: 15508110 num_examples: 10142 - name: validation num_bytes: 5183809 num_examples: 3219 - name: trial num_bytes: 1606931 num_examples: 1002 download_size: 3405146 dataset_size: 25410916 - config_name: drcd features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: test num_bytes: 4982402 num_examples: 3493 - name: train num_bytes: 37443458 num_examples: 26936 - name: validation num_bytes: 5222753 num_examples: 3524 download_size: 7264200 dataset_size: 47648613 - config_name: chid features: - name: idx dtype: int32 - name: candidates sequence: string - name: content sequence: string - name: answers sequence: - name: text dtype: string - name: candidate_id dtype: int32 splits: - name: test num_bytes: 11480463 num_examples: 3447 - name: train num_bytes: 252478178 num_examples: 84709 - name: validation num_bytes: 10117789 num_examples: 3218 download_size: 139199202 dataset_size: 274076430 - config_name: c3 features: - name: id dtype: int32 - name: context sequence: string - name: question dtype: string - name: choice sequence: string - name: answer dtype: string splits: - name: test num_bytes: 1600166 num_examples: 1625 - name: train num_bytes: 9672787 num_examples: 11869 - name: validation num_bytes: 2990967 num_examples: 3816 download_size: 3495930 dataset_size: 14263920 - config_name: ocnli features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': neutral '1': entailment '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 376066 num_examples: 3000 - name: train num_bytes: 6187190 num_examples: 50437 - name: validation num_bytes: 366235 num_examples: 2950 download_size: 4359754 dataset_size: 6929491 - config_name: diagnostics features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': neutral '1': entailment '2': contradiction - name: idx dtype: int32 splits: - name: test num_bytes: 42400 num_examples: 514 download_size: 12062 dataset_size: 42400 --- # Dataset Card for "clue" ## 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.cluebenchmarks.com - **Repository:** https://github.com/CLUEbenchmark/CLUE - **Paper:** [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://aclanthology.org/2020.coling-main.419/) - **Point of Contact:** [Zhenzhong Lan](mailto:[email protected]) - **Size of downloaded dataset files:** 198.68 MB - **Size of the generated dataset:** 486.34 MB - **Total amount of disk used:** 685.02 MB ### Dataset Summary CLUE, A Chinese Language Understanding Evaluation Benchmark (https://www.cluebenchmarks.com/) is a collection of resources for training, evaluating, and analyzing Chinese language understanding 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 #### afqmc - **Size of downloaded dataset files:** 1.20 MB - **Size of the generated dataset:** 4.20 MB - **Total amount of disk used:** 5.40 MB An example of 'validation' looks as follows. ``` { "idx": 0, "label": 0, "sentence1": "双十一花呗提额在哪", "sentence2": "里可以提花呗额度" } ``` #### c3 - **Size of downloaded dataset files:** 3.20 MB - **Size of the generated dataset:** 15.69 MB - **Total amount of disk used:** 18.90 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answer": "比人的灵敏", "choice": ["没有人的灵敏", "和人的差不多", "和人的一样好", "比人的灵敏"], "context": "[\"许多动物的某些器官感觉特别灵敏，它们能比人类提前知道一些灾害事件的发生，例如，海洋中的水母能预报风暴，老鼠能事先躲避矿井崩塌或有害气体，等等。地震往往能使一些动物的某些感觉器官受到刺激而发生异常反应。如一个地区的重力发生变异，某些动物可能通过它们的平衡...", "id": 1, "question": "动物的器官感觉与人的相比有什么不同?" } ``` #### chid - **Size of downloaded dataset files:** 139.20 MB - **Size of the generated dataset:** 274.08 MB - **Total amount of disk used:** 413.28 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "answers": { "candidate_id": [3, 5, 6, 1, 7, 4, 0], "text": ["碌碌无为", "无所作为", "苦口婆心", "得过且过", "未雨绸缪", "软硬兼施", "传宗接代"] }, "candidates": "[\"传宗接代\", \"得过且过\", \"咄咄逼人\", \"碌碌无为\", \"软硬兼施\", \"无所作为\", \"苦口婆心\", \"未雨绸缪\", \"和衷共济\", \"人老珠黄\"]...", "content": "[\"谈到巴萨目前的成就，瓜迪奥拉用了“坚持”两个字来形容。自从上世纪90年代克鲁伊夫带队以来，巴萨就坚持每年都有拉玛西亚球员进入一队的传统。即便是范加尔时代，巴萨强力推出的“巴萨五鹰”德拉·佩纳、哈维、莫雷罗、罗杰·加西亚和贝拉乌桑几乎#idiom0000...", "idx": 0 } ``` #### cluewsc2020 - **Size of downloaded dataset files:** 0.28 MB - **Size of the generated dataset:** 1.03 MB - **Total amount of disk used:** 1.29 MB An example of 'train' looks as follows. ``` { "idx": 0, "label": 1, "target": { "span1_index": 3, "span1_text": "伤口", "span2_index": 27, "span2_text": "它们" }, "text": "裂开的伤口涂满尘土，里面有碎石子和木头刺，我小心翼翼把它们剔除出去。" } ``` #### cmnli - **Size of downloaded dataset files:** 31.40 MB - **Size of the generated dataset:** 72.12 MB - **Total amount of disk used:** 103.53 MB An example of 'train' looks as follows. ``` { "idx": 0, "label": 0, "sentence1": "从概念上讲，奶油略读有两个基本维度-产品和地理。", "sentence2": "产品和地理位置是使奶油撇油起作用的原因。" } ``` ### Data Fields The data fields are the same among all splits. #### afqmc - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `label`: a classification label, with possible values including `0` (0), `1` (1). - `idx`: a `int32` feature. #### c3 - `id`: a `int32` feature. - `context`: a `list` of `string` features. - `question`: a `string` feature. - `choice`: a `list` of `string` features. - `answer`: a `string` feature. #### chid - `idx`: a `int32` feature. - `candidates`: a `list` of `string` features. - `content`: a `list` of `string` features. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `candidate_id`: a `int32` feature. #### cluewsc2020 - `idx`: a `int32` feature. - `text`: a `string` feature. - `label`: a classification label, with possible values including `true` (0), `false` (1). - `span1_text`: a `string` feature. - `span2_text`: a `string` feature. - `span1_index`: a `int32` feature. - `span2_index`: a `int32` feature. #### cmnli - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `label`: a classification label, with possible values including `neutral` (0), `entailment` (1), `contradiction` (2). - `idx`: a `int32` feature. ### Data Splits | name |train |validation|test | |-----------|-----:|---------:|----:| |afqmc | 34334| 4316| 3861| |c3 | 11869| 3816| 3892| |chid | 84709| 3218| 3231| |cluewsc2020| 1244| 304| 290| |cmnli |391783| 12241|13880| ## 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{xu-etal-2020-clue, title = "{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark", author = "Xu, Liang and Hu, Hai and Zhang, Xuanwei and Li, Lu and Cao, Chenjie and Li, Yudong and Xu, Yechen and Sun, Kai and Yu, Dian and Yu, Cong and Tian, Yin and Dong, Qianqian and Liu, Weitang and Shi, Bo and Cui, Yiming and Li, Junyi and Zeng, Jun and Wang, Rongzhao and Xie, Weijian and Li, Yanting and Patterson, Yina and Tian, Zuoyu and Zhang, Yiwen and Zhou, He and Liu, Shaoweihua and Zhao, Zhe and Zhao, Qipeng and Yue, Cong and Zhang, Xinrui and Yang, Zhengliang and Richardson, Kyle and Lan, Zhenzhong", booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", month = dec, year = "2020", address = "Barcelona, Spain (Online)", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2020.coling-main.419", doi = "10.18653/v1/2020.coling-main.419", pages = "4762--4772", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@JetRunner](https://github.com/JetRunner) for adding this dataset.

mteb/sprintduplicatequestions-pairclassification

mteb

--- language: - en ---

tydiqa

--- pretty_name: TyDi QA annotations_creators: - crowdsourced language_creators: - crowdsourced language: - ar - bn - en - fi - id - ja - ko - ru - sw - te - th license: - apache-2.0 multilinguality: - multilingual size_categories: - unknown source_datasets: - extended|wikipedia task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: tydi-qa dataset_info: - config_name: primary_task features: - name: passage_answer_candidates sequence: - name: plaintext_start_byte dtype: int32 - name: plaintext_end_byte dtype: int32 - name: question_text dtype: string - name: document_title dtype: string - name: language dtype: string - name: annotations sequence: - name: passage_answer_candidate_index dtype: int32 - name: minimal_answers_start_byte dtype: int32 - name: minimal_answers_end_byte dtype: int32 - name: yes_no_answer dtype: string - name: document_plaintext dtype: string - name: document_url dtype: string splits: - name: train num_bytes: 5550574617 num_examples: 166916 - name: validation num_bytes: 484380443 num_examples: 18670 download_size: 1953887429 dataset_size: 6034955060 - config_name: secondary_task features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: train num_bytes: 52948607 num_examples: 49881 - name: validation num_bytes: 5006461 num_examples: 5077 download_size: 1953887429 dataset_size: 57955068 --- # Dataset Card for "tydiqa" ## 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://github.com/google-research-datasets/tydiqa](https://github.com/google-research-datasets/tydiqa) - **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:** 3.91 GB - **Size of the generated dataset:** 6.10 GB - **Total amount of disk used:** 10.00 GB ### Dataset Summary TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language expresses -- such that we expect models performing well on this set to generalize across a large number of the languages in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without the use of translation (unlike MLQA and XQuAD). ### 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 #### primary_task - **Size of downloaded dataset files:** 1.95 GB - **Size of the generated dataset:** 6.04 GB - **Total amount of disk used:** 7.99 GB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "annotations": { "minimal_answers_end_byte": [-1, -1, -1], "minimal_answers_start_byte": [-1, -1, -1], "passage_answer_candidate_index": [-1, -1, -1], "yes_no_answer": ["NONE", "NONE", "NONE"] }, "document_plaintext": "\"\\nรองศาสตราจารย์[1] หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร (22 กันยายน 2495 -) ผู้ว่าราชการกรุงเทพมหานครคนที่ 15 อดีตรองหัวหน้าพรรคปร...", "document_title": "หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร", "document_url": "\"https://th.wikipedia.org/wiki/%E0%B8%AB%E0%B8%A1%E0%B9%88%E0%B8%AD%E0%B8%A1%E0%B8%A3%E0%B8%B2%E0%B8%8A%E0%B8%A7%E0%B8%87%E0%B8%...", "language": "thai", "passage_answer_candidates": "{\"plaintext_end_byte\": [494, 1779, 2931, 3904, 4506, 5588, 6383, 7122, 8224, 9375, 10473, 12563, 15134, 17765, 19863, 21902, 229...", "question_text": "\"หม่อมราชวงศ์สุขุมพันธุ์ บริพัตร เรียนจบจากที่ไหน ?\"..." } ``` #### secondary_task - **Size of downloaded dataset files:** 1.95 GB - **Size of the generated dataset:** 58.03 MB - **Total amount of disk used:** 2.01 GB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [394], "text": ["بطولتين"] }, "context": "\"أقيمت البطولة 21 مرة، شارك في النهائيات 78 دولة، وعدد الفرق التي فازت بالبطولة حتى الآن 8 فرق، ويعد المنتخب البرازيلي الأكثر تت...", "id": "arabic-2387335860751143628-1", "question": "\"كم عدد مرات فوز الأوروغواي ببطولة كاس العالم لكرو القدم؟\"...", "title": "قائمة نهائيات كأس العالم" } ``` ### Data Fields The data fields are the same among all splits. #### primary_task - `passage_answer_candidates`: a dictionary feature containing: - `plaintext_start_byte`: a `int32` feature. - `plaintext_end_byte`: a `int32` feature. - `question_text`: a `string` feature. - `document_title`: a `string` feature. - `language`: a `string` feature. - `annotations`: a dictionary feature containing: - `passage_answer_candidate_index`: a `int32` feature. - `minimal_answers_start_byte`: a `int32` feature. - `minimal_answers_end_byte`: a `int32` feature. - `yes_no_answer`: a `string` feature. - `document_plaintext`: a `string` feature. - `document_url`: a `string` feature. #### secondary_task - `id`: a `string` feature. - `title`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | train | validation | | -------------- | -----: | ---------: | | primary_task | 166916 | 18670 | | secondary_task | 49881 | 5077 | ## 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 ``` @article{tydiqa, title = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages}, author = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki} year = {2020}, journal = {Transactions of the Association for Computational Linguistics} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

medmcqa

--- 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: 131904057 num_examples: 182822 - name: test num_bytes: 1447829 num_examples: 6150 - name: validation num_bytes: 2221468 num_examples: 4183 download_size: 55285460 dataset_size: 135573354 --- # 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.

yhavinga/ccmatrix

yhavinga

--- annotations_creators: - found language_creators: - found language: - af - am - ar - ast - az - be - bg - bn - br - ca - ceb - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - ha - he - hi - hr - hu - hy - id - ig - ilo - is - it - ja - jv - ka - kk - km - ko - la - lb - lg - lt - lv - mg - mk - ml - mr - ms - my - ne - nl - 'no' - oc - om - or - pl - pt - ro - ru - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - tl - tr - tt - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu - se license: - unknown multilinguality: - multilingual size_categories: en-nl: - n<110M en-af: - n<9M en-lt: - <24M source_datasets: - original task_categories: - text2text-generation - translation task_ids: [] paperswithcode_id: ccmatrix pretty_name: CCMatrixV1 tags: - conditional-text-generation --- # Dataset Card for CCMatrix v1 ## 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://opus.nlpl.eu/CCMatrix.php - **Repository:** None - **Paper:** https://arxiv.org/abs/1911.04944 ### Dataset Summary This corpus has been extracted from web crawls using the margin-based bitext mining techniques described at https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix. * 90 languages, 1,197 bitexts * total number of files: 90 * total number of tokens: 112.14G * total number of sentence fragments: 7.37G ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Configs are generated for all language pairs in both directions. You can find the valid pairs in Homepage section of Dataset Description: https://opus.nlpl.eu/CCMatrix.php E.g. ``` from datasets import load_dataset dataset = load_dataset("yhavinga/ccmatrix", "en-nl", streaming=True) ``` This will open the `en-nl` dataset in streaming mode. Without streaming, download and prepare will take tens of minutes. You can inspect elements with: ``` print(next(iter(dataset['train']))) {'id': 0, 'score': 1.2499677, 'translation': {'en': 'They come from all parts of Egypt, just like they will at the day of His coming.', 'nl': 'Zij kwamen uit alle delen van Egypte, evenals zij op de dag van Zijn komst zullen doen.'}} ``` ## Dataset Structure ### Data Instances For example: ```json { "id": 1, "score": 1.2498379, "translation": { "nl": "En we moeten elke waarheid vals noemen die niet minstens door een lach vergezeld ging.”", "en": "And we should call every truth false which was not accompanied by at least one laugh.”" } } ``` ### Data Fields Each example contains an integer id starting with 0, a score, and a translation dictionary with the language 1 and language 2 texts. ### Data Splits Only a `train` split is provided. ## 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? [More Information Needed] ### Annotations [More Information Needed] #### 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 [More Information Needed] ### Citation Information IMPORTANT: Please cite reference [2][3] if you use this data. 1. **[CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://arxiv.org/abs/1911.00359)** by *Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Jouli and Edouard Grave*. 2. **[CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB](https://arxiv.org/abs/1911.04944)** by *Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin*. 3. **[Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125)** by *Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, and Armand Joulin.* This HuggingFace CCMatrix dataset is a wrapper around the service and files prepared and hosted by OPUS: * **[Parallel Data, Tools and Interfaces in OPUS](https://www.aclweb.org/anthology/L12-1246/)** by *Jörg Tiedemann*. ### Contributions

juletxara/mgsm

juletxara

--- annotations_creators: - found language_creators: - found - expert-generated language: - en - es - fr - de - ru - zh - ja - th - sw - bn license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - extended|gsm8k task_categories: - text2text-generation task_ids: [] paperswithcode_id: multi-task-language-understanding-on-mgsm pretty_name: Multilingual Grade School Math Benchmark (MGSM) tags: - math-word-problems dataset_info: - config_name: en features: - name: question dtype: string - name: answer dtype: string - name: answer_number dtype: int32 - name: equation_solution dtype: string splits: - name: train num_bytes: 3963202 num_examples: 8 - name: test num_bytes: 713732 num_examples: 250 download_size: 4915944 dataset_size: 4676934 - config_name: es features: - name: question dtype: string - name: answer dtype: string - name: answer_number dtype: int32 - name: equation_solution dtype: string splits: - name: train num_bytes: 3963202 num_examples: 8 - name: test num_bytes: 713732 num_examples: 250 download_size: 4915944 dataset_size: 4676934 --- # Dataset Card for MGSM ## 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://openai.com/blog/grade-school-math/ - **Repository:** https://github.com/openai/grade-school-math - **Paper:** https://arxiv.org/abs/2110.14168 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed in the paper [Language models are multilingual chain-of-thought reasoners](http://arxiv.org/abs/2210.03057). The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are: - Spanish - French - German - Russian - Chinese - Japanese - Thai - Swahili - Bengali - Telugu GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. You can find the input and targets for each of the ten languages (and English) as `.tsv` files. We also include few-shot exemplars that are also manually translated from each language in `exemplars.py`. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are: - Spanish - French - German - Russian - Chinese - Japanese - Thai - Swahili - Bengali - Telugu ## Dataset Structure ### Data Instances Each instance in the train split contains: - a string for the grade-school level math question - a string for the corresponding answer with chain-of-thought steps. - the numeric solution to the question - the equation solution to the question ```python {'question': 'Question: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?', 'answer': 'Step-by-Step Answer: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.', 'answer_number': 11, 'equation_solution': '5 + 6 = 11.'} ``` Each instance in the test split contains: - a string for the grade-school level math question - the numeric solution to the question ```python {'question': "Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?", 'answer': None, 'answer_number': 18, 'equation_solution': None} ``` ### Data Fields The data fields are the same among `train` and `test` splits. - question: The question string to a grade school math problem. - answer: The full solution string to the `question`. It contains multiple steps of reasoning with calculator annotations and the final numeric solution. - answer_number: The numeric solution to the `question`. - equation_solution: The equation solution to the `question`. ### Data Splits - The train split includes 8 few-shot exemplars that are also manually translated from each language. - The test split includes the same 250 problems from GSM8K translated via human annotators in 10 languages. | name |train|test | |--------|----:|---------:| |en | 8 | 250 | |es | 8 | 250 | |fr | 8 | 250 | |de | 8 | 250 | |ru | 8 | 250 | |zh | 8 | 250 | |ja | 8 | 250 | |th | 8 | 250 | |sw | 8 | 250 | |bn | 8 | 250 | |te | 8 | 250 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization From the paper: > We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solu- tions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that con- tain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors. #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? Surge AI (surgehq.ai) ### 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 The GSM8K dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT). ### Citation Information ```bibtex @article{cobbe2021gsm8k, title={Training Verifiers to Solve Math Word Problems}, author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John}, journal={arXiv preprint arXiv:2110.14168}, year={2021} } @misc{shi2022language, title={Language Models are Multilingual Chain-of-Thought Reasoners}, author={Freda Shi and Mirac Suzgun and Markus Freitag and Xuezhi Wang and Suraj Srivats and Soroush Vosoughi and Hyung Won Chung and Yi Tay and Sebastian Ruder and Denny Zhou and Dipanjan Das and Jason Wei}, year={2022}, eprint={2210.03057}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@juletx](https://github.com/juletx) for adding this dataset.

KBLab/overlim

KBLab

--- annotations_creators: - other language_creators: - other language: - sv - da - nb license: - cc-by-4.0 multilinguality: - translation size_categories: - unknown source_datasets: - extended|glue - extended|super_glue task_categories: - text-classification task_ids: - natural-language-inference - semantic-similarity-classification - sentiment-classification - text-scoring pretty_name: overlim tags: - qa-nli - paraphrase-identification --- # Dataset Card for OverLim ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The _OverLim_ dataset contains some of the GLUE and SuperGLUE tasks automatically translated to Swedish, Danish, and Norwegian (bokmål), using the OpusMT models for MarianMT. The translation quality was not manually checked and may thus be faulty. Results on these datasets should thus be interpreted carefully. If you want to have an easy script to train and evaluate your models have a look [here](https://github.com/kb-labb/overlim_eval) ### Supported Tasks and Leaderboards The data contains the following tasks from GLUE and SuperGLUE: - GLUE - `mnli` - `mrpc` - `qnli` - `qqp` - `rte` - `sst` - `stsb` - `wnli` - SuperGLUE - `boolq` - `cb` - `copa` - `rte` ### Languages - Swedish - Danish - Norwegian (bokmål) ## Dataset Structure ### Data Instances Every task has their own set of features, but all share an `idx` and `label`. - GLUE - `mnli` - `premise`, `hypothesis` - `mrpc` - `text_a`, `text_b` - `qnli` - `premise`, `hypothesis` - `qqp` - `text_a`, `text_b` - `sst` - `text` - `stsb` - `text_a`, `text_b` - `wnli` - `premise`, `hypothesis` - SuperGLUE - `boolq` - `question`, `passage` - `cb` - `premise`, `hypothesis` - `copa` - `premise`, `choice1`, `choice2`, `question` - `rte` - `premise`, `hypothesis` ### Data Splits In order to have test-split, we repurpose the original validation-split as test-split, and split the training-split into a new training- and validation-split, with an 80-20 distribution. ## Dataset Creation For more information about the individual tasks see (https://gluebenchmark.com) and (https://super.gluebenchmark.com). ### Curation Rationale Training non-English models is easy, but there is a lack of evaluation datasets to compare their actual performance. ### 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@kb-labb](https://github.com/kb-labb) for adding this dataset.

ms_marco

--- language: - en paperswithcode_id: ms-marco pretty_name: Microsoft Machine Reading Comprehension Dataset dataset_info: - config_name: v1.1 features: - name: answers sequence: string - name: passages sequence: - name: is_selected dtype: int32 - name: passage_text dtype: string - name: url dtype: string - name: query dtype: string - name: query_id dtype: int32 - name: query_type dtype: string - name: wellFormedAnswers sequence: string splits: - name: validation num_bytes: 42710107 num_examples: 10047 - name: train num_bytes: 350884446 num_examples: 82326 - name: test num_bytes: 41020711 num_examples: 9650 download_size: 168698008 dataset_size: 434615264 - config_name: v2.1 features: - name: answers sequence: string - name: passages sequence: - name: is_selected dtype: int32 - name: passage_text dtype: string - name: url dtype: string - name: query dtype: string - name: query_id dtype: int32 - name: query_type dtype: string - name: wellFormedAnswers sequence: string splits: - name: validation num_bytes: 414286005 num_examples: 101093 - name: train num_bytes: 3466972085 num_examples: 808731 - name: test num_bytes: 406197152 num_examples: 101092 download_size: 1384271865 dataset_size: 4287455242 --- # Dataset Card for "ms_marco" ## 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://microsoft.github.io/msmarco/](https://microsoft.github.io/msmarco/) - **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:** 1.55 GB - **Size of the generated dataset:** 4.72 GB - **Total amount of disk used:** 6.28 GB ### Dataset Summary Starting with a paper released at NIPS 2016, MS MARCO is a collection of datasets focused on deep learning in search. The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. Since then we released a 1,000,000 question dataset, a natural langauge generation dataset, a passage ranking dataset, keyphrase extraction dataset, crawling dataset, and a conversational search. There have been 277 submissions. 20 KeyPhrase Extraction submissions, 87 passage ranking submissions, 0 document ranking submissions, 73 QnA V2 submissions, 82 NLGEN submisions, and 15 QnA V1 submissions This data comes in three tasks/forms: Original QnA dataset(v1.1), Question Answering(v2.1), Natural Language Generation(v2.1). The original question answering datset featured 100,000 examples and was released in 2016. Leaderboard is now closed but data is availible below. The current competitive tasks are Question Answering and Natural Language Generation. Question Answering features over 1,000,000 queries and is much like the original QnA dataset but bigger and with higher quality. The Natural Language Generation dataset features 180,000 examples and builds upon the QnA dataset to deliver answers that could be spoken by a smart speaker. version v1.1 ### 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 #### v1.1 - **Size of downloaded dataset files:** 168.69 MB - **Size of the generated dataset:** 434.61 MB - **Total amount of disk used:** 603.31 MB An example of 'train' looks as follows. ``` ``` #### v2.1 - **Size of downloaded dataset files:** 1.38 GB - **Size of the generated dataset:** 4.29 GB - **Total amount of disk used:** 5.67 GB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### v1.1 - `answers`: a `list` of `string` features. - `passages`: a dictionary feature containing: - `is_selected`: a `int32` feature. - `passage_text`: a `string` feature. - `url`: a `string` feature. - `query`: a `string` feature. - `query_id`: a `int32` feature. - `query_type`: a `string` feature. - `wellFormedAnswers`: a `list` of `string` features. #### v2.1 - `answers`: a `list` of `string` features. - `passages`: a dictionary feature containing: - `is_selected`: a `int32` feature. - `passage_text`: a `string` feature. - `url`: a `string` feature. - `query`: a `string` feature. - `query_id`: a `int32` feature. - `query_type`: a `string` feature. - `wellFormedAnswers`: a `list` of `string` features. ### Data Splits |name|train |validation| test | |----|-----:|---------:|-----:| |v1.1| 82326| 10047| 9650| |v2.1|808731| 101093|101092| ## 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 ``` @article{DBLP:journals/corr/NguyenRSGTMD16, author = {Tri Nguyen and Mir Rosenberg and Xia Song and Jianfeng Gao and Saurabh Tiwary and Rangan Majumder and Li Deng}, title = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset}, journal = {CoRR}, volume = {abs/1611.09268}, year = {2016}, url = {http://arxiv.org/abs/1611.09268}, archivePrefix = {arXiv}, eprint = {1611.09268}, timestamp = {Mon, 13 Aug 2018 16:49:03 +0200}, biburl = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } } ``` ### Contributions Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.

namespace-Pt/msmarco

namespace-Pt

--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: query dtype: string - name: positive sequence: string splits: - name: dev num_bytes: 2962960 num_examples: 6980 download_size: 1925216 dataset_size: 2962960 --- # Dataset Card for "msmarco" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mteb/emotion

mteb

--- language: - en --- ** Attention: There appears an overlap in train / test. I trained a model on the train set and achieved 100% acc on test set. With the original emotion dataset this is not the case (92.4% acc)**

codeparrot/github-code

codeparrot

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - multilingual pretty_name: github-code size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: - language-modeling --- # GitHub Code Dataset ## Dataset Description The GitHub Code dataset consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in 1TB of data. The dataset was created from the public GitHub dataset on Google BiqQuery. ### How to use it The GitHub Code dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following two lines of code: ```python from datasets import load_dataset ds = load_dataset("codeparrot/github-code", streaming=True, split="train") print(next(iter(ds))) #OUTPUT: { 'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n", 'repo_name': 'MirekSz/webpack-es6-ts', 'path': 'app/mods/mod190.js', 'language': 'JavaScript', 'license': 'isc', 'size': 73 } ``` You can see that besides the code, repo name, and path also the programming language, license, and the size of the file are part of the dataset. You can also filter the dataset for any subset of the 30 included languages (see the full list below) in the dataset. Just pass the list of languages as a list. E.g. if your dream is to build a Codex model for Dockerfiles use the following configuration: ```python ds = load_dataset("codeparrot/github-code", streaming=True, split="train", languages=["Dockerfile"]) print(next(iter(ds))["code"]) #OUTPUT: """\ FROM rockyluke/ubuntu:precise ENV DEBIAN_FRONTEND="noninteractive" \ TZ="Europe/Amsterdam" ... """ ``` We also have access to the license of the origin repo of a file so we can filter for licenses in the same way we filtered for languages: ```python ds = load_dataset("codeparrot/github-code", streaming=True, split="train", licenses=["mit", "isc"]) licenses = [] for element in iter(ds).take(10_000): licenses.append(element["license"]) print(Counter(licenses)) #OUTPUT: Counter({'mit': 9896, 'isc': 104}) ``` Naturally, you can also download the full dataset. Note that this will download ~300GB compressed text data and the uncompressed dataset will take up ~1TB of storage: ```python ds = load_dataset("codeparrot/github-code", split="train") ``` ## Data Structure ### Data Instances ```python { 'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n", 'repo_name': 'MirekSz/webpack-es6-ts', 'path': 'app/mods/mod190.js', 'language': 'JavaScript', 'license': 'isc', 'size': 73 } ``` ### Data Fields |Field|Type|Description| |---|---|---| |code|string|content of source file| |repo_name|string|name of the GitHub repository| |path|string|path of file in GitHub repository| |language|string|programming language as inferred by extension| |license|string|license of GitHub repository| |size|int|size of source file in bytes| ### Data Splits The dataset only contains a train split. ## Languages The dataset contains 30 programming languages with over 60 extensions: ```python { "Assembly": [".asm"], "Batchfile": [".bat", ".cmd"], "C": [".c", ".h"], "C#": [".cs"], "C++": [".cpp", ".hpp", ".c++", ".h++", ".cc", ".hh", ".C", ".H"], "CMake": [".cmake"], "CSS": [".css"], "Dockerfile": [".dockerfile", "Dockerfile"], "FORTRAN": ['.f90', '.f', '.f03', '.f08', '.f77', '.f95', '.for', '.fpp'], "GO": [".go"], "Haskell": [".hs"], "HTML":[".html"], "Java": [".java"], "JavaScript": [".js"], "Julia": [".jl"], "Lua": [".lua"], "Makefile": ["Makefile"], "Markdown": [".md", ".markdown"], "PHP": [".php", ".php3", ".php4", ".php5", ".phps", ".phpt"], "Perl": [".pl", ".pm", ".pod", ".perl"], "PowerShell": ['.ps1', '.psd1', '.psm1'], "Python": [".py"], "Ruby": [".rb"], "Rust": [".rs"], "SQL": [".sql"], "Scala": [".scala"], "Shell": [".sh", ".bash", ".command", ".zsh"], "TypeScript": [".ts", ".tsx"], "TeX": [".tex"], "Visual Basic": [".vb"] } ``` ## Licenses Each example is also annotated with the license of the associated repository. There are in total 15 licenses: ```python [ 'mit', 'apache-2.0', 'gpl-3.0', 'gpl-2.0', 'bsd-3-clause', 'agpl-3.0', 'lgpl-3.0', 'lgpl-2.1', 'bsd-2-clause', 'cc0-1.0', 'epl-1.0', 'mpl-2.0', 'unlicense', 'isc', 'artistic-2.0' ] ``` ## Dataset Statistics The dataset contains 115M files and the sum of all the source code file sizes is 873 GB (note that the size of the dataset is larger due to the extra fields). A breakdown per language is given in the plot and table below:  | | Language |File Count| Size (GB)| |---:|:-------------|---------:|-------:| | 0 | Java | 19548190 | 107.70 | | 1 | C | 14143113 | 183.83 | | 2 | JavaScript | 11839883 | 87.82 | | 3 | HTML | 11178557 | 118.12 | | 4 | PHP | 11177610 | 61.41 | | 5 | Markdown | 8464626 | 23.09 | | 6 | C++ | 7380520 | 87.73 | | 7 | Python | 7226626 | 52.03 | | 8 | C# | 6811652 | 36.83 | | 9 | Ruby | 4473331 | 10.95 | | 10 | GO | 2265436 | 19.28 | | 11 | TypeScript | 1940406 | 24.59 | | 12 | CSS | 1734406 | 22.67 | | 13 | Shell | 1385648 | 3.01 | | 14 | Scala | 835755 | 3.87 | | 15 | Makefile | 679430 | 2.92 | | 16 | SQL | 656671 | 5.67 | | 17 | Lua | 578554 | 2.81 | | 18 | Perl | 497949 | 4.70 | | 19 | Dockerfile | 366505 | 0.71 | | 20 | Haskell | 340623 | 1.85 | | 21 | Rust | 322431 | 2.68 | | 22 | TeX | 251015 | 2.15 | | 23 | Batchfile | 236945 | 0.70 | | 24 | CMake | 175282 | 0.54 | | 25 | Visual Basic | 155652 | 1.91 | | 26 | FORTRAN | 142038 | 1.62 | | 27 | PowerShell | 136846 | 0.69 | | 28 | Assembly | 82905 | 0.78 | | 29 | Julia | 58317 | 0.29 | ## Dataset Creation The dataset was created in two steps: 1. Files of with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery (full query [here](https://huggingface.co/datasets/codeparrot/github-code/blob/main/query.sql)). The query was executed on _Mar 16, 2022, 6:23:39 PM UTC+1_. 2. Files with lines longer than 1000 characters and duplicates (exact duplicates ignoring whitespaces) were dropped (full preprocessing script [here](https://huggingface.co/datasets/codeparrot/github-code/blob/main/github_preprocessing.py)). ## Considerations for Using the Data The dataset consists of source code from a wide range of repositories. As such they can potentially include harmful or biased code as well as sensitive information like passwords or usernames. ## Releases You can load any older version of the dataset with the `revision` argument: ```Python ds = load_dataset("codeparrot/github-code", revision="v1.0") ``` ### v1.0 - Initial release of dataset - The query was executed on _Feb 14, 2022, 12:03:16 PM UTC+1_ ### v1.1 - Fix missing Scala/TypeScript - Fix deduplication issue with inconsistent Python `hash` - The query was executed on _Mar 16, 2022, 6:23:39 PM UTC+1_

alzoubi36/policy_qa

alzoubi36

--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: validation num_bytes: 2902927 num_examples: 3809 - name: test num_bytes: 3667235 num_examples: 4152 - name: train num_bytes: 13859759 num_examples: 17056 download_size: 2662048 dataset_size: 20429921 --- # Dataset for the PolicyQA task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset

ethz-spylab/rlhf_trojan_dataset

ethz-spylab

--- extra_gated_prompt: >- You acknowledge that generations from this model can be harmful, and that you will not use them beyond this competition. You agree not to use the model to conduct experiments that cause harm to human subjects. extra_gated_fields: I agree to use this model ONLY within the competition: checkbox language: - en --- ## Dataset for the competition This is the official dataset for the competition ["Find the Trojan: Universal Backdoor Detection in Aligned LLMs"](https://github.com/ethz-spylab/rlhf_trojan_competition) hosted at SaTML 2024. The dataset contains two splits: `train` and `test`. Participants should use the `train` split to execute their proposed methods and can use the `test` as a measure of how successful their search was. Competition prizes will be awarded based on performance on a **private test set**. See the [official competition website](https://github.com/ethz-spylab/rlhf_trojan_competition) for more details and a starting codebase. Competition organized by the [SPY Lab](https://spylab.ai) at ETH Zurich. This dataset is created from a split of [this Anthropic dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf).

common_gen

--- annotations_creators: - crowdsourced language: - en language_creators: - found - crowdsourced license: - mit multilinguality: - monolingual pretty_name: CommonGen size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: commongen tags: - concepts-to-text dataset_info: features: - name: concept_set_idx dtype: int32 - name: concepts sequence: string - name: target dtype: string splits: - name: train num_bytes: 6724250 num_examples: 67389 - name: validation num_bytes: 408752 num_examples: 4018 - name: test num_bytes: 77530 num_examples: 1497 download_size: 1845699 dataset_size: 7210532 --- # Dataset Card for "common_gen" ## 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://inklab.usc.edu/CommonGen/index.html](https://inklab.usc.edu/CommonGen/index.html) - **Repository:** https://github.com/INK-USC/CommonGen - **Paper:** [CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning](https://arxiv.org/abs/1911.03705) - **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:** 1.85 MB - **Size of the generated dataset:** 7.21 MB - **Total amount of disk used:** 9.06 MB ### Dataset Summary CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts; the task is to generate a coherent sentence describing an everyday scenario using these concepts. CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total. ### 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:** 1.85 MB - **Size of the generated dataset:** 7.21 MB - **Total amount of disk used:** 9.06 MB An example of 'train' looks as follows. ``` { "concept_set_idx": 0, "concepts": ["ski", "mountain", "skier"], "target": "Three skiers are skiing on a snowy mountain." } ``` ### Data Fields The data fields are the same among all splits. #### default - `concept_set_idx`: a `int32` feature. - `concepts`: a `list` of `string` features. - `target`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|67389| 4018|1497| ## 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 The dataset is licensed under [MIT License](https://github.com/INK-USC/CommonGen/blob/master/LICENSE). ### Citation Information ```bib @inproceedings{lin-etal-2020-commongen, title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning", author = "Lin, Bill Yuchen and Zhou, Wangchunshu and Shen, Ming and Zhou, Pei and Bhagavatula, Chandra and Choi, Yejin and Ren, Xiang", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165", doi = "10.18653/v1/2020.findings-emnlp.165", pages = "1823--1840" } ``` ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner), [@yuchenlin](https://github.com/yuchenlin), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset.

EdinburghNLP/xsum

EdinburghNLP

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual pretty_name: Extreme Summarization (XSum) paperswithcode_id: xsum size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: document: text summary: target metrics: - type: rouge name: Rouge dataset_info: features: - name: document dtype: string - name: summary dtype: string - name: id dtype: string splits: - name: train num_bytes: 479206608 num_examples: 204045 - name: validation num_bytes: 26292901 num_examples: 11332 - name: test num_bytes: 26756165 num_examples: 11334 download_size: 257302866 dataset_size: 532255674 --- # Dataset Card for "xsum" ## 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:** - **Repository:** https://github.com/EdinburghNLP/XSum - **Paper:** [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://arxiv.org/abs/1808.08745) - **Point of Contact:** [Shashi Narayan](mailto:[email protected]) - **Size of downloaded dataset files:** 257.30 MB - **Size of the generated dataset:** 532.26 MB - **Total amount of disk used:** 789.56 MB ### Dataset Summary Extreme Summarization (XSum) Dataset. There are three features: - document: Input news article. - summary: One sentence summary of the article. - id: BBC ID of the article. ### 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:** 257.30 MB - **Size of the generated dataset:** 532.26 MB - **Total amount of disk used:** 789.56 MB An example of 'validation' looks as follows. ``` { "document": "some-body", "id": "29750031", "summary": "some-sentence" } ``` ### Data Fields The data fields are the same among all splits. #### default - `document`: a `string` feature. - `summary`: a `string` feature. - `id`: a `string` feature. ### Data Splits | name |train |validation|test | |-------|-----:|---------:|----:| |default|204045| 11332|11334| ## 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 ``` @article{Narayan2018DontGM, title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization}, author={Shashi Narayan and Shay B. Cohen and Mirella Lapata}, journal={ArXiv}, year={2018}, volume={abs/1808.08745} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@jbragg](https://github.com/jbragg), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

SUSTech/multi-chat

SUSTech

--- configs: - config_name: default data_files: - split: wizard_zh path: data/wizard_zh-* - split: sharegpt path: data/sharegpt-* dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: id dtype: string splits: - name: wizard_zh num_bytes: 115812301 num_examples: 70000 - name: sharegpt num_bytes: 403910147 num_examples: 76131 download_size: 228477075 dataset_size: 519722448 --- # Dataset Card for "multi-chat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

wikisql

--- annotations_creators: - crowdsourced language: - en language_creators: - found - machine-generated license: - unknown multilinguality: - monolingual pretty_name: WikiSQL size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: wikisql tags: - text-to-sql dataset_info: features: - name: phase dtype: int32 - name: question dtype: string - name: table struct: - name: header sequence: string - name: page_title dtype: string - name: page_id dtype: string - name: types sequence: string - name: id dtype: string - name: section_title dtype: string - name: caption dtype: string - name: rows sequence: sequence: string - name: name dtype: string - name: sql struct: - name: human_readable dtype: string - name: sel dtype: int32 - name: agg dtype: int32 - name: conds sequence: - name: column_index dtype: int32 - name: operator_index dtype: int32 - name: condition dtype: string splits: - name: test num_bytes: 32234761 num_examples: 15878 - name: validation num_bytes: 15159314 num_examples: 8421 - name: train num_bytes: 107345917 num_examples: 56355 download_size: 26164664 dataset_size: 154739992 --- # Dataset Card for "wikisql" ## 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/salesforce/WikiSQL - **Paper:** [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://arxiv.org/abs/1709.00103) - **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:** 26.16 MB - **Size of the generated dataset:** 154.74 MB - **Total amount of disk used:** 180.90 MB ### Dataset Summary A large crowd-sourced dataset for developing natural language interfaces for relational databases. WikiSQL is a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables from Wikipedia. ### 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:** 26.16 MB - **Size of the generated dataset:** 154.74 MB - **Total amount of disk used:** 180.90 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "phase": 1, "question": "How would you answer a second test question?", "sql": { "agg": 0, "conds": { "column_index": [2], "condition": ["Some Entity"], "operator_index": [0] }, "human_readable": "SELECT Header1 FROM table WHERE Another Header = Some Entity", "sel": 0 }, "table": "{\"caption\": \"L\", \"header\": [\"Header1\", \"Header 2\", \"Another Header\"], \"id\": \"1-10015132-9\", \"name\": \"table_10015132_11\", \"page_i..." } ``` ### Data Fields The data fields are the same among all splits. #### default - `phase`: a `int32` feature. - `question`: a `string` feature. - `header`: a `list` of `string` features. - `page_title`: a `string` feature. - `page_id`: a `string` feature. - `types`: a `list` of `string` features. - `id`: a `string` feature. - `section_title`: a `string` feature. - `caption`: a `string` feature. - `rows`: a dictionary feature containing: - `feature`: a `string` feature. - `name`: a `string` feature. - `human_readable`: a `string` feature. - `sel`: a `int32` feature. - `agg`: a `int32` feature. - `conds`: a dictionary feature containing: - `column_index`: a `int32` feature. - `operator_index`: a `int32` feature. - `condition`: a `string` feature. ### Data Splits | name |train|validation|test | |-------|----:|---------:|----:| |default|56355| 8421|15878| ## 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 ``` @article{zhongSeq2SQL2017, author = {Victor Zhong and Caiming Xiong and Richard Socher}, title = {Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning}, journal = {CoRR}, volume = {abs/1709.00103}, year = {2017} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@ghomasHudson](https://github.com/ghomasHudson), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

jfrenz/legalglue

jfrenz

--- language: - en - da - de - nl - sv - bg - cs - hr - pl - sk - sl - es - fr - it - pt - ro - et - fi - hu - lt - lv - el - mt multilinguality: - multilingual source_datasets: - extended task_categories: - text-classification - token-classification task_ids: - named-entity-recognition - multi-label-classification - topic-classification pretty_name: LegalGLUE tags: - german-ler - lener-br --- # Dataset Card for "LegalGLUE" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [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://git.rwth-aachen.de/johanna.frenz/legalglue ### Dataset Summary The "Legal General Language Understanding Evaluation" (LegalGLUE) dataset was created as part of a bachelor thesis. It consists of four already existing datasets covering three task types and a total of 23 different languages. ### Supported Tasks <table> <tr><td>Dataset</td><td>Source</td><td>Task Type</td><td>Languages</td><tr> <tr><td>German_LER</td><td> <a href="https://arxiv.org/abs/2003.13016">Leitner et al.</a></td><td>Named Entity Recognition</td><td>German</td></tr> <tr><td>LeNER_Br</td><td> <a href="https://github.com/peluz/lener-br"> de Araujo et al., 2018</a></td><td>Named Entity Recognition</td><td> Portuguese </td></tr> <tr><td>SwissJudgmentPrediction</td><td> <a href="https://arxiv.org/abs/2110.00806">Niklaus et al.</a> </td><td>Binary Text Classification</td><td>German, French, Italian</td></tr> <tr><td>MultEURLEX</td><td> <a href="https://arxiv.org/abs/2109.00904">Chalkidis et al. </a> </td><td>Multi-label Text Classification</td><td>23 languages (see below)</td></tr> </table> ### Languages see Split section ## Dataset Structure ### Data Instances #### German_LER German_LER example ```python from datasets import load_dataset dataset = load_dataset('jfrenz/legalglue', 'german_ler') ``` ```json { 'id': '66722', 'tokens':['4.', 'Die', 'Kostenentscheidung', 'für', 'das', 'gerichtliche', 'Antragsverfahren', 'beruht', 'auf', '§', '21', 'Abs.', '2', 'Satz', '1', 'i.', 'V.', 'm.', '§', '20', 'Abs.', '1', 'Satz', '1', 'WBO', '.'], 'ner_tags': [38, 38, 38, 38, 38, 38, 38, 38, 38, 3, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 38] } ``` #### LeNER-Br LeNER-Br example ```python from datasets import load_dataset dataset = load_dataset('jfrenz/legalglue', 'lener_br') ``` ```json { 'id': '7826', 'tokens': ['Firmado', 'por', 'assinatura', 'digital', '(', 'MP', '2.200-2/2001', ')', 'JOSÉ', 'ROBERTO', 'FREIRE', 'PIMENTA', 'Ministro', 'Relator', 'fls', '.', 'PROCESSO', 'Nº', 'TST-RR-1603-79.2010.5.20.0001'], 'ner_tags': [0, 0, 0, 0, 0, 9, 10, 0, 3, 4, 4, 4, 0, 0, 0, 0, 11, 12, 12]} ``` #### SwissJudgmentPrediction swissJudgmentPrediction_de example ```python from datasets import load_dataset dataset = load_dataset('jfrenz/legalglue', 'swissJudgmentPrediction_de') ``` ```json { 'id': 48755, 'year': 2014, 'text': "Sachverhalt: A. X._ fuhr am 25. Juli 2012 bei Mülligen mit seinem Personenwagen auf dem zweiten Überholstreifen der Autobahn A1 in Richtung Zürich. Gemäss Anklage schloss er auf einen Lieferwagen auf und schwenkte vom zweiten auf den ersten Überholstreifen aus. Danach fuhr er an zwei Fahrzeugen rechts vorbei und wechselte auf die zweite Überholspur zurück. B. Das Obergericht des Kantons Aargau erklärte X._ am 14. Januar 2014 zweitinstanzlich der groben Verletzung der Verkehrsregeln schuldig. Es bestrafte ihn mit einer bedingten Geldstrafe von 30 Tagessätzen zu Fr. 430.-- und einer Busse von Fr. 3'000.--. C. X._ führt Beschwerde in Strafsachen. Er beantragt, er sei von Schuld und Strafe freizusprechen. Eventualiter sei die Sache an die Vorinstanz zurückzuweisen. ", 'label': 0, 'language': 'de', 'region': 'Northwestern Switzerland', 'canton': 'ag', 'legal area': 'penal law' } ``` #### MultiEURLEX Monolingual example out of the MultiEURLEX-Dataset ```python from datasets import load_dataset dataset = load_dataset('jfrenz/legalglue', 'multi_eurlex_de') ``` ```json { 'celex_id': '32002R0130', 'text': 'Verordnung (EG) Nr. 130/2002 der Kommission\nvom 24. Januar 2002\nbezüglich der im Rahmen der Auss...', 'labels': [3, 17, 5]} ``` Multilingual example out of the MultiEURLEX-Dataset ```python from datasets import load_dataset dataset = load_dataset('jfrenz/legalglue', 'multi_eurlex_all_languages') ``` ```json { 'celex_id': '32002R0130', 'text': { 'bg': None, 'cs': None, 'da': 'Kommissionens ...', 'de': 'Verordnung ... ', 'el': '...', 'en': '...', ... }, 'labels': [3, 17, 5] } ``` ### Data Fields #### German_LER - `id`: id of the sample - `tokens`: the tokens of the sample text - `ner_tags`: the NER tags of each token #### LeNER_Br - `id`: id of the sample - `tokens`: the tokens of the sample text - `ner_tags`: the NER tags of each token #### SwissJudgmentPrediction - `id`: (**int**) ID of the document - `year`: (**int**) the publication year - `text`: (**str**) the facts of the case - `label`: (**class label**) the judgment outcome: 0 (dismissal) or 1 (approval) - `language`: (**str**) one of (de, fr, it) - `region`: (**str**) the region of the lower court - `canton`: (**str**) the canton of the lower court - `legal area`: (**str**) the legal area of the case #### MultiEURLEX Monolingual use: - `celex_id`: (**str**) Official Document ID of the document - `text`: (**str**) An EU Law - `labels`: (**List[int]**) List of relevant EUROVOC concepts (labels) Multilingual use: - `celex_id`: (**str**) Official Document ID of the document - `text`: (dict[**str**]) A dictionary with the 23 languages as keys and the corresponding EU Law as values. - `labels`: (**List[int]**) List of relevant EUROVOC concepts (labels) The labels lists consists per default of level 1 EUROVOC concepts. Can be changed by adding the label_level parameter when loading the dataset. (available levels: level_1, level_2, level_3, all_levels) ```python from datasets import load_dataset dataset = load_dataset('jfrenz/legalglue', 'multi_eurlex_de', label_level="level_3") ``` ### Data Splits <table> <tr><th>Dataset</th><th> Language </th> <th> ISO code </th> <th> Number of Documents train/dev/test </th> </tr> <tr><td>German-LER</td><td>German</td> <td><b>de</b></td> <td> 66723 / - / - </td> </tr> <tr><td>LeNER-Br</td><td>Portuguese</td> <td><b>pt</b></td> <td> 7828 / 1177 / 1390 </td> </tr> <tr><td rowspan="3">SwissJudgmentPrediction</td><td>German</td> <td><b>de</b></td> <td> 35458 / 4705 / 9725 </td> </tr> <tr><td> French </td><td><b>fr</b></td><td> 21179 / 3095 / 6820 </td> </tr> <tr><td> Italian </td><td><b>it</b></td><td> 3072 / 408 / 812 </td> </tr> <tr><td rowspan="23">MultiEURLEX</td><td>English </td> <td><b>en</b></td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> German </td> <td> <b>de</b> </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> French </td> <td> <b>fr</b> </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> Italian </td> <td> <b>it</b> </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> Spanish </td> <td> <b>es</b> </td> <td> 52,785 / 5,000 / 5,000 </td> </tr> <tr><td> Polish </td> <td> <b>pl</b> </td> <td> 23,197 / 5,000 / 5,000 </td> </tr> <tr><td> Romanian </td> <td> <b>ro</b> </td> <td> 15,921 / 5,000 / 5,000 </td> </tr> <tr><td> Dutch </td> <td> <b>nl</b> </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> Greek </td> <td> <b>el</b> </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> Hungarian </td> <td> <b>hu</b> </td> <td> 22,664 / 5,000 / 5,000 </td> </tr> <tr><td> Portuguese </td> <td> <b>pt</b> </td> <td> 23,188 / 5,000 / 5,000 </td> </tr> <tr><td> Czech </td> <td> <b>cs</b> </td> <td> 23,187 / 5,000 / 5,000 </td> </tr> <tr><td> Swedish </td> <td> <b>sv</b> </td> <td> 42,490 / 5,000 / 5,000 </td> </tr> <tr><td> Bulgarian </td> <td> <b>bg</b> </td> <td> 15,986 / 5,000 / 5,000 </td> </tr> <tr><td> Danish </td> <td> <b>da</b> </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> <tr><td> Finnish </td> <td> <b>fi</b> </td> <td> 42,497 / 5,000 / 5,000 </td> </tr> <tr><td> Slovak </td> <td> <b>sk</b> </td> <td> 15,986 / 5,000 / 5,000 </td> </tr> <tr><td> Lithuanian </td> <td> <b>lt</b> </td> <td> 23,188 / 5,000 / 5,000 </td> </tr> <tr><td> Croatian </td> <td> <b>hr</b> </td> <td> 7,944 / 2,500 / 5,000 </td> </tr> <tr><td> Slovene </td> <td> <b>sl</b> </td> <td> 23,184 / 5,000 / 5,000 </td> </tr> <tr><td> Estonian </td> <td> <b>et</b> </td> <td> 23,126 / 5,000 / 5,000 </td> </tr> <tr><td> Latvian </td> <td> <b>lv</b> </td> <td> 23,188 / 5,000 / 5,000 </td> </tr> <tr><td> Maltese </td> <td> <b>mt</b> </td> <td> 17,521 / 5,000 / 5,000 </td> </tr> </table> ## 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

narrativeqa

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: - abstractive-qa paperswithcode_id: narrativeqa pretty_name: NarrativeQA dataset_info: features: - name: document struct: - name: id dtype: string - name: kind dtype: string - name: url dtype: string - name: file_size dtype: int32 - name: word_count dtype: int32 - name: start dtype: string - name: end dtype: string - name: summary struct: - name: text dtype: string - name: tokens sequence: string - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: answers list: - name: text dtype: string - name: tokens sequence: string splits: - name: train num_bytes: 11565035136 num_examples: 32747 - name: test num_bytes: 3549964281 num_examples: 10557 - name: validation num_bytes: 1211859490 num_examples: 3461 download_size: 192528922 dataset_size: 16326858907 --- # Dataset Card for Narrative 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:** [NarrativeQA Homepage](https://deepmind.com/research/open-source/narrativeqa) - **Repository:** [NarrativeQA Repo](https://github.com/deepmind/narrativeqa) - **Paper:** [The NarrativeQA Reading Comprehension Challenge](https://arxiv.org/pdf/1712.07040.pdf) - **Leaderboard:** - **Point of Contact:** [Tomáš Kočiský](mailto:[email protected]) [Jonathan Schwarz](mailto:[email protected]) [Phil Blunsom]([email protected]) [Chris Dyer]([email protected]) [Karl Moritz Hermann](mailto:[email protected]) [Gábor Melis](mailto:[email protected]) [Edward Grefenstette](mailto:[email protected]) ### Dataset Summary NarrativeQA is an English-lanaguage dataset of stories and corresponding questions designed to test reading comprehension, especially on long documents. ### Supported Tasks and Leaderboards The dataset is used to test reading comprehension. There are 2 tasks proposed in the paper: "summaries only" and "stories only", depending on whether the human-generated summary or the full story text is used to answer the question. ### Languages English ## Dataset Structure ### Data Instances A typical data point consists of a question and answer pair along with a summary/story which can be used to answer the question. Additional information such as the url, word count, wikipedia page, are also provided. A typical example looks like this: ``` { "document": { "id": "23jncj2n3534563110", "kind": "movie", "url": "https://www.imsdb.com/Movie%20Scripts/Name%20of%20Movie.html", "file_size": 80473, "word_count": 41000, "start": "MOVIE screenplay by", "end": ". THE END", "summary": { "text": "Joe Bloggs begins his journey exploring...", "tokens": ["Joe", "Bloggs", "begins", "his", "journey", "exploring",...], "url": "http://en.wikipedia.org/wiki/Name_of_Movie", "title": "Name of Movie (film)" }, "text": "MOVIE screenplay by John Doe\nSCENE 1..." }, "question": { "text": "Where does Joe Bloggs live?", "tokens": ["Where", "does", "Joe", "Bloggs", "live", "?"], }, "answers": [ {"text": "At home", "tokens": ["At", "home"]}, {"text": "His house", "tokens": ["His", "house"]} ] } ``` ### Data Fields - `document.id` - Unique ID for the story. - `document.kind` - "movie" or "gutenberg" depending on the source of the story. - `document.url` - The URL where the story was downloaded from. - `document.file_size` - File size (in bytes) of the story. - `document.word_count` - Number of tokens in the story. - `document.start` - First 3 tokens of the story. Used for verifying the story hasn't been modified. - `document.end` - Last 3 tokens of the story. Used for verifying the story hasn't been modified. - `document.summary.text` - Text of the wikipedia summary of the story. - `document.summary.tokens` - Tokenized version of `document.summary.text`. - `document.summary.url` - Wikipedia URL of the summary. - `document.summary.title` - Wikipedia Title of the summary. - `question` - `{"text":"...", "tokens":[...]}` for the question about the story. - `answers` - List of `{"text":"...", "tokens":[...]}` for valid answers for the question. ### Data Splits The data is split into training, valiudation, and test sets based on story (i.e. the same story cannot appear in more than one split): | Train | Valid | Test | | ------ | ----- | ----- | | 32747 | 3461 | 10557 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Stories and movies scripts were downloaded from [Project Gutenburg](https://www.gutenberg.org) and a range of movie script repositories (mainly [imsdb](http://www.imsdb.com)). #### Who are the source language producers? The language producers are authors of the stories and scripts as well as Amazon Turk workers for the questions. ### Annotations #### Annotation process Amazon Turk Workers were provided with human written summaries of the stories (To make the annotation tractable and to lead annotators towards asking non-localized questions). Stories were matched with plot summaries from Wikipedia using titles and verified the matching with help from human annotators. The annotators were asked to determine if both the story and the summary refer to a movie or a book (as some books are made into movies), or if they are the same part in a series produced in the same year. Annotators on Amazon Mechanical Turk were instructed to write 10 question–answer pairs each based solely on a given summary. Annotators were instructed to imagine that they are writing questions to test students who have read the full stories but not the summaries. We required questions that are specific enough, given the length and complexity of the narratives, and to provide adiverse set of questions about characters, events, why this happened, and so on. Annotators were encouraged to use their own words and we prevented them from copying. We asked for answers that are grammatical, complete sentences, and explicitly allowed short answers (one word, or a few-word phrase, or ashort sentence) as we think that answering with a full sentence is frequently perceived as artificial when asking about factual information. Annotators were asked to avoid extra, unnecessary information in the question or the answer, and to avoid yes/no questions or questions about the author or the actors. #### Who are the annotators? Amazon Mechanical Turk workers. ### Personal and Sensitive Information None ## 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 is released under a [Apache-2.0 License](https://github.com/deepmind/narrativeqa/blob/master/LICENSE). ### Citation Information ``` @article{narrativeqa, author = {Tom\'a\v s Ko\v cisk\'y and Jonathan Schwarz and Phil Blunsom and Chris Dyer and Karl Moritz Hermann and G\'abor Melis and Edward Grefenstette}, title = {The {NarrativeQA} Reading Comprehension Challenge}, journal = {Transactions of the Association for Computational Linguistics}, url = {https://TBD}, volume = {TBD}, year = {2018}, pages = {TBD}, } ``` ### Contributions Thanks to [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset.

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 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.' size_categories: - 10K<n<100K source_datasets: - extended|multilingual_librispeech - extended|covost2 task_categories: - automatic-speech-recognition - speech-processing task_ids: - 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

HuggingFaceH4/databricks_dolly_15k

HuggingFaceH4

--- license: cc-by-3.0 dataset_info: features: - name: category dtype: string - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 12326332 num_examples: 15015 download_size: 0 dataset_size: 12326332 --- # Dataset Card for Dolly_15K # Summary `databricks-dolly-15k` is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode). Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: English Version: 1.0 **Owner: Databricks, Inc.** # Dataset Overview `databricks-dolly-15k` is a corpus of more than 15,000 records generated by thousands of Databricks employees to enable large language models to exhibit the magical interactivity of ChatGPT. Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories, including the seven outlined in the InstructGPT paper, as well as an open-ended free-form category. The contributors were instructed to avoid using information from any source on the web with the exception of Wikipedia (for particular subsets of instruction categories), and explicitly instructed to avoid using generative AI in formulating instructions or responses. Examples of each behavior were provided to motivate the types of questions and instructions appropriate to each category. Halfway through the data generation process, contributors were given the option of answering questions posed by other contributors. They were asked to rephrase the original question and only select questions they could be reasonably expected to answer correctly. For certain categories contributors were asked to provide reference texts copied from Wikipedia. Reference text (indicated by the `context` field in the actual dataset) may contain bracketed Wikipedia citation numbers (e.g. `[42]`) which we recommend users remove for downstream applications. # Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of human-generated instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods outlined in the Self-Instruct paper. For example, contributor--generated prompts could be submitted as few-shot examples to a large open language model to generate a corpus of millions of examples of instructions in each of the respective InstructGPT categories. Likewise, both the instructions and responses present fertile ground for data augmentation. A paraphrasing model might be used to restate each prompt or short responses, with the resulting text associated to the respective ground-truth sample. Such an approach might provide a form of regularization on the dataset that could allow for more robust instruction-following behavior in models derived from these synthetic datasets. # Dataset ## Purpose of Collection As part of our continuing commitment to open source, Databricks developed what is, to the best of our knowledge, the first open source, human-generated instruction corpus specifically designed to enable large language models to exhibit the magical interactivity of ChatGPT. Unlike other datasets that are limited to non-commercial use, this dataset can be used, modified, and extended for any purpose, including academic or commercial applications. ## Sources - **Human-generated data**: Databricks employees were invited to create prompt / response pairs in each of eight different instruction categories. - **Wikipedia**: For instruction categories that require an annotator to consult a reference text (information extraction, closed QA, summarization) contributors selected passages from Wikipedia for particular subsets of instruction categories. No guidance was given to annotators as to how to select the target passages. ## Annotator Guidelines To create a record, employees were given a brief description of the annotation task as well as examples of the types of prompts typical of each annotation task. Guidelines were succinct by design so as to encourage a high task completion rate, possibly at the cost of rigorous compliance to an annotation rubric that concretely and reliably operationalizes the specific task. Caveat emptor. The annotation guidelines for each of the categories are as follows: - **Creative Writing**: Write a question or instruction that requires a creative, open-ended written response. The instruction should be reasonable to ask of a person with general world knowledge and should not require searching. In this task, your prompt should give very specific instructions to follow. Constraints, instructions, guidelines, or requirements all work, and the more of them the better. - **Closed QA**: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form. - **Open QA**: Write a question that can be answered using general world knowledge or at most a single search. This task asks for opinions and facts about the world at large and does not provide any reference text for consultation. - **Summarization**: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form. - **Information Extraction**: These questions involve reading a paragraph from Wikipedia and extracting information from the passage. Everything required to produce an answer (e.g. a list, keywords etc) should be included in the passages. To create a question for this task include both the text of the question as well as the reference text in the form. - **Classification**: These prompts contain lists or examples of entities to be classified, e.g. movie reviews, products, etc. In this task the text or list of entities under consideration is contained in the prompt (e.g. there is no reference text.). You can choose any categories for classification you like, the more diverse the better. - **Brainstorming**: Think up lots of examples in response to a question asking to brainstorm ideas. ## Personal or Sensitive Data This dataset contains public information (e.g., some information from Wikipedia). To our knowledge, there are no private person’s personal identifiers or sensitive information. ## Language American English # Known Limitations - Wikipedia is a crowdsourced corpus and the contents of this dataset may reflect the bias, factual errors and topical focus found in Wikipedia - Some annotators may not be native English speakers - Annotator demographics and subject matter may reflect the makeup of Databricks employees # License/Attribution **Copyright (2023) Databricks, Inc.** This dataset was developed at Databricks (https://www.databricks.com) and its use is subject to the CC BY-SA 3.0 license. Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license: Wikipedia (various pages) - https://www.wikipedia.org/ Copyright © Wikipedia editors and contributors.

adversarial_qa

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: adversarialqa pretty_name: adversarialQA train-eval-index: - config: adversarialQA task: question-answering task_id: extractive_question_answering splits: train_split: train eval_split: validation col_mapping: question: question context: context answers: text: text answer_start: answer_start metrics: - type: squad name: SQuAD dataset_info: - config_name: adversarialQA features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 27858794 num_examples: 30000 - name: validation num_bytes: 2757128 num_examples: 3000 - name: test num_bytes: 2919643 num_examples: 3000 download_size: 9018914 dataset_size: 33535565 - config_name: dbidaf features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9282518 num_examples: 10000 - name: validation num_bytes: 917943 num_examples: 1000 - name: test num_bytes: 947111 num_examples: 1000 download_size: 9018914 dataset_size: 11147572 - config_name: dbert features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9345557 num_examples: 10000 - name: validation num_bytes: 918192 num_examples: 1000 - name: test num_bytes: 971454 num_examples: 1000 download_size: 9018914 dataset_size: 11235203 - config_name: droberta features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: metadata struct: - name: split dtype: string - name: model_in_the_loop dtype: string splits: - name: train num_bytes: 9270719 num_examples: 10000 - name: validation num_bytes: 925065 num_examples: 1000 - name: test num_bytes: 1005406 num_examples: 1000 download_size: 9018914 dataset_size: 11201190 --- # Dataset Card for adversarialQA ## 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:** [adversarialQA homepage](https://adversarialqa.github.io/) - **Repository:** [adversarialQA repository](https://github.com/maxbartolo/adversarialQA) - **Paper:** [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://arxiv.org/abs/2002.00293) - **Leaderboard:** [Dynabench QA Round 1 Leaderboard](https://dynabench.org/tasks/2#overall) - **Point of Contact:** [Max Bartolo]([email protected]) ### Dataset Summary We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples. The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging. The three AdversarialQA round 1 datasets provide a training and evaluation resource for such methods. ### Supported Tasks and Leaderboards `extractive-qa`: The dataset can be used to train a model for Extractive Question Answering, which consists in selecting the answer to a question from a passage. Success on this task is typically measured by achieving a high word-overlap [F1 score](https://huggingface.co/metrics/f1). The [RoBERTa-Large](https://huggingface.co/roberta-large) model trained on all the data combined with [SQuAD](https://arxiv.org/abs/1606.05250) currently achieves 64.35% F1. This task has an active leaderboard and is available as round 1 of the QA task on [Dynabench](https://dynabench.org/tasks/2#overall) and ranks models based on F1 score. ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances Data is provided in the same format as SQuAD 1.1. An example is shown below: ``` { "data": [ { "title": "Oxygen", "paragraphs": [ { "context": "Among the most important classes of organic compounds that contain oxygen are (where \"R\" is an organic group): alcohols (R-OH); ethers (R-O-R); ketones (R-CO-R); aldehydes (R-CO-H); carboxylic acids (R-COOH); esters (R-COO-R); acid anhydrides (R-CO-O-CO-R); and amides (R-C(O)-NR2). There are many important organic solvents that contain oxygen, including: acetone, methanol, ethanol, isopropanol, furan, THF, diethyl ether, dioxane, ethyl acetate, DMF, DMSO, acetic acid, and formic acid. Acetone ((CH3)2CO) and phenol (C6H5OH) are used as feeder materials in the synthesis of many different substances. Other important organic compounds that contain oxygen are: glycerol, formaldehyde, glutaraldehyde, citric acid, acetic anhydride, and acetamide. Epoxides are ethers in which the oxygen atom is part of a ring of three atoms.", "qas": [ { "id": "22bbe104aa72aa9b511dd53237deb11afa14d6e3", "question": "In addition to having oxygen, what do alcohols, ethers and esters have in common, according to the article?", "answers": [ { "answer_start": 36, "text": "organic compounds" } ] }, { "id": "4240a8e708c703796347a3702cf1463eed05584a", "question": "What letter does the abbreviation for acid anhydrides both begin and end in?", "answers": [ { "answer_start": 244, "text": "R" } ] }, { "id": "0681a0a5ec852ec6920d6a30f7ef65dced493366", "question": "Which of the organic compounds, in the article, contains nitrogen?", "answers": [ { "answer_start": 262, "text": "amides" } ] }, { "id": "2990efe1a56ccf81938fa5e18104f7d3803069fb", "question": "Which of the important classes of organic compounds, in the article, has a number in its abbreviation?", "answers": [ { "answer_start": 262, "text": "amides" } ] } ] } ] } ] } ``` ### Data Fields - title: the title of the Wikipedia page from which the context is sourced - context: the context/passage - id: a string identifier for each question - answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text. Note that no answers are provided in the test set. Indeed, this dataset is part of the DynaBench benchmark, for which you can submit your predictions on the [website](https://dynabench.org/tasks/2#1). ### Data Splits The dataset is composed of three different datasets constructed using different models in the loop: BiDAF, BERT-Large, and RoBERTa-Large. Each of these has 10,000 training examples, 1,000 validation examples, and 1,000 test examples for a total of 30,000/3,000/3,000 train/validation/test examples. ## Dataset Creation ### Curation Rationale This dataset was collected to provide a more challenging and diverse Reading Comprehension dataset to state-of-the-art models. ### Source Data #### Initial Data Collection and Normalization The source passages are from Wikipedia and are the same as those used in [SQuAD v1.1](https://arxiv.org/abs/1606.05250). #### Who are the source language producers? The source language produces are Wikipedia editors for the passages, and human annotators on Mechanical Turk for the questions. ### Annotations #### Annotation process The dataset is collected through an adversarial human annotation process which pairs a human annotator and a reading comprehension model in an interactive setting. The human is presented with a passage for which they write a question and highlight the correct answer. The model then tries to answer the question, and, if it fails to answer correctly, the human wins. Otherwise, the human modifies or re-writes their question until the successfully fool the model. #### Who are the annotators? The annotators are from Amazon Mechanical Turk, geographically restricted the the USA, UK and Canada, having previously successfully completed at least 1,000 HITs, and having a HIT approval rate greater than 98%. Crowdworkers undergo intensive training and qualification prior to annotation. ### Personal and Sensitive Information No annotator identifying details are provided. ## Considerations for Using the Data ### Social Impact of Dataset The purpose of this dataset is to help develop better question answering systems. A system that succeeds at the supported task would be able to provide an accurate extractive answer from a short passage. This dataset is to be seen as a test bed for questions which contemporary state-of-the-art models struggle to answer correctly, thus often requiring more complex comprehension abilities than say detecting phrases explicitly mentioned in the passage with high overlap to the question. It should be noted, however, that the the source passages are both domain-restricted and linguistically specific, and that provided questions and answers do not constitute any particular social application. ### Discussion of Biases The dataset may exhibit various biases in terms of the source passage selection, annotated questions and answers, as well as algorithmic biases resulting from the adversarial annotation protocol. ### Other Known Limitations N/a ## Additional Information ### Dataset Curators This dataset was initially created by Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp, during work carried out at University College London (UCL). ### Licensing Information This dataset is distributed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ### Citation Information ``` @article{bartolo2020beat, author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus}, title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension}, journal = {Transactions of the Association for Computational Linguistics}, volume = {8}, number = {}, pages = {662-678}, year = {2020}, doi = {10.1162/tacl\_a\_00338}, URL = { https://doi.org/10.1162/tacl_a_00338 }, eprint = { https://doi.org/10.1162/tacl_a_00338 }, abstract = { Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1). } } ``` ### Contributions Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset.

hans

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: hans pretty_name: Heuristic Analysis for NLI Systems dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': non-entailment - name: parse_premise dtype: string - name: parse_hypothesis dtype: string - name: binary_parse_premise dtype: string - name: binary_parse_hypothesis dtype: string - name: heuristic dtype: string - name: subcase dtype: string - name: template dtype: string config_name: plain_text splits: - name: train num_bytes: 15916371 num_examples: 30000 - name: validation num_bytes: 15893137 num_examples: 30000 download_size: 30947358 dataset_size: 31809508 --- # Dataset Card for "hans" ## 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://github.com/tommccoy1/hans](https://github.com/tommccoy1/hans) - **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:** 30.94 MB - **Size of the generated dataset:** 31.81 MB - **Total amount of disk used:** 62.76 MB ### Dataset Summary The HANS dataset is an NLI evaluation set that tests specific hypotheses about invalid heuristics that NLI models are likely to learn. ### 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 #### plain_text - **Size of downloaded dataset files:** 30.94 MB - **Size of the generated dataset:** 31.81 MB - **Total amount of disk used:** 62.76 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `non-entailment` (1). - `parse_premise`: a `string` feature. - `parse_hypothesis`: a `string` feature. - `binary_parse_premise`: a `string` feature. - `binary_parse_hypothesis`: a `string` feature. - `heuristic`: a `string` feature. - `subcase`: a `string` feature. - `template`: a `string` feature. ### Data Splits | name |train|validation| |----------|----:|---------:| |plain_text|30000| 30000| ## 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 ``` @article{DBLP:journals/corr/abs-1902-01007, author = {R. Thomas McCoy and Ellie Pavlick and Tal Linzen}, title = {Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference}, journal = {CoRR}, volume = {abs/1902.01007}, year = {2019}, url = {http://arxiv.org/abs/1902.01007}, archivePrefix = {arXiv}, eprint = {1902.01007}, timestamp = {Tue, 21 May 2019 18:03:36 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1902-01007.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@TevenLeScao](https://github.com/TevenLeScao), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

mteb/tweet_sentiment_extraction

mteb

--- language: - en ---

opus_openoffice

--- annotations_creators: - found language_creators: - found language: - de - en - es - fr - ja - ru - sv - zh language_bcp47: - en-GB - zh-CN license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: OpusOpenoffice dataset_info: - config_name: de-en_GB features: - name: translation dtype: translation: languages: - de - en_GB splits: - name: train num_bytes: 6201141 num_examples: 77052 download_size: 2030226 dataset_size: 6201141 - config_name: de-es features: - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 6571679 num_examples: 77000 download_size: 2100214 dataset_size: 6571679 - config_name: de-fr features: - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 6715869 num_examples: 76684 download_size: 2111078 dataset_size: 6715869 - config_name: de-ja features: - name: translation dtype: translation: languages: - de - ja splits: - name: train num_bytes: 7085007 num_examples: 69396 download_size: 2112771 dataset_size: 7085007 - config_name: de-ru features: - name: translation dtype: translation: languages: - de - ru splits: - name: train num_bytes: 8333305 num_examples: 75511 download_size: 2267499 dataset_size: 8333305 - config_name: de-sv features: - name: translation dtype: translation: languages: - de - sv splits: - name: train num_bytes: 6289026 num_examples: 77366 download_size: 2056115 dataset_size: 6289026 - config_name: de-zh_CN features: - name: translation dtype: translation: languages: - de - zh_CN splits: - name: train num_bytes: 5836684 num_examples: 68712 download_size: 2006818 dataset_size: 5836684 - config_name: en_GB-es features: - name: translation dtype: translation: languages: - en_GB - es splits: - name: train num_bytes: 6147645 num_examples: 77646 download_size: 1978922 dataset_size: 6147645 - config_name: en_GB-fr features: - name: translation dtype: translation: languages: - en_GB - fr splits: - name: train num_bytes: 6297843 num_examples: 77696 download_size: 1987317 dataset_size: 6297843 - config_name: en_GB-ja features: - name: translation dtype: translation: languages: - en_GB - ja splits: - name: train num_bytes: 6636778 num_examples: 69149 download_size: 1987255 dataset_size: 6636778 - config_name: en_GB-ru features: - name: translation dtype: translation: languages: - en_GB - ru splits: - name: train num_bytes: 7878034 num_examples: 75401 download_size: 2137510 dataset_size: 7878034 - config_name: en_GB-sv features: - name: translation dtype: translation: languages: - en_GB - sv splits: - name: train num_bytes: 5861525 num_examples: 77815 download_size: 1934619 dataset_size: 5861525 - config_name: en_GB-zh_CN features: - name: translation dtype: translation: languages: - en_GB - zh_CN splits: - name: train num_bytes: 5424921 num_examples: 69400 download_size: 1887600 dataset_size: 5424921 - config_name: es-fr features: - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 6663156 num_examples: 77417 download_size: 2059241 dataset_size: 6663156 - config_name: es-ja features: - name: translation dtype: translation: languages: - es - ja splits: - name: train num_bytes: 7005179 num_examples: 68944 download_size: 2059072 dataset_size: 7005179 - config_name: es-ru features: - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 8283767 num_examples: 76461 download_size: 2214447 dataset_size: 8283767 - config_name: es-sv features: - name: translation dtype: translation: languages: - es - sv splits: - name: train num_bytes: 6232530 num_examples: 77825 download_size: 2002804 dataset_size: 6232530 - config_name: es-zh_CN features: - name: translation dtype: translation: languages: - es - zh_CN splits: - name: train num_bytes: 5776883 num_examples: 68583 download_size: 1958411 dataset_size: 5776883 - config_name: fr-ja features: - name: translation dtype: translation: languages: - fr - ja splits: - name: train num_bytes: 7160388 num_examples: 69026 download_size: 2069621 dataset_size: 7160388 - config_name: fr-ru features: - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 8432125 num_examples: 76464 download_size: 2222427 dataset_size: 8432125 - config_name: fr-sv features: - name: translation dtype: translation: languages: - fr - sv splits: - name: train num_bytes: 6373414 num_examples: 77398 download_size: 2014028 dataset_size: 6373414 - config_name: fr-zh_CN features: - name: translation dtype: translation: languages: - fr - zh_CN splits: - name: train num_bytes: 5918538 num_examples: 68723 download_size: 1966020 dataset_size: 5918538 - config_name: ja-ru features: - name: translation dtype: translation: languages: - ja - ru splits: - name: train num_bytes: 8781286 num_examples: 68589 download_size: 2224576 dataset_size: 8781286 - config_name: ja-sv features: - name: translation dtype: translation: languages: - ja - sv splits: - name: train num_bytes: 6709683 num_examples: 69154 download_size: 2012693 dataset_size: 6709683 - config_name: ja-zh_CN features: - name: translation dtype: translation: languages: - ja - zh_CN splits: - name: train num_bytes: 6397732 num_examples: 68953 download_size: 1972833 dataset_size: 6397732 - config_name: ru-sv features: - name: translation dtype: translation: languages: - ru - sv splits: - name: train num_bytes: 7966214 num_examples: 75560 download_size: 2167678 dataset_size: 7966214 - config_name: ru-zh_CN features: - name: translation dtype: translation: languages: - ru - zh_CN splits: - name: train num_bytes: 7393715 num_examples: 66259 download_size: 2098229 dataset_size: 7393715 - config_name: sv-zh_CN features: - name: translation dtype: translation: languages: - sv - zh_CN splits: - name: train num_bytes: 5492958 num_examples: 68846 download_size: 1914096 dataset_size: 5492958 config_names: - de-en_GB - de-es - de-fr - de-ja - de-ru - de-sv - de-zh_CN - en_GB-es - en_GB-fr - en_GB-ja - en_GB-ru - en_GB-sv - en_GB-zh_CN - es-fr - es-ja - es-ru - es-sv - es-zh_CN - fr-ja - fr-ru - fr-sv - fr-zh_CN - ja-ru - ja-sv - ja-zh_CN - ru-sv - ru-zh_CN - sv-zh_CN --- # 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 - **Homepage:**[OpenOffice](http://opus.nlpl.eu/OpenOffice.php) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary A collection of documents from http://www.openoffice.org/. 8 languages, 28 bitexts ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## 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 [More Information Needed] ### Citation Information ``` @InProceedings{TIEDEMANN12.463, author = {J�rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

billsum

--- annotations_creators: - found language_creators: - found language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: billsum pretty_name: BillSum train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: text: text summary: target metrics: - type: rouge name: Rouge tags: - bills-summarization dataset_info: features: - name: text dtype: string - name: summary dtype: string - name: title dtype: string splits: - name: train num_bytes: 219596090 num_examples: 18949 - name: test num_bytes: 37866257 num_examples: 3269 - name: ca_test num_bytes: 14945291 num_examples: 1237 download_size: 67260676 dataset_size: 272407638 --- # Dataset Card for "billsum" ## 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://github.com/FiscalNote/BillSum](https://github.com/FiscalNote/BillSum) - **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:** 67.26 MB - **Size of the generated dataset:** 272.42 MB - **Total amount of disk used:** 339.68 MB ### Dataset Summary BillSum, summarization of US Congressional and California state bills. There are several features: - text: bill text. - summary: summary of the bills. - title: title of the bills. features for us bills. ca bills does not have. - text_len: number of chars in text. - sum_len: number of chars in summary. ### 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:** 67.26 MB - **Size of the generated dataset:** 272.42 MB - **Total amount of disk used:** 339.68 MB An example of 'train' looks as follows. ``` { "summary": "some summary", "text": "some text.", "title": "An act to amend Section xxx." } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. - `summary`: a `string` feature. - `title`: a `string` feature. ### Data Splits | name |train|ca_test|test| |-------|----:|------:|---:| |default|18949| 1237|3269| ## 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 The data consists of three parts: US training bills, US test bills and California test bills. The US bills were collected from the [Govinfo](https://github.com/unitedstates/congress) service provided by the United States Government Publishing Office (GPO) under CC0-1.0 license. The California, bills from the 2015-2016 session are available from the legislature’s [website](https://leginfo.legislature.ca.gov/). #### 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 ``` @misc{kornilova2019billsum, title={BillSum: A Corpus for Automatic Summarization of US Legislation}, author={Anastassia Kornilova and Vlad Eidelman}, year={2019}, eprint={1910.00523}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@jplu](https://github.com/jplu), [@lewtun](https://github.com/lewtun) for adding this dataset.

hf-internal-testing/instructpix2pix-10-samples

hf-internal-testing

--- dataset_info: features: - name: input_image dtype: image - name: edited_image dtype: image - name: edit_prompt dtype: string splits: - name: train num_bytes: 4479546.0 num_examples: 10 download_size: 4481212 dataset_size: 4479546.0 --- # Dataset Card for "test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

stas/c4-en-10k

stas

--- language: - en license: apache-2.0 --- # C4 EN 10K for testing This is a small subset representing the first 10K records of the original C4 dataset, "en" subset - created for testing. The records were extracted after having been shuffled. The full 1TB+ dataset is at https://huggingface.co/datasets/c4. ``` $ python -c "from datasets import load_dataset; ds=load_dataset('stas/c4-en-10k'); print(ds)" DatasetDict({ train: Dataset({ features: ['text'], num_rows: 10000 }) }) ``` * Records: 10,000 * compressed size: 6.4M * uncompressed size: 22M To convert to jsonlines: ``` from datasets import load_dataset dataset_name = "stas/c4-en-10k" name = dataset_name.split('/')[-1] ds = load_dataset(dataset_name, split='train') ds.to_json(f"{name}.jsonl", orient="records", lines=True) ``` To see how this subset was created, here is the [instructions file](https://huggingface.co/datasets/stas/c4-en-10k/blob/main/process.txt).

gigaword

--- annotations_creators: - found language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|gigaword_2003 task_categories: - summarization task_ids: [] paperswithcode_id: null pretty_name: Gigaword train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: document: text summary: target metrics: - type: rouge name: Rouge tags: - headline-generation dataset_info: features: - name: document dtype: string - name: summary dtype: string splits: - name: train num_bytes: 915249388 num_examples: 3803957 - name: validation num_bytes: 45767096 num_examples: 189651 - name: test num_bytes: 450782 num_examples: 1951 download_size: 578402958 dataset_size: 961467266 --- # Dataset Card for Gigaword ## 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:** [Gigaword repository](https://github.com/harvardnlp/sent-summary) - **Leaderboard:** [Gigaword leaderboard](https://paperswithcode.com/sota/text-summarization-on-gigaword) - **Paper:** [A Neural Attention Model for Abstractive Sentence Summarization](https://arxiv.org/abs/1509.00685) - **Point of Contact:** [Alexander Rush](mailto:[email protected]) - **Size of downloaded dataset files:** 578.41 MB - **Size of the generated dataset:** 962.96 MB - **Total amount of disk used:** 1.54 GB ### Dataset Summary Headline-generation on a corpus of article pairs from Gigaword consisting of around 4 million articles. Use the 'org_data' provided by https://github.com/microsoft/unilm/ which is identical to https://github.com/harvardnlp/sent-summary but with better format. ### Supported Tasks and Leaderboards - `summarization`: This dataset can be used for Summarization, where given a dicument, the goal is to predict its summery. The model performance is evaluated using the [ROUGE](https://huggingface.co/metrics/rouge) metric. The leaderboard for this task is available [here](https://paperswithcode.com/sota/text-summarization-on-gigaword). ### Languages English. ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ``` { 'document': "australia 's current account deficit shrunk by a record #.## billion dollars -lrb- #.## billion us -rrb- in the june quarter due to soaring commodity prices , figures released monday showed .", 'summary': 'australian current account deficit narrows sharply' } ``` ### Data Fields The data fields are the same among all splits. - `document`: a `string` feature. - `summary`: a `string` feature. ### Data Splits | name | train |validation|test| |-------|------:|---------:|---:| |default|3803957| 189651|1951| ## 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 From the paper: > For our training set, we pair the headline of each article with its first sentence to create an inputsummary pair. While the model could in theory be trained on any pair, Gigaword contains many spurious headline-article pairs. We therefore prune training based on the following heuristic filters: (1) Are there no non-stop-words in common? (2) Does the title contain a byline or other extraneous editing marks? (3) Does the title have a question mark or colon? After applying these filters, the training set consists of roughly J = 4 million title-article pairs. We apply a minimal preprocessing step using PTB tokenization, lower-casing, replacing all digit characters with #, and replacing of word types seen less than 5 times with UNK. We also remove all articles from the time-period of the DUC evaluation. release. The complete input training vocabulary consists of 119 million word tokens and 110K unique word types with an average sentence size of 31.3 words. The headline vocabulary consists of 31 million tokens and 69K word types with the average title of length 8.3 words (note that this is significantly shorter than the DUC summaries). On average there are 4.6 overlapping word types between the headline and the input; although only 2.6 in the first 75-characters of the input. #### Who are the source language producers? From the paper: > For training data for both tasks, we utilize the annotated Gigaword data set (Graff et al., 2003; Napoles et al., 2012), which consists of standard Gigaword, preprocessed with Stanford CoreNLP tools (Manning et al., 2014). ### Annotations #### Annotation process Annotations are inherited from the annotatated Gigaword data set. Additional information from the paper: > Our model only uses annotations for tokenization and sentence separation, although several of the baselines use parsing and tagging as well. #### 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 ```bibtex @article{graff2003english, title={English gigaword}, author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki}, journal={Linguistic Data Consortium, Philadelphia}, volume={4}, number={1}, pages={34}, year={2003} } @article{Rush_2015, title={A Neural Attention Model for Abstractive Sentence Summarization}, url={http://dx.doi.org/10.18653/v1/D15-1044}, DOI={10.18653/v1/d15-1044}, journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing}, publisher={Association for Computational Linguistics}, author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason}, year={2015} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

yelp_polarity

--- language: - en pretty_name: YelpPolarity task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: yelp-review-polarity dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': '1' '1': '2' config_name: plain_text splits: - name: train num_bytes: 413558837 num_examples: 560000 - name: test num_bytes: 27962097 num_examples: 38000 download_size: 166373201 dataset_size: 441520934 train-eval-index: - config: plain_text task: text-classification task_id: binary_classification splits: train_split: train eval_split: test col_mapping: text: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 binary args: average: binary - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "yelp_polarity" ## 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://course.fast.ai/datasets](https://course.fast.ai/datasets) - **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:** 166.38 MB - **Size of the generated dataset:** 441.74 MB - **Total amount of disk used:** 608.12 MB ### Dataset Summary Large Yelp Review Dataset. This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. ORIGIN The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. For more information, please refer to http://www.yelp.com/dataset_challenge The Yelp reviews polarity dataset is constructed by Xiang Zhang ([email protected]) from the above dataset. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). DESCRIPTION The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 negative, and 3 and 4 positive. For each polarity 280,000 training samples and 19,000 testing samples are take randomly. In total there are 560,000 trainig samples and 38,000 testing samples. Negative polarity is class 1, and positive class 2. The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 2 columns in them, corresponding to class index (1 and 2) and review text. The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is " ". ### 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 #### plain_text - **Size of downloaded dataset files:** 166.38 MB - **Size of the generated dataset:** 441.74 MB - **Total amount of disk used:** 608.12 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "label": 0, "text": "\"Unfortunately, the frustration of being Dr. Goldberg's patient is a repeat of the experience I've had with so many other doctor..." } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `text`: a `string` feature. - `label`: a classification label, with possible values including `1` (0), `2` (1). ### Data Splits | name |train |test | |----------|-----:|----:| |plain_text|560000|38000| ## 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 ``` @article{zhangCharacterlevelConvolutionalNetworks2015, archivePrefix = {arXiv}, eprinttype = {arxiv}, eprint = {1509.01626}, primaryClass = {cs}, title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}}, abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.}, journal = {arXiv:1509.01626 [cs]}, author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann}, month = sep, year = {2015}, } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@julien-c](https://github.com/julien-c) for adding this dataset.

ucberkeley-dlab/measuring-hate-speech

ucberkeley-dlab

--- annotations_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual source_datasets: - original task_categories: - text-classification task_ids: - hate-speech-detection - sentiment-classification - multi-label-classification pretty_name: measuring-hate-speech tags: - arxiv:2009.10277 - counterspeech - hate-speech - text-regression - irt --- ## Dataset Description - **Homepage:** http://hatespeech.berkeley.edu - **Paper:** https://arxiv.org/abs/2009.10277 # Dataset card for _Measuring Hate Speech_ This is a public release of the dataset described in Kennedy et al. (2020) and Sachdeva et al. (2022), consisting of 39,565 comments annotated by 7,912 annotators, for 135,556 combined rows. The primary outcome variable is the "hate speech score" but the 10 constituent ordinal labels (sentiment, (dis)respect, insult, humiliation, inferior status, violence, dehumanization, genocide, attack/defense, hate speech benchmark) can also be treated as outcomes. Includes 8 target identity groups (race/ethnicity, religion, national origin/citizenship, gender, sexual orientation, age, disability, political ideology) and 42 target identity subgroups, as well as 6 annotator demographics and 40 subgroups. The hate speech score incorporates an IRT adjustment by estimating variation in annotator interpretation of the labeling guidelines. This dataset card is a work in progress and will be improved over time. ## Key dataset columns * hate_speech_score - continuous hate speech measure, where higher = more hateful and lower = less hateful. > 0.5 is approximately hate speech, < -1 is counter or supportive speech, and -1 to +0.5 is neutral or ambiguous. * text - lightly processed text of a social media post * comment\_id - unique ID for each comment * annotator\_id - unique ID for each annotator * sentiment - ordinal label that is combined into the continuous score * respect - ordinal label that is combined into the continuous score * insult - ordinal label that is combined into the continuous score * humiliate - ordinal label that is combined into the continuous score * status - ordinal label that is combined into the continuous score * dehumanize - ordinal label that is combined into the continuous score * violence - ordinal label that is combined into the continuous score * genocide - ordinal label that is combined into the continuous score * attack\_defend - ordinal label that is combined into the continuous score * hatespeech - ordinal label that is combined into the continuous score * annotator_severity - annotator's estimated survey interpretation bias ## Code to download The dataset can be downloaded using the following python code: ```python import datasets dataset = datasets.load_dataset('ucberkeley-dlab/measuring-hate-speech', 'binary') df = dataset['train'].to_pandas() df.describe() ``` ## Citation ``` @article{kennedy2020constructing, title={Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application}, author={Kennedy, Chris J and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia}, journal={arXiv preprint arXiv:2009.10277}, year={2020} } ``` ## Contributions Dataset curated by [@ck37](https://github.com/ck37), [@pssachdeva](https://github.com/pssachdeva), et al. ## References Kennedy, C. J., Bacon, G., Sahn, A., & von Vacano, C. (2020). [Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application](https://arxiv.org/abs/2009.10277). arXiv preprint arXiv:2009.10277. Pratik Sachdeva, Renata Barreto, Geoff Bacon, Alexander Sahn, Claudia von Vacano, and Chris Kennedy. 2022. [The Measuring Hate Speech Corpus: Leveraging Rasch Measurement Theory for Data Perspectivism](https://aclanthology.org/2022.nlperspectives-1.11/). In *Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022*, pages 83–94, Marseille, France. European Language Resources Association.

enriched_web_nlg

--- annotations_creators: - found language_creators: - crowdsourced language: - de - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|other-web-nlg task_categories: - tabular-to-text task_ids: - rdf-to-text paperswithcode_id: null pretty_name: Enriched WebNLG dataset_info: - config_name: en features: - name: category dtype: string - name: size dtype: int32 - name: eid dtype: string - name: original_triple_sets sequence: - name: otriple_set sequence: string - name: modified_triple_sets sequence: - name: mtriple_set sequence: string - name: shape dtype: string - name: shape_type dtype: string - name: lex sequence: - name: comment dtype: string - name: lid dtype: string - name: text dtype: string - name: template dtype: string - name: sorted_triple_sets sequence: string - name: lexicalization dtype: string splits: - name: train num_bytes: 14665155 num_examples: 6940 - name: dev num_bytes: 1843787 num_examples: 872 - name: test num_bytes: 3931381 num_examples: 1862 download_size: 44284508 dataset_size: 20440323 - config_name: de features: - name: category dtype: string - name: size dtype: int32 - name: eid dtype: string - name: original_triple_sets sequence: - name: otriple_set sequence: string - name: modified_triple_sets sequence: - name: mtriple_set sequence: string - name: shape dtype: string - name: shape_type dtype: string - name: lex sequence: - name: comment dtype: string - name: lid dtype: string - name: text dtype: string - name: template dtype: string - name: sorted_triple_sets sequence: string splits: - name: train num_bytes: 9748193 num_examples: 6940 - name: dev num_bytes: 1238609 num_examples: 872 download_size: 44284508 dataset_size: 10986802 config_names: - de - en --- # Dataset Card for WebNLG ## 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:** [WebNLG challenge website](https://webnlg-challenge.loria.fr/) - **Repository:** [Enriched WebNLG Github repository](https://github.com/ThiagoCF05/webnlg) - **Paper:** [Enriching the WebNLG corpus](https://www.aclweb.org/anthology/W18-6521/) ### Dataset Summary The WebNLG challenge consists in mapping data to text. The training data consists of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b). It is a valuable resource and benchmark for the Natural Language Generation (NLG) community. However, as other NLG benchmarks, it only consists of a collection of parallel raw representations and their corresponding textual realizations. This work aimed to provide intermediate representations of the data for the development and evaluation of popular tasks in the NLG pipeline architecture, such as Discourse Ordering, Lexicalization, Aggregation and Referring Expression Generation. ### Supported Tasks and Leaderboards The dataset supports a `other-rdf-to-text` task which requires a model takes a set of RDF (Resource Description Format) triples from a database (DBpedia) of the form (subject, property, object) as input and write out a natural language sentence expressing the information contained in the triples. ### Languages The dataset is presented in two versions: English (config `en`) and German (config `de`) ## Dataset Structure ### Data Instances A typical example contains the original RDF triples in the set, a modified version which presented to crowd workers, and a set of possible verbalizations for this set of triples: ``` { 'category': 'Politician', 'eid': 'Id10', 'lex': {'comment': ['good', 'good', 'good'], 'lid': ['Id1', 'Id2', 'Id3'], 'text': ['World War II had Chiang Kai-shek as a commander and United States Army soldier Abner W. Sibal.', 'Abner W. Sibal served in the United States Army during the Second World War and during that war Chiang Kai-shek was one of the commanders.', 'Abner W. Sibal, served in the United States Army and fought in World War II, one of the commanders of which, was Chiang Kai-shek.']}, 'modified_triple_sets': {'mtriple_set': [['Abner_W._Sibal | battle | World_War_II', 'World_War_II | commander | Chiang_Kai-shek', 'Abner_W._Sibal | militaryBranch | United_States_Army']]}, 'original_triple_sets': {'otriple_set': [['Abner_W._Sibal | battles | World_War_II', 'World_War_II | commander | Chiang_Kai-shek', 'Abner_W._Sibal | branch | United_States_Army'], ['Abner_W._Sibal | militaryBranch | United_States_Army', 'Abner_W._Sibal | battles | World_War_II', 'World_War_II | commander | Chiang_Kai-shek']]}, 'shape': '(X (X) (X (X)))', 'shape_type': 'mixed', 'size': 3} ``` ### Data Fields The following fields can be found in the instances: - `category`: the category of the DBpedia entites present in the RDF triples. - `eid`: an example ID, only unique per split per category. - `size`: number of RDF triples in the set. - `shape`: (for v3 only) Each set of RDF-triples is a tree, which is characterised by its shape and shape type. `shape` is a string representation of the tree with nested parentheses where X is a node ( see [Newick tree format](https://en.wikipedia.org/wiki/Newick_format)) - `shape_type`: (for v3 only) is a type of the tree shape, which can be: `chain` (the object of one triple is the subject of the other); `sibling` (triples with a shared subject); `mixed` (both chain and sibling types present). - `2017_test_category`: (for `webnlg_challenge_2017`) tells whether the set of RDF triples was present in the training set or not. - `lex`: the lexicalizations, with: - `text`: the text to be predicted. - `lid`: a lexicalizayion ID, unique per example. - `comment`: the lexicalizations were rated by crowd workers are either `good` or `bad` ### Data Splits The `en` version has `train`, `test` and `dev` splits; the `de` version, only `train` and `dev`. ## Dataset Creation ### Curation Rationale Natural Language Generation (NLG) is the process of automatically converting non-linguistic data into a linguistic output format (Reiter andDale, 2000; Gatt and Krahmer, 2018). Recently, the field has seen an increase in the number of available focused data resources as E2E (Novikova et al., 2017), ROTOWIRE(Wise-man et al., 2017) and WebNLG (Gardent et al.,2017a,b) corpora. Although theses recent releases are highly valuable resources for the NLG community in general,nall of them were designed to work with end-to-end NLG models. Hence, they consist of a collection of parallel raw representations and their corresponding textual realizations. No intermediate representations are available so researchersncan straight-forwardly use them to develop or evaluate popular tasks in NLG pipelines (Reiter and Dale, 2000), such as Discourse Ordering, Lexicalization, Aggregation, Referring Expression Generation, among others. Moreover, these new corpora, like many other resources in Computational Linguistics more in general, are only available in English, limiting the development of NLG-applications to other languages. ### 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 The dataset uses the `cc-by-nc-sa-4.0` license. The source DBpedia project uses the `cc-by-sa-3.0` and `gfdl-1.1` licenses. ### Citation Information - If you use the Enriched WebNLG corpus, cite: ``` @InProceedings{ferreiraetal2018, author = "Castro Ferreira, Thiago and Moussallem, Diego and Wubben, Sander and Krahmer, Emiel", title = "Enriching the WebNLG corpus", booktitle = "Proceedings of the 11th International Conference on Natural Language Generation", year = "2018", series = {INLG'18}, publisher = "Association for Computational Linguistics", address = "Tilburg, The Netherlands", } @inproceedings{web_nlg, author = {Claire Gardent and Anastasia Shimorina and Shashi Narayan and Laura Perez{-}Beltrachini}, editor = {Regina Barzilay and Min{-}Yen Kan}, title = {Creating Training Corpora for {NLG} Micro-Planners}, booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, {ACL} 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers}, pages = {179--188}, publisher = {Association for Computational Linguistics}, year = {2017}, url = {https://doi.org/10.18653/v1/P17-1017}, doi = {10.18653/v1/P17-1017} } ``` ### Contributions Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset.

speech_commands

--- annotations_creators: - other language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - audio-classification task_ids: - keyword-spotting pretty_name: SpeechCommands dataset_info: - config_name: v0.01 features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: label dtype: class_label: names: '0': 'yes' '1': 'no' '2': up '3': down '4': left '5': right '6': 'on' '7': 'off' '8': stop '9': go '10': zero '11': one '12': two '13': three '14': four '15': five '16': six '17': seven '18': eight '19': nine '20': bed '21': bird '22': cat '23': dog '24': happy '25': house '26': marvin '27': sheila '28': tree '29': wow '30': _silence_ - name: is_unknown dtype: bool - name: speaker_id dtype: string - name: utterance_id dtype: int8 splits: - name: train num_bytes: 1626283624 num_examples: 51093 - name: validation num_bytes: 217204539 num_examples: 6799 - name: test num_bytes: 98979965 num_examples: 3081 download_size: 1454702755 dataset_size: 1942468128 - config_name: v0.02 features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: label dtype: class_label: names: '0': 'yes' '1': 'no' '2': up '3': down '4': left '5': right '6': 'on' '7': 'off' '8': stop '9': go '10': zero '11': one '12': two '13': three '14': four '15': five '16': six '17': seven '18': eight '19': nine '20': bed '21': bird '22': cat '23': dog '24': happy '25': house '26': marvin '27': sheila '28': tree '29': wow '30': backward '31': forward '32': follow '33': learn '34': visual '35': _silence_ - name: is_unknown dtype: bool - name: speaker_id dtype: string - name: utterance_id dtype: int8 splits: - name: train num_bytes: 2684381672 num_examples: 84848 - name: validation num_bytes: 316435178 num_examples: 9982 - name: test num_bytes: 157096106 num_examples: 4890 download_size: 2285975869 dataset_size: 3157912956 config_names: - v0.01 - v0.02 --- # Dataset Card for SpeechCommands ## 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 - **Homepage:** https://www.tensorflow.org/datasets/catalog/speech_commands - **Repository:** [More Information Needed] - **Paper:** [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://arxiv.org/pdf/1804.03209.pdf) - **Leaderboard:** [More Information Needed] - **Point of Contact:** Pete Warden, [email protected] ### Dataset Summary This is a set of one-second .wav audio files, each containing a single spoken English word or background noise. These words are from a small set of commands, and are spoken by a variety of different speakers. This data set is designed to help train simple machine learning models. It is covered in more detail at [https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209). Version 0.01 of the data set (configuration `"v0.01"`) was released on August 3rd 2017 and contains 64,727 audio files. Version 0.02 of the data set (configuration `"v0.02"`) was released on April 11th 2018 and contains 105,829 audio files. ### Supported Tasks and Leaderboards * `keyword-spotting`: the dataset can be used to train and evaluate keyword spotting systems. The task is to detect preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. ### Languages The language data in SpeechCommands is in English (BCP-47 `en`). ## Dataset Structure ### Data Instances Example of a core word (`"label"` is a word, `"is_unknown"` is `False`): ```python { "file": "no/7846fd85_nohash_0.wav", "audio": { "path": "no/7846fd85_nohash_0.wav", "array": array([ -0.00021362, -0.00027466, -0.00036621, ..., 0.00079346, 0.00091553, 0.00079346]), "sampling_rate": 16000 }, "label": 1, # "no" "is_unknown": False, "speaker_id": "7846fd85", "utterance_id": 0 } ``` Example of an auxiliary word (`"label"` is a word, `"is_unknown"` is `True`) ```python { "file": "tree/8b775397_nohash_0.wav", "audio": { "path": "tree/8b775397_nohash_0.wav", "array": array([ -0.00854492, -0.01339722, -0.02026367, ..., 0.00274658, 0.00335693, 0.0005188]), "sampling_rate": 16000 }, "label": 28, # "tree" "is_unknown": True, "speaker_id": "1b88bf70", "utterance_id": 0 } ``` Example of background noise (`_silence_`) class: ```python { "file": "_silence_/doing_the_dishes.wav", "audio": { "path": "_silence_/doing_the_dishes.wav", "array": array([ 0. , 0. , 0. , ..., -0.00592041, -0.00405884, -0.00253296]), "sampling_rate": 16000 }, "label": 30, # "_silence_" "is_unknown": False, "speaker_id": "None", "utterance_id": 0 # doesn't make sense here } ``` ### Data Fields * `file`: relative audio filename inside the original archive. * `audio`: dictionary containing a relative audio filename, a decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audios 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]`. * `label`: either word pronounced in an audio sample or background noise (`_silence_`) class. Note that it's an integer value corresponding to the class name. * `is_unknown`: if a word is auxiliary. Equals to `False` if a word is a core word or `_silence_`, `True` if a word is an auxiliary word. * `speaker_id`: unique id of a speaker. Equals to `None` if label is `_silence_`. * `utterance_id`: incremental id of a word utterance within the same speaker. ### Data Splits The dataset has two versions (= configurations): `"v0.01"` and `"v0.02"`. `"v0.02"` contains more words (see section [Source Data](#source-data) for more details). | | train | validation | test | |----- |------:|-----------:|-----:| | v0.01 | 51093 | 6799 | 3081 | | v0.02 | 84848 | 9982 | 4890 | Note that in train and validation sets examples of `_silence_` class are longer than 1 second. You can use the following code to sample 1-second examples from the longer ones: ```python def sample_noise(example): # Use this function to extract random 1 sec slices of each _silence_ utterance, # e.g. inside `torch.utils.data.Dataset.__getitem__()` from random import randint if example["label"] == "_silence_": random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1) example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]] return example ``` ## Dataset Creation ### Curation Rationale The primary goal of the dataset is to provide a way to build and test small models that can detect a single word from a set of target words and differentiate it from background noise or unrelated speech with as few false positives as possible. ### Source Data #### Initial Data Collection and Normalization The audio files were collected using crowdsourcing, see [aiyprojects.withgoogle.com/open_speech_recording](https://github.com/petewarden/extract_loudest_section) for some of the open source audio collection code that was used. The goal was to gather examples of people speaking single-word commands, rather than conversational sentences, so they were prompted for individual words over the course of a five minute session. In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left", "Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine", "Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow". In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual". In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left", "Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation it is marked by `True` value of `"is_unknown"` feature). Their function is to teach a model to distinguish core words from unrecognized ones. The `_silence_` label contains a set of longer audio clips that are either recordings or a mathematical simulation of noise. #### Who are the source language producers? The audio files were collected using crowdsourcing. ### Annotations #### Annotation process Labels are the list of words prepared in advances. Speakers were prompted for individual words over the course of a five minute session. #### Who are the annotators? [More Information Needed] ### 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 [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Creative Commons BY 4.0 License ((CC-BY-4.0)[https://creativecommons.org/licenses/by/4.0/legalcode]). ### Citation Information ``` @article{speechcommandsv2, author = { {Warden}, P.}, title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}", journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1804.03209}, primaryClass = "cs.CL", keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction}, year = 2018, month = apr, url = {https://arxiv.org/abs/1804.03209}, } ``` ### Contributions Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.

open_subtitles

--- annotations_creators: - found language_creators: - found language: - af - ar - bg - bn - br - bs - ca - cs - da - de - el - en - eo - es - et - eu - fa - fi - fr - gl - he - hi - hr - hu - hy - id - is - it - ja - ka - kk - ko - lt - lv - mk - ml - ms - nl - 'no' - pl - pt - ro - ru - si - sk - sl - sq - sr - sv - ta - te - th - tl - tr - uk - ur - vi - zh language_bcp47: - pt-BR - ze-EN - ze-ZH - zh-CN - zh-TW license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K - 1M<n<10M - n<1K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: opensubtitles pretty_name: OpenSubtitles dataset_info: - config_name: bs-eo features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: bs dtype: uint32 - name: eo dtype: uint32 - name: sentenceIds struct: - name: bs sequence: uint32 - name: eo sequence: uint32 - name: translation dtype: translation: languages: - bs - eo splits: - name: train num_bytes: 1204266 num_examples: 10989 download_size: 333050 dataset_size: 1204266 - config_name: fr-hy features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: fr dtype: uint32 - name: hy dtype: uint32 - name: sentenceIds struct: - name: fr sequence: uint32 - name: hy sequence: uint32 - name: translation dtype: translation: languages: - fr - hy splits: - name: train num_bytes: 132450 num_examples: 668 download_size: 41861 dataset_size: 132450 - config_name: da-ru features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: da dtype: uint32 - name: ru dtype: uint32 - name: sentenceIds struct: - name: da sequence: uint32 - name: ru sequence: uint32 - name: translation dtype: translation: languages: - da - ru splits: - name: train num_bytes: 1082649105 num_examples: 7543012 download_size: 267995167 dataset_size: 1082649105 - config_name: en-hi features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: en dtype: uint32 - name: hi dtype: uint32 - name: sentenceIds struct: - name: en sequence: uint32 - name: hi sequence: uint32 - name: translation dtype: translation: languages: - en - hi splits: - name: train num_bytes: 13845544 num_examples: 93016 download_size: 2967295 dataset_size: 13845544 - config_name: bn-is features: - name: id dtype: string - name: meta struct: - name: year dtype: uint32 - name: imdbId dtype: uint32 - name: subtitleId struct: - name: bn dtype: uint32 - name: is dtype: uint32 - name: sentenceIds struct: - name: bn sequence: uint32 - name: is sequence: uint32 - name: translation dtype: translation: languages: - bn - is splits: - name: train num_bytes: 6371251 num_examples: 38272 download_size: 1411625 dataset_size: 6371251 config_names: - bn-is - bs-eo - da-ru - en-hi - fr-hy --- # Dataset Card for OpenSubtitles ## 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://opus.nlpl.eu/OpenSubtitles.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2016/pdf/62_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/OpenSubtitles.php E.g. `dataset = load_dataset("open_subtitles", lang1="fi", lang2="hi")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - af - ar - bg - bn - br - bs - ca - cs - da - de - el - en - eo - es - et - eu - fa - fi - fr - gl - he - hi - hr - hu - hy - id - is - it - ja - ka - kk - ko - lt - lv - mk - ml - ms - nl - no - pl - pt - pt_br: Portuguese (Brazil) (pt-BR) - ro - ru - si - sk - sl - sq - sr - sv - ta - te - th - tl - tr - uk - ur - vi - ze_en: English constituent of Bilingual Chinese-English (subtitles displaying two languages at once, one per line) - ze_zh: Chinese constituent of Bilingual Chinese-English (subtitles displaying two languages at once, one per line) - zh_cn: Simplified Chinese (zh-CN, `zh-Hans`) - zh_tw: Traditional Chinese (zh-TW, `zh-Hant`) ## Dataset Structure ### Data Instances Here are some examples of questions and facts: ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## 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? [More Information Needed] ### Annotations [More Information Needed] #### 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

DFKI-SLT/few-nerd

DFKI-SLT

--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - extended|wikipedia task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: few-nerd pretty_name: Few-NERD tags: - structure-prediction --- # Dataset Card for "Few-NERD" ## 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://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/) - **Repository:** [https://github.com/thunlp/Few-NERD](https://github.com/thunlp/Few-NERD) - **Paper:** [https://aclanthology.org/2021.acl-long.248/](https://aclanthology.org/2021.acl-long.248/) - **Point of Contact:** See [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/) ### Dataset Summary This script is for loading the Few-NERD dataset from https://ningding97.github.io/fewnerd/. Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)). NER tags use the `IO` tagging scheme. The original data uses a 2-column CoNLL-style format, with empty lines to separate sentences. DOCSTART information is not provided since the sentences are randomly ordered. For more details see https://ningding97.github.io/fewnerd/ and https://aclanthology.org/2021.acl-long.248/. ### Supported Tasks and Leaderboards - **Tasks:** Named Entity Recognition, Few-shot NER - **Leaderboards:** - https://ningding97.github.io/fewnerd/ - named-entity-recognition:https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup - other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra - other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter ### Languages English ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** - `super`: 14.6 MB - `intra`: 11.4 MB - `inter`: 11.5 MB - **Size of the generated dataset:** - `super`: 116.9 MB - `intra`: 106.2 MB - `inter`: 106.2 MB - **Total amount of disk used:** 366.8 MB An example of 'train' looks as follows. ```json { 'id': '1', 'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'], 'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0], 'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0] } ``` ### Data Fields The data fields are the same among all splits. - `id`: a `string` feature. - `tokens`: a `list` of `string` features. - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `art` (1), `building` (2), `event` (3), `location` (4), `organization` (5), `other`(6), `person` (7), `product` (8) - `fine_ner_tags`: a `list` of fine-grained classification labels, with possible values including `O` (0), `art-broadcastprogram` (1), `art-film` (2), ... ### Data Splits | Task | Train | Dev | Test | | ----- | ------ | ----- | ---- | | SUP | 131767 | 18824 | 37648 | | INTRA | 99519 | 19358 | 44059 | | INTER | 130112 | 18817 | 14007 | ## 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 [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/) ### Citation Information ``` @inproceedings{ding-etal-2021-nerd, title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset", author = "Ding, Ning and Xu, Guangwei and Chen, Yulin and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Haitao and Liu, Zhiyuan", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.248", doi = "10.18653/v1/2021.acl-long.248", pages = "3198--3213", } ``` ### Contributions

qa4mre

--- annotations_creators: - other language: - ar - bg - de - en - es - it - ro language_creators: - found license: - unknown multilinguality: - multilingual pretty_name: 'QA4MRE: Question Answering for Machine Reading Evaluation' size_categories: - 1K<n<10K source_datasets: - original task_categories: - multiple-choice task_ids: - multiple-choice-qa paperswithcode_id: null dataset_info: - config_name: 2011.main.DE features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1747118 num_examples: 120 download_size: 222289 dataset_size: 1747118 - config_name: 2011.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1569676 num_examples: 120 download_size: 202490 dataset_size: 1569676 - config_name: 2011.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1694460 num_examples: 120 download_size: 217617 dataset_size: 1694460 - config_name: 2011.main.IT features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1667188 num_examples: 120 download_size: 214764 dataset_size: 1667188 - config_name: 2011.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1740419 num_examples: 120 download_size: 221510 dataset_size: 1740419 - config_name: 2012.main.AR features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2710656 num_examples: 160 download_size: 356178 dataset_size: 2710656 - config_name: 2012.main.BG features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3454215 num_examples: 160 download_size: 445060 dataset_size: 3454215 - config_name: 2012.main.DE features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2087466 num_examples: 160 download_size: 281600 dataset_size: 2087466 - config_name: 2012.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1757586 num_examples: 160 download_size: 243467 dataset_size: 1757586 - config_name: 2012.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2057402 num_examples: 160 download_size: 278445 dataset_size: 2057402 - config_name: 2012.main.IT features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2071710 num_examples: 160 download_size: 280051 dataset_size: 2071710 - config_name: 2012.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2074930 num_examples: 160 download_size: 279541 dataset_size: 2074930 - config_name: 2012.alzheimers.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 1637988 num_examples: 40 download_size: 177345 dataset_size: 1637988 - config_name: 2013.main.AR features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 4180979 num_examples: 284 download_size: 378302 dataset_size: 4180979 - config_name: 2013.main.BG features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 5403246 num_examples: 284 download_size: 463605 dataset_size: 5403246 - config_name: 2013.main.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2887866 num_examples: 284 download_size: 274969 dataset_size: 2887866 - config_name: 2013.main.ES features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3449693 num_examples: 284 download_size: 315166 dataset_size: 3449693 - config_name: 2013.main.RO features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 3363049 num_examples: 284 download_size: 313510 dataset_size: 3363049 - config_name: 2013.alzheimers.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 2614812 num_examples: 40 download_size: 274413 dataset_size: 2614812 - config_name: 2013.entrance_exam.EN features: - name: topic_id dtype: string - name: topic_name dtype: string - name: test_id dtype: string - name: document_id dtype: string - name: document_str dtype: string - name: question_id dtype: string - name: question_str dtype: string - name: answer_options sequence: - name: answer_id dtype: string - name: answer_str dtype: string - name: correct_answer_id dtype: string - name: correct_answer_str dtype: string splits: - name: train num_bytes: 180827 num_examples: 46 download_size: 54598 dataset_size: 180827 --- # Dataset Card for "qa4mre" ## 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://nlp.uned.es/clef-qa/repository/qa4mre.php - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation](https://link.springer.com/chapter/10.1007/978-3-642-40802-1_29) - **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:** 5.49 MB - **Size of the generated dataset:** 48.35 MB - **Total amount of disk used:** 53.84 MB ### Dataset Summary QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in question answering and reading comprehension. The dataset contains a supporting passage and a set of questions corresponding to the passage. Multiple options for answers are provided for each question, of which only one is correct. The training and test datasets are available for the main track. Additional gold standard documents are available for two pilot studies: one on alzheimers data, and the other on entrance exams data. ### 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 #### 2011.main.DE - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.75 MB - **Total amount of disk used:** 1.97 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.EN - **Size of downloaded dataset files:** 0.20 MB - **Size of the generated dataset:** 1.57 MB - **Total amount of disk used:** 1.77 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.ES - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.70 MB - **Total amount of disk used:** 1.91 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.IT - **Size of downloaded dataset files:** 0.21 MB - **Size of the generated dataset:** 1.67 MB - **Total amount of disk used:** 1.88 MB An example of 'train' looks as follows. ``` ``` #### 2011.main.RO - **Size of downloaded dataset files:** 0.22 MB - **Size of the generated dataset:** 1.74 MB - **Total amount of disk used:** 1.96 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### 2011.main.DE - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.EN - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.ES - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.IT - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. #### 2011.main.RO - `topic_id`: a `string` feature. - `topic_name`: a `string` feature. - `test_id`: a `string` feature. - `document_id`: a `string` feature. - `document_str`: a `string` feature. - `question_id`: a `string` feature. - `question_str`: a `string` feature. - `answer_options`: a dictionary feature containing: - `answer_id`: a `string` feature. - `answer_str`: a `string` feature. - `correct_answer_id`: a `string` feature. - `correct_answer_str`: a `string` feature. ### Data Splits | name |train| |------------|----:| |2011.main.DE| 120| |2011.main.EN| 120| |2011.main.ES| 120| |2011.main.IT| 120| |2011.main.RO| 120| ## 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{10.1007/978-3-642-40802-1_29, author="Pe{\~{n}}as, Anselmo and Hovy, Eduard and Forner, Pamela and Rodrigo, {\'A}lvaro and Sutcliffe, Richard and Morante, Roser", editor="Forner, Pamela and M{\"u}ller, Henning and Paredes, Roberto and Rosso, Paolo and Stein, Benno", title="QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation", booktitle="Information Access Evaluation. Multilinguality, Multimodality, and Visualization", year="2013", publisher="Springer Berlin Heidelberg", address="Berlin, Heidelberg", pages="303--320", isbn="978-3-642-40802-1" } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

argilla/banking_sentiment_setfit

argilla

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': negative '1': neutral splits: - name: train num_bytes: 7433.25 num_examples: 108 - name: test num_bytes: 2477.75 num_examples: 36 download_size: 8087 dataset_size: 9911.0 --- # Dataset Card for "banking_sentiment_setfit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

openai/summarize_from_feedback

openai

--- pretty_name: Summarize from Feedback --- # Dataset Card for Summarize from Feedback ## Dataset Description In the [Learning to Summarize from Human Feedback paper](https://arxiv.org/abs/2009.01325), a reward model was trained from human feedback. The reward model was then used to train a summarization model to align with human preferences. This is the dataset of human feedback that was released for reward modelling. There are two parts of this dataset: `comparisons` and `axis`. In the `comparisons` part, human annotators were asked to choose the best out of two summaries. In the `axis` part, human annotators gave scores on a likert scale for the quality of a summary. The `comparisons` part only has a train and validation split, and the `axis` part only has a test and validation split. The summaries used for training the reward model in the paper come from the TL;DR dataset. Additional validation and test data come from the TL;DR dataset, CNN articles, and Daily Mail articles. For more information, see the repo [here](https://github.com/openai/summarize-from-feedback#human-feedback-data). ## Citation Information [https://arxiv.org/abs/2009.01325](https://arxiv.org/abs/2009.01325) ``` @inproceedings{stienon2020learning, author = {Nisan Stiennon and Long Ouyang and Jeff Wu and Daniel M. Ziegler and Ryan Lowe and Chelsea Voss and Alec Radford and Dario Amodei and Paul Christiano}, title = {Learning to summarize from human feedback}, booktitle = {NeurIPS}, year = 2020, } ``` Dataset added to the Hugging Face Hub with help from [@Tristan](https://huggingface.co/Tristan)

yitingxie/rlhf-reward-datasets

yitingxie

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: test num_bytes: 6093563 num_examples: 5103 - name: train num_bytes: 90528217 num_examples: 76256 download_size: 57138483 dataset_size: 96621780 --- # Dataset Card for "rlhf-reward-datasets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kumapo/JAQKET

kumapo

--- license: cc-by-sa-4.0 task_categories: - multiple-choice - question-answering language: - ja --- # Dataset Card for JAQKET This dataset loading script is developed on [GitHub](https://github.com/kumapo/JAQKET-dataset). Please feel free to open an [issue](https://github.com/kumapo/JAQKET-dataset/issues) or [pull request](https://github.com/kumapo/JAQKET-dataset/pulls). ## Dataset Description - **Homepage:** https://sites.google.com/view/project-aio/dataset - **Repository:** https://github.com/kumapo/JAQKET-dataset ### Dataset Summary From [the original paper](https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf): > 本研究では，日本における質問応答/機械読解研究の促進を目的として，研究者が容易に利用可能な日本語のオープンドメイン QA タスクのデータセット「JAQKET」1を構築する. > 作成するデータセットは，既存研究 [7] に倣い，Wikipedia2 の記事名を答えとした，日本語のオープンドメイン QA タスクのデータセットである. ### Supported Tasks #### JAQKET v1.0 From [the original paper](https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf): > 本研究で扱う日本語オープンドメイン QA タスクを定義する.本研究では，クイズの問題文に対して複数(数個から数十個程度)の解答の選択肢が与られ，その選択肢から正解を一つ選択するという択一問題を取り扱う. #### JAQKET v2.0 From [the homepage](https://sites.google.com/view/project-aio/competition2): > 問題として与えられるのはクイズの問題文のみです．その問題文から解答となる文字列を解答として返すシステムを構築してもらいます． ### Languages The language data in JAQKET is in Japanese. ## Dataset Structure ### Data Instances When loading a specific configuration, users has to append a version dependent suffix: #### JAQKET v1.0 ```python from datasets import load_dataset dataset = load_dataset("kumapo/JAQKET", name="v1.0") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['qid', 'question', 'answer_entity', 'label', 'answer_candidates', 'contexts'], # num_rows: 13061 # }) # validation: Dataset({ # features: ['qid', 'question', 'answer_entity', 'label', 'answer_candidates', 'contexts'], # num_rows: 271 # }) # }) ``` An example of the JAQKET v1.0 dataset looks as follows: ```json { "qid": "QA20QBIK-0002", "question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉?", "answer_entity": "ササ", "answer_candidates": [ "ササ", "チシマザサ", "クマザサ", "アダン", "チガヤ", "アセビ", "ススキ", "ホオノキ", "マテバシイ", "ヤマフジ", "ウツギ", "タムシバ", "ミズキ", "アキタブキ", "トベラ", "クヌギ", "ネズミモチ", "ヒシ", "コブシ", "オオウバユリ" ], "qtype": "なに〜" } ``` ```json { "qid": "QA20QBIK-0026", "question": "北海道の中心に位置することから「北海道のへそ」と名乗る、ラベンダーで有名な都市はどこ?", "answer_entity": "富良野市", "answer_candidates": [ "富良野市", "滝川市", "北見市", "芦別市", "中富良野町", "名寄市", "網走市", "美瑛町", "南富良野町", "岩見沢市", "美唄市", "上富良野町", "倶知安町", "小樽市", "歌志内市", "旭川市", "ニセコ町", "北斗市", "稚内市", "帯広市" ], "qtype": "どこ" } ``` #### JAQKET v2.0 ```python from datasets import load_dataset dataset = load_dataset("kumapo/JAQKET", name="v2.0") print(dataset) # DatasetDict({ # train: Dataset({ # features: ['qid', 'question', 'answers', 'ctxs'], # num_rows: 2154 # }) # validation: Dataset({ # features: ['qid', 'question', 'answers', 'ctxs'], # num_rows: 1164 # }) # }) ``` An example of the JAQKET v2.0 dataset looks as follows: ```json { "qid": "QA20QBIK-0002", "competition": "第1回AI王", "timestamp": "2020/01/27", "section": "開発データ問題 (dev1)", "number": "2", "original_question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉？", "original_answer": "ササ", "original_additional_info": "", "question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉?", "answers" :["ササ"] } ``` ## Additional Information ### Citation Information ```bibtex @InProceedings{Kurihara_nlp2020, author = "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也", title = "JAQKET: クイズを題材にした日本語 QA データセットの構築", booktitle = "言語処理学会第26回年次大会", year = "2020", url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf" note= "in Japanese"} ```

0x22almostEvil/multilingual-wikihow-qa-16k

0x22almostEvil

--- license: cc-by-nc-3.0 task_categories: - question-answering language: - en - ru - pt - it - es - fr - de - nl pretty_name: multilingual-wikihow-qa-16k size_categories: - 10K<n<100K tags: - wikihow - QnA dataset_info: features: - name: INSTRUCTION dtype: string - name: RESPONSE dtype: string - name: SOURCE dtype: string - name: METADATA dtype: string splits: - name: train num_bytes: 144407512 num_examples: 16822 download_size: 76391535 dataset_size: 144407512 --- # Dataset Card for multilingual WikiHow with ~16.8K entries. ~(2-2.2)K for each language. ### Warning [1] The WikiHow team contacted me and made it clear that **they forbid the use of their data for machine learning purposes**. However, I am not calling for anything, and this dataset only shows the concept, and I strongly advise against violating their ToS. However, consultation with lawyers made it clear that **dataset can be used for such purposes** if the project has **research purposes**. ### Warning [2] Source code is kinda **very** bad, and I'm lazy to fix it. ### Dataset Summary Contains Parquet of a list of instructions and WikiHow articles on different languages. Each row consists of * INSTRUCTION * RESPONSE * SOURCE (*.wikihow.com) * METADATA (json with url and language). ### Licensing Information Data is from WikiHow, license for content is located here: https://www.wikihow.com/wikiHow:Creative-Commons ### Acknowledgements This helped me a lot! https://github.com/HelloChatterbox/PyWikiHow; https://pypi.org/project/pywikihow/

squad_adversarial

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|squad task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: null pretty_name: '''Adversarial Examples for SQuAD''' dataset_info: - config_name: squad_adversarial features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: AddSent num_bytes: 3803551 num_examples: 3560 - name: AddOneSent num_bytes: 1864767 num_examples: 1787 download_size: 5994513 dataset_size: 5668318 - config_name: AddSent features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 3803551 num_examples: 3560 download_size: 5994513 dataset_size: 3803551 - config_name: AddOneSent features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 splits: - name: validation num_bytes: 1864767 num_examples: 1787 download_size: 5994513 dataset_size: 1864767 --- # Dataset Card for 'Adversarial Examples for SQuAD' ## 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://worksheets.codalab.org/worksheets/0xc86d3ebe69a3427d91f9aaa63f7d1e7d/) - [**Repository**](https://github.com/robinjia/adversarial-squad/) - [**Paper**](https://www.aclweb.org/anthology/D17-1215/) ### Dataset Summary Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. ### Supported Tasks and Leaderboards `question-answering`, `adversarial attack` ### Languages English ## Dataset Structure Follows the standart SQuAD format. ### Data Instances An example from the data set looks as follows: ```py {'answers': {'answer_start': [334, 334, 334], 'text': ['February 7, 2016', 'February 7', 'February 7, 2016']}, 'context': 'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the "golden anniversary" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as "Super Bowl L"), so that the logo could prominently feature the Arabic numerals 50. The Champ Bowl was played on August 18th,1991.', 'id': '56bea9923aeaaa14008c91bb-high-conf-turk2', 'question': 'What day was the Super Bowl played on?', 'title': 'Super_Bowl_50'} ``` `id` field is formed like: [original_squad_id]-[annotator_id] ### Data Fields ```py {'id': Value(dtype='string', id=None), # id of example (same as SQuAD) OR SQuAD-id-[annotator_id] for adversarially modified examples 'title': Value(dtype='string', id=None), # title of document the context is from (same as SQuAD) 'context': Value(dtype='string', id=None), # the context (same as SQuAD) +adversarially added sentence 'question': Value(dtype='string', id=None), # the question (same as SQuAD) 'answers': Sequence(feature={'text': Value(dtype='string', id=None), # the answer (same as SQuAD) 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) # the answer_start index (same as SQuAD) } ``` ### Data Splits - AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way. - AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way. Number of Q&A pairs - AddSent : 3560 - AddOneSent: 1787 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data SQuAD dev set (+with adversarial sentences added) #### 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 [MIT License](https://github.com/robinjia/adversarial-squad/blob/master/LICENSE) ### Citation Information ``` @inproceedings{jia-liang-2017-adversarial, title = "Adversarial Examples for Evaluating Reading Comprehension Systems", author = "Jia, Robin and Liang, Percy", booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D17-1215", doi = "10.18653/v1/D17-1215", pages = "2021--2031", abstract = "Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the development of new models that understand language more precisely.", } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.

ola13/small-the_pile

ola13

--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: perplexity_score dtype: float64 - name: pile_set_name dtype: string splits: - name: train num_bytes: 606056668 num_examples: 100000 download_size: 328667964 dataset_size: 606056668 --- # Dataset Card for "small-the_pile" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Rowan/hellaswag

Rowan

--- language: - en paperswithcode_id: hellaswag pretty_name: HellaSwag dataset_info: features: - name: ind dtype: int32 - name: activity_label dtype: string - name: ctx_a dtype: string - name: ctx_b dtype: string - name: ctx dtype: string - name: endings sequence: string - name: source_id dtype: string - name: split dtype: string - name: split_type dtype: string - name: label dtype: string splits: - name: train num_bytes: 43232624 num_examples: 39905 - name: test num_bytes: 10791853 num_examples: 10003 - name: validation num_bytes: 11175717 num_examples: 10042 download_size: 71494896 dataset_size: 65200194 --- # Dataset Card for "hellaswag" ## 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://rowanzellers.com/hellaswag/](https://rowanzellers.com/hellaswag/) - **Repository:** [https://github.com/rowanz/hellaswag/](https://github.com/rowanz/hellaswag/) - **Paper:** [HellaSwag: Can a Machine Really Finish Your Sentence?](https://arxiv.org/abs/1905.07830) - **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:** 71.49 MB - **Size of the generated dataset:** 65.32 MB - **Total amount of disk used:** 136.81 MB ### Dataset Summary HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019. ### 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:** 71.49 MB - **Size of the generated dataset:** 65.32 MB - **Total amount of disk used:** 136.81 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "activity_label": "Removing ice from car", "ctx": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles. then", "ctx_a": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles.", "ctx_b": "then", "endings": "[\", the man adds wax to the windshield and cuts it.\", \", a person board a ski lift, while two men supporting the head of the per...", "ind": 4, "label": "3", "source_id": "activitynet~v_-1IBHYS3L-Y", "split": "train", "split_type": "indomain" } ``` ### Data Fields The data fields are the same among all splits. #### default - `ind`: a `int32` feature. - `activity_label`: a `string` feature. - `ctx_a`: a `string` feature. - `ctx_b`: a `string` feature. - `ctx`: a `string` feature. - `endings`: a `list` of `string` features. - `source_id`: a `string` feature. - `split`: a `string` feature. - `split_type`: a `string` feature. - `label`: a `string` feature. ### Data Splits | name |train|validation|test | |-------|----:|---------:|----:| |default|39905| 10042|10003| ## 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 MIT https://github.com/rowanz/hellaswag/blob/master/LICENSE ### Citation Information ``` @inproceedings{zellers2019hellaswag, title={HellaSwag: Can a Machine Really Finish Your Sentence?}, author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin}, booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year={2019} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

fsicoli/common_voice_15_0

fsicoli

--- license: cc language: - ab - af - am - 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 - gl - gn - ha - he - hi - hsb - hu - 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 - nl - oc - or - pl - ps - pt - quy - ro - ru - rw - sah - sat - sc - sk - skr - sl - sq - sr - sw - ta - th - ti - tig - tk - tok - tr - tt - tw - ug - uk - ur - uz - vi - vot - yue - zgh - zh - yo task_categories: - automatic-speech-recognition pretty_name: Common Voice Corpus 15.0 size_categories: - 100B<n<1T tags: - mozilla - foundation --- # Dataset Card for Common Voice Corpus 15.0 <!-- Provide a quick summary of the dataset. --> This dataset is an unofficial version of the Mozilla Common Voice Corpus 15. It was downloaded and converted from the project's website https://commonvoice.mozilla.org/. ## Languages ``` Abkhaz, Albanian, Amharic, 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, Pashto, 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), Tamazight, 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 Portuguese config, simply specify the corresponding language config name (i.e., "pt" for Portuguese): ``` from datasets import load_dataset cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", 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. ``` from datasets import load_dataset cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train", streaming=True) print(next(iter(cv_15))) ``` Bonus: create a PyTorch dataloader directly with your own datasets (local/streamed). ### Local ``` from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train") batch_sampler = BatchSampler(RandomSampler(cv_15), batch_size=32, drop_last=False) dataloader = DataLoader(cv_15, batch_sampler=batch_sampler) ``` ### Streaming ``` from datasets import load_dataset from torch.utils.data import DataLoader cv_15 = load_dataset("fsicoli/common_voice_15_0", "pt", split="train") dataloader = DataLoader(cv_15, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to hf.co/blog/audio-datasets. ### 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. ### Licensing Information Public Domain, CC-0 ### 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 } ```

mteb/mtop_domain

mteb

--- task_categories: - text-classification language: - de - en - es - fr - hi - th ---

drop

--- pretty_name: DROP annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering - text2text-generation task_ids: - extractive-qa - abstractive-qa paperswithcode_id: drop dataset_info: features: - name: section_id dtype: string - name: query_id dtype: string - name: passage dtype: string - name: question dtype: string - name: answers_spans sequence: - name: spans dtype: string - name: types dtype: string splits: - name: train num_bytes: 105572762 num_examples: 77400 - name: validation num_bytes: 11737787 num_examples: 9535 download_size: 8308692 dataset_size: 117310549 --- # Dataset Card for "drop" ## 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://allennlp.org/drop](https://allennlp.org/drop) - **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:** 8.30 MB - **Size of the generated dataset:** 110.91 MB - **Total amount of disk used:** 119.21 MB ### Dataset Summary DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs. . DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was necessary for prior datasets. ### 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:** 8.30 MB - **Size of the generated dataset:** 110.91 MB - **Total amount of disk used:** 119.21 MB An example of 'validation' looks as follows. ``` This example was too long and was cropped: { "answers_spans": { "spans": ["Chaz Schilens"] }, "passage": "\" Hoping to rebound from their loss to the Patriots, the Raiders stayed at home for a Week 16 duel with the Houston Texans. Oak...", "question": "Who scored the first touchdown of the game?" } ``` ### Data Fields The data fields are the same among all splits. #### default - `passage`: a `string` feature. - `question`: a `string` feature. - `answers_spans`: a dictionary feature containing: - `spans`: a `string` feature. ### Data Splits | name |train|validation| |-------|----:|---------:| |default|77409| 9536| ## 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{Dua2019DROP, author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner}, title={ {DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs}, booktitle={Proc. of NAACL}, year={2019} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset.

story_cloze

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: null pretty_name: Story Cloze Test dataset_info: - config_name: '2016' features: - name: story_id dtype: string - name: input_sentence_1 dtype: string - name: input_sentence_2 dtype: string - name: input_sentence_3 dtype: string - name: input_sentence_4 dtype: string - name: sentence_quiz1 dtype: string - name: sentence_quiz2 dtype: string - name: answer_right_ending dtype: int32 splits: - name: validation num_bytes: 614084 num_examples: 1871 - name: test num_bytes: 613184 num_examples: 1871 download_size: 0 dataset_size: 1227268 - config_name: '2018' features: - name: story_id dtype: string - name: input_sentence_1 dtype: string - name: input_sentence_2 dtype: string - name: input_sentence_3 dtype: string - name: input_sentence_4 dtype: string - name: sentence_quiz1 dtype: string - name: sentence_quiz2 dtype: string - name: answer_right_ending dtype: int32 splits: - name: validation num_bytes: 515439 num_examples: 1571 download_size: 0 dataset_size: 515439 --- # Dataset Card for "story_cloze" ## 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://cs.rochester.edu/nlp/rocstories/](https://cs.rochester.edu/nlp/rocstories/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Lsdsem 2017 shared task: The story cloze test](https://aclanthology.org/W17-0906.pdf) - **Point of Contact:** [Nasrin Mostafazadeh]([email protected]) - **Size of downloaded dataset files:** 2.13 MB - **Size of the generated dataset:** 2.13 MB - **Total amount of disk used:** 2.15 MB ### Dataset Summary Story Cloze Test' is a new commonsense reasoning framework for evaluating story understanding, story generation, and script learning.This test requires a system to choose the correct ending to a four-sentence story. ### Supported Tasks and Leaderboards commonsense reasoning ### Languages English ## Dataset Structure ### Data Instances - **Size of downloaded dataset files:** 2.13 MB - **Size of the generated dataset:** 2.13 MB - **Total amount of disk used:** 2.15 MB An example of 'train' looks as follows. ``` {'answer_right_ending': 1, 'input_sentence_1': 'Rick grew up in a troubled household.', 'input_sentence_2': 'He never found good support in family, and turned to gangs.', 'input_sentence_3': "It wasn't long before Rick got shot in a robbery.", 'input_sentence_4': 'The incident caused him to turn a new leaf.', 'sentence_quiz1': 'He is happy now.', 'sentence_quiz2': 'He joined a gang.', 'story_id': '138d5bfb-05cc-41e3-bf2c-fa85ebad14e2'} ``` ### Data Fields The data fields are the same among all splits. - `input_sentence_1`: The first statement in the story. - `input_sentence_2`: The second statement in the story. - `input_sentence_3`: The third statement in the story. - `input_sentence_4`: The forth statement in the story. - `sentence_quiz1`: first possible continuation of the story. - `sentence_quiz2`: second possible continuation of the story. - `answer_right_ending`: correct possible ending; either 1 or 2. - `story_id`: story id. ### Data Splits | name |validation |test| |-------|-----:|---:| |2016|1871|1871| |2018|1571|-| ## 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{mostafazadeh2017lsdsem, title={Lsdsem 2017 shared task: The story cloze test}, author={Mostafazadeh, Nasrin and Roth, Michael and Louis, Annie and Chambers, Nathanael and Allen, James}, booktitle={Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics}, pages={46--51}, year={2017} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai).

ccdv/arxiv-summarization

ccdv

--- language: - en multilinguality: - monolingual size_categories: - 100K<n<1M task_categories: - summarization - text-generation task_ids: [] tags: - conditional-text-generation train-eval-index: - config: document task: summarization task_id: summarization splits: eval_split: test col_mapping: article: text abstract: target --- # Arxiv dataset for summarization Dataset for summarization of long documents.\ Adapted from this [repo](https://github.com/armancohan/long-summarization).\ Note that original data are pre-tokenized so this dataset returns " ".join(text) and add "\n" for paragraphs. \ This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable: ```python "ccdv/arxiv-summarization": ("article", "abstract") ``` ### Data Fields - `id`: paper id - `article`: a string containing the body of the paper - `abstract`: a string containing the abstract of the paper ### Data Splits This dataset has 3 splits: _train_, _validation_, and _test_. \ Token counts are white space based. | Dataset Split | Number of Instances | Avg. tokens | | ------------- | --------------------|:----------------------| | Train | 203,037 | 6038 / 299 | | Validation | 6,436 | 5894 / 172 | | Test | 6,440 | 5905 / 174 | # Cite original article ``` @inproceedings{cohan-etal-2018-discourse, title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents", author = "Cohan, Arman and Dernoncourt, Franck and Kim, Doo Soon and Bui, Trung and Kim, Seokhwan and Chang, Walter and Goharian, Nazli", booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)", month = jun, year = "2018", address = "New Orleans, Louisiana", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/N18-2097", doi = "10.18653/v1/N18-2097", pages = "615--621", abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.", } ```

Blablablab/SOCKET

Blablablab

--- license: cc-by-4.0 --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository: https://github.com/minjechoi/SOCKET - **Paper: Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark [link](https://arxiv.org/abs/2305.14938) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This Dataset contains the tasks used in the paper "Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark" [link](https://arxiv.org/abs/2305.14938). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation This benchmark is created by aggregating several existing NLP datasets that measure different aspects of social information. ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [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 [More Information Needed] ### Citation Information @misc{choi2023llms, title={Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark}, author={Minje Choi and Jiaxin Pei and Sagar Kumar and Chang Shu and David Jurgens}, year={2023}, eprint={2305.14938}, archivePrefix={arXiv}, primaryClass={cs.CL} } ### Contributions [More Information Needed]

para_crawl

--- annotations_creators: - no-annotation language_creators: - found language: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sk - sl - sv license: - cc0-1.0 multilinguality: - translation pretty_name: ParaCrawl size_categories: - 10M<n<100M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: paracrawl dataset_info: - config_name: enbg features: - name: translation dtype: translation: languages: - en - bg splits: - name: train num_bytes: 356532771 num_examples: 1039885 download_size: 103743335 dataset_size: 356532771 - config_name: encs features: - name: translation dtype: translation: languages: - en - cs splits: - name: train num_bytes: 638068353 num_examples: 2981949 download_size: 196410022 dataset_size: 638068353 - config_name: enda features: - name: translation dtype: translation: languages: - en - da splits: - name: train num_bytes: 598624306 num_examples: 2414895 download_size: 182804827 dataset_size: 598624306 - config_name: ende features: - name: translation dtype: translation: languages: - en - de splits: - name: train num_bytes: 3997191986 num_examples: 16264448 download_size: 1307754745 dataset_size: 3997191986 - config_name: enel features: - name: translation dtype: translation: languages: - en - el splits: - name: train num_bytes: 688069020 num_examples: 1985233 download_size: 193553374 dataset_size: 688069020 - config_name: enes features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 6209466040 num_examples: 21987267 download_size: 1953839527 dataset_size: 6209466040 - config_name: enet features: - name: translation dtype: translation: languages: - en - et splits: - name: train num_bytes: 201408919 num_examples: 853422 download_size: 70158650 dataset_size: 201408919 - config_name: enfi features: - name: translation dtype: translation: languages: - en - fi splits: - name: train num_bytes: 524624150 num_examples: 2156069 download_size: 159209242 dataset_size: 524624150 - config_name: enfr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 9015440258 num_examples: 31374161 download_size: 2827554088 dataset_size: 9015440258 - config_name: enga features: - name: translation dtype: translation: languages: - en - ga splits: - name: train num_bytes: 104523278 num_examples: 357399 download_size: 29394367 dataset_size: 104523278 - config_name: enhr features: - name: translation dtype: translation: languages: - en - hr splits: - name: train num_bytes: 247646552 num_examples: 1002053 download_size: 84904103 dataset_size: 247646552 - config_name: enhu features: - name: translation dtype: translation: languages: - en - hu splits: - name: train num_bytes: 403168065 num_examples: 1901342 download_size: 119784765 dataset_size: 403168065 - config_name: enit features: - name: translation dtype: translation: languages: - en - it splits: - name: train num_bytes: 3340542050 num_examples: 12162239 download_size: 1066720197 dataset_size: 3340542050 - config_name: enlt features: - name: translation dtype: translation: languages: - en - lt splits: - name: train num_bytes: 197053694 num_examples: 844643 download_size: 66358392 dataset_size: 197053694 - config_name: enlv features: - name: translation dtype: translation: languages: - en - lv splits: - name: train num_bytes: 142409870 num_examples: 553060 download_size: 47368967 dataset_size: 142409870 - config_name: enmt features: - name: translation dtype: translation: languages: - en - mt splits: - name: train num_bytes: 52786023 num_examples: 195502 download_size: 19028352 dataset_size: 52786023 - config_name: ennl features: - name: translation dtype: translation: languages: - en - nl splits: - name: train num_bytes: 1384042007 num_examples: 5659268 download_size: 420090979 dataset_size: 1384042007 - config_name: enpl features: - name: translation dtype: translation: languages: - en - pl splits: - name: train num_bytes: 854786500 num_examples: 3503276 download_size: 270427885 dataset_size: 854786500 - config_name: enpt features: - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 2031891156 num_examples: 8141940 download_size: 638184462 dataset_size: 2031891156 - config_name: enro features: - name: translation dtype: translation: languages: - en - ro splits: - name: train num_bytes: 518359240 num_examples: 1952043 download_size: 160684751 dataset_size: 518359240 - config_name: ensk features: - name: translation dtype: translation: languages: - en - sk splits: - name: train num_bytes: 337704729 num_examples: 1591831 download_size: 101307152 dataset_size: 337704729 - config_name: ensl features: - name: translation dtype: translation: languages: - en - sl splits: - name: train num_bytes: 182399034 num_examples: 660161 download_size: 65037465 dataset_size: 182399034 - config_name: ensv features: - name: translation dtype: translation: languages: - en - sv splits: - name: train num_bytes: 875576366 num_examples: 3476729 download_size: 275528370 dataset_size: 875576366 --- # Dataset Card for "para_crawl" ## 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://paracrawl.eu/releases.html](https://paracrawl.eu/releases.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:** 10.36 GB - **Size of the generated dataset:** 32.90 GB - **Total amount of disk used:** 43.26 GB ### Dataset Summary Web-Scale Parallel Corpora for Official European Languages. ### 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 #### enbg - **Size of downloaded dataset files:** 103.75 MB - **Size of the generated dataset:** 356.54 MB - **Total amount of disk used:** 460.27 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"bg\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### encs - **Size of downloaded dataset files:** 196.41 MB - **Size of the generated dataset:** 638.07 MB - **Total amount of disk used:** 834.48 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"cs\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### enda - **Size of downloaded dataset files:** 182.81 MB - **Size of the generated dataset:** 598.62 MB - **Total amount of disk used:** 781.43 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"da\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### ende - **Size of downloaded dataset files:** 1.31 GB - **Size of the generated dataset:** 4.00 GB - **Total amount of disk used:** 5.30 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"de\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` #### enel - **Size of downloaded dataset files:** 193.56 MB - **Size of the generated dataset:** 688.07 MB - **Total amount of disk used:** 881.62 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "translation": "{\"el\": \". “A felirat faragott karnis a bejárat fölött, templom épült 14 Július 1643, A földesúr és felesége Jeremiás Murguleţ, C..." } ``` ### Data Fields The data fields are the same among all splits. #### enbg - `translation`: a multilingual `string` variable, with possible languages including `en`, `bg`. #### encs - `translation`: a multilingual `string` variable, with possible languages including `en`, `cs`. #### enda - `translation`: a multilingual `string` variable, with possible languages including `en`, `da`. #### ende - `translation`: a multilingual `string` variable, with possible languages including `en`, `de`. #### enel - `translation`: a multilingual `string` variable, with possible languages including `en`, `el`. ### Data Splits | name | train | |------|---------:| | enbg | 1039885 | | encs | 2981949 | | enda | 2414895 | | ende | 16264448 | | enel | 1985233 | ## 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 CC0 license ("no rights reserved")](https://creativecommons.org/share-your-work/public-domain/cc0/). ### Citation Information ``` @inproceedings{banon-etal-2020-paracrawl, title = "{P}ara{C}rawl: Web-Scale Acquisition of Parallel Corpora", author = "Ba{\~n}{\'o}n, Marta and Chen, Pinzhen and Haddow, Barry and Heafield, Kenneth and Hoang, Hieu and Espl{\`a}-Gomis, Miquel and Forcada, Mikel L. and Kamran, Amir and Kirefu, Faheem and Koehn, Philipp and Ortiz Rojas, Sergio and Pla Sempere, Leopoldo and Ram{\'\i}rez-S{\'a}nchez, Gema and Sarr{\'\i}as, Elsa and Strelec, Marek and Thompson, Brian and Waites, William and Wiggins, Dion and Zaragoza, Jaume", 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.417", doi = "10.18653/v1/2020.acl-main.417", pages = "4555--4567", abstract = "We report on methods to create the largest publicly available parallel corpora by crawling the web, using open source software. We empirically compare alternative methods and publish benchmark data sets for sentence alignment and sentence pair filtering. We also describe the parallel corpora released and evaluate their quality and their usefulness to create machine translation systems.", } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset.

tiny_shakespeare

--- paperswithcode_id: null pretty_name: TinyShakespeare dataset_info: features: - name: text dtype: string splits: - name: test num_bytes: 55780 num_examples: 1 - name: train num_bytes: 1003864 num_examples: 1 - name: validation num_bytes: 55780 num_examples: 1 download_size: 1115394 dataset_size: 1115424 --- # Dataset Card for "tiny_shakespeare" ## 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://github.com/karpathy/char-rnn/blob/master/data/tinyshakespeare/input.txt](https://github.com/karpathy/char-rnn/blob/master/data/tinyshakespeare/input.txt) - **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:** 1.11 MB - **Size of the generated dataset:** 1.11 MB - **Total amount of disk used:** 2.23 MB ### Dataset Summary 40,000 lines of Shakespeare from a variety of Shakespeare's plays. Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of Recurrent Neural Networks': http://karpathy.github.io/2015/05/21/rnn-effectiveness/. To use for e.g. character modelling: ``` d = datasets.load_dataset(name='tiny_shakespeare')['train'] d = d.map(lambda x: datasets.Value('strings').unicode_split(x['text'], 'UTF-8')) # train split includes vocabulary for other splits vocabulary = sorted(set(next(iter(d)).numpy())) d = d.map(lambda x: {'cur_char': x[:-1], 'next_char': x[1:]}) d = d.unbatch() seq_len = 100 batch_size = 2 d = d.batch(seq_len) d = d.batch(batch_size) ``` ### 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:** 1.11 MB - **Size of the generated dataset:** 1.11 MB - **Total amount of disk used:** 2.23 MB An example of 'train' looks as follows. ``` { "text": "First Citizen:\nBefore we proceed any further, hear me " } ``` ### Data Fields The data fields are the same among all splits. #### default - `text`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 1| 1| 1| ## 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 ``` @misc{ author={Karpathy, Andrej}, title={char-rnn}, year={2015}, howpublished={\url{https://github.com/karpathy/char-rnn}} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

mteb/amazon_reviews_multi

mteb

--- language: - de - en - es - fr - ja - zh ---

multi_news

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual pretty_name: Multi-News size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization paperswithcode_id: multi-news train-eval-index: - config: default task: summarization task_id: summarization splits: train_split: train eval_split: test col_mapping: document: text summary: target metrics: - type: rouge name: Rouge dataset_info: features: - name: document dtype: string - name: summary dtype: string splits: - name: train num_bytes: 558392265 num_examples: 44972 - name: validation num_bytes: 68272432 num_examples: 5622 - name: test num_bytes: 70032124 num_examples: 5622 download_size: 756785627 dataset_size: 696696821 --- # Dataset Card for Multi-News ## 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 - **Homepage:** [https://github.com/Alex-Fabbri/Multi-News](https://github.com/Alex-Fabbri/Multi-News) - **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:** 256.96 MB - **Size of the generated dataset:** 700.18 MB - **Total amount of disk used:** 957.14 MB ### Dataset Summary Multi-News, consists of news articles and human-written summaries of these articles from the site newser.com. Each summary is professionally written by editors and includes links to the original articles cited. There are two features: - document: text of news articles seperated by special token "|||||". - summary: news summary. ### 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:** 256.96 MB - **Size of the generated dataset:** 700.18 MB - **Total amount of disk used:** 957.14 MB An example of 'validation' looks as follows. ``` { "document": "some line val \n another line", "summary": "target val line" } ``` ### Data Fields The data fields are the same among all splits. #### default - `document`: a `string` feature. - `summary`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default|44972| 5622|5622| ## 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 ``` This Dataset Usage Agreement ("Agreement") is a legal agreement with LILY LAB for the Dataset made available to the individual or entity ("Researcher") exercising rights under this Agreement. "Dataset" includes all text, data, information, source code, and any related materials, documentation, files, media, updates or revisions. The Dataset is intended for non-commercial research and educational purposes only, and is made available free of charge without extending any license or other intellectual property rights. By downloading or using the Dataset, the Researcher acknowledges that they agree to the terms in this Agreement, and represent and warrant that they have authority to do so on behalf of any entity exercising rights under this Agreement. The Researcher accepts and agrees to be bound by the terms and conditions of this Agreement. If the Researcher does not agree to this Agreement, they may not download or use the Dataset. By sharing content with m, such as by submitting content to this site or by corresponding with LILY LAB contributors, the Researcher grants LILY LAB the right to use, reproduce, display, perform, adapt, modify, distribute, have distributed, and promote the content in any form, anywhere and for any purpose, such as for evaluating and comparing summarization systems. Nothing in this Agreement shall obligate LILY LAB to provide any support for the Dataset. Any feedback, suggestions, ideas, comments, improvements given by the Researcher related to the Dataset is voluntarily given, and may be used by LILY LAB without obligation or restriction of any kind. The Researcher accepts full responsibility for their use of the Dataset and shall defend indemnify, and hold harmless m, including their employees, trustees, officers, and agents, against any and all claims arising from the Researcher's use of the Dataset. The Researcher agrees to comply with all laws and regulations as they relate to access to and use of the Dataset and Service including U.S. export jurisdiction and other U.S. and international regulations. THE DATASET IS PROVIDED "AS IS." LILY LAB DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. WITHOUT LIMITATION OF THE ABOVE, LILY LAB DISCLAIMS ANY WARRANTY THAT DATASET IS BUG OR ERROR-FREE, AND GRANTS NO WARRANTY REGARDING ITS USE OR THE RESULTS THEREFROM INCLUDING, WITHOUT LIMITATION, ITS CORRECTNESS, ACCURACY, OR RELIABILITY. THE DATASET IS NOT WARRANTIED TO FULFILL ANY PARTICULAR PURPOSES OR NEEDS. TO THE EXTENT NOT PROHIBITED BY LAW, IN NO EVENT SHALL LILY LAB BE LIABLE FOR ANY LOSS, DAMAGE OR INJURY, DIRECT AND INDIRECT, INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER FOR BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE) OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, INCLUDING BUT NOT LIMITED TO LOSS OF PROFITS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THESE LIMITATIONS SHALL APPLY NOTWITHSTANDING ANY FAILURE OF ESSENTIAL PURPOSE OF ANY LIMITED REMEDY. This Agreement is effective until terminated. LILY LAB reserves the right to terminate the Researcher's access to the Dataset at any time. If the Researcher breaches this Agreement, the Researcher's rights to use the Dataset shall terminate automatically. The Researcher will immediately cease all use and distribution of the Dataset and destroy any copies or portions of the Dataset in their possession. This Agreement is governed by the laws of the SOME_PLACE, without regard to conflict of law principles. All terms and provisions of this Agreement shall, if possible, be construed in a manner which makes them valid, but in the event any term or provision of this Agreement is found by a court of competent jurisdiction to be illegal or unenforceable, the validity or enforceability of the remainder of this Agreement shall not be affected. This Agreement is the complete and exclusive agreement between the parties with respect to its subject matter and supersedes all prior or contemporaneous oral or written agreements or understandings relating to the subject matter. ``` ### Citation Information ``` @misc{alex2019multinews, title={Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model}, author={Alexander R. Fabbri and Irene Li and Tianwei She and Suyi Li and Dragomir R. Radev}, year={2019}, eprint={1906.01749}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

scitail

--- language: - en paperswithcode_id: scitail pretty_name: SciTail dataset_info: - config_name: snli_format features: - name: sentence1_binary_parse dtype: string - name: sentence1_parse dtype: string - name: sentence1 dtype: string - name: sentence2_parse dtype: string - name: sentence2 dtype: string - name: annotator_labels sequence: string - name: gold_label dtype: string splits: - name: train num_bytes: 22495833 num_examples: 23596 - name: test num_bytes: 2008631 num_examples: 2126 - name: validation num_bytes: 1266529 num_examples: 1304 download_size: 14174621 dataset_size: 25770993 - config_name: tsv_format features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: string splits: - name: train num_bytes: 4618115 num_examples: 23097 - name: test num_bytes: 411343 num_examples: 2126 - name: validation num_bytes: 261086 num_examples: 1304 download_size: 14174621 dataset_size: 5290544 - config_name: dgem_format features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: string - name: hypothesis_graph_structure dtype: string splits: - name: train num_bytes: 6832104 num_examples: 23088 - name: test num_bytes: 608213 num_examples: 2126 - name: validation num_bytes: 394040 num_examples: 1304 download_size: 14174621 dataset_size: 7834357 - config_name: predictor_format features: - name: answer dtype: string - name: sentence2_structure dtype: string - name: sentence1 dtype: string - name: sentence2 dtype: string - name: gold_label dtype: string - name: question dtype: string splits: - name: train num_bytes: 8884823 num_examples: 23587 - name: test num_bytes: 797161 num_examples: 2126 - name: validation num_bytes: 511305 num_examples: 1304 download_size: 14174621 dataset_size: 10193289 --- # Dataset Card for "scitail" ## 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://allenai.org/data/scitail](https://allenai.org/data/scitail) - **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:** 56.70 MB - **Size of the generated dataset:** 49.09 MB - **Total amount of disk used:** 105.79 MB ### Dataset Summary The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We crowdsource the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples with neutral label ### 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 #### dgem_format - **Size of downloaded dataset files:** 14.18 MB - **Size of the generated dataset:** 7.83 MB - **Total amount of disk used:** 22.01 MB An example of 'train' looks as follows. ``` ``` #### predictor_format - **Size of downloaded dataset files:** 14.18 MB - **Size of the generated dataset:** 10.19 MB - **Total amount of disk used:** 24.37 MB An example of 'validation' looks as follows. ``` ``` #### snli_format - **Size of downloaded dataset files:** 14.18 MB - **Size of the generated dataset:** 25.77 MB - **Total amount of disk used:** 39.95 MB An example of 'validation' looks as follows. ``` ``` #### tsv_format - **Size of downloaded dataset files:** 14.18 MB - **Size of the generated dataset:** 5.30 MB - **Total amount of disk used:** 19.46 MB An example of 'validation' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### dgem_format - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a `string` feature. - `hypothesis_graph_structure`: a `string` feature. #### predictor_format - `answer`: a `string` feature. - `sentence2_structure`: a `string` feature. - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `gold_label`: a `string` feature. - `question`: a `string` feature. #### snli_format - `sentence1_binary_parse`: a `string` feature. - `sentence1_parse`: a `string` feature. - `sentence1`: a `string` feature. - `sentence2_parse`: a `string` feature. - `sentence2`: a `string` feature. - `annotator_labels`: a `list` of `string` features. - `gold_label`: a `string` feature. #### tsv_format - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a `string` feature. ### Data Splits | name |train|validation|test| |----------------|----:|---------:|---:| |dgem_format |23088| 1304|2126| |predictor_format|23587| 1304|2126| |snli_format |23596| 1304|2126| |tsv_format |23097| 1304|2126| ## 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{scitail, Author = {Tushar Khot and Ashish Sabharwal and Peter Clark}, Booktitle = {AAAI}, Title = {{SciTail}: A Textual Entailment Dataset from Science Question Answering}, Year = {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.

mlsum

--- annotations_creators: - found language_creators: - found language: - de - es - fr - ru - tr license: - other multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - extended|cnn_dailymail - original task_categories: - summarization - translation - text-classification task_ids: - news-articles-summarization - multi-class-classification - multi-label-classification - topic-classification paperswithcode_id: mlsum pretty_name: MLSUM dataset_info: - config_name: de features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 846959840 num_examples: 220887 - name: validation num_bytes: 47119541 num_examples: 11394 - name: test num_bytes: 46847612 num_examples: 10701 download_size: 1005814154 dataset_size: 940926993 - config_name: es features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 1214558302 num_examples: 266367 - name: validation num_bytes: 50643400 num_examples: 10358 - name: test num_bytes: 71263665 num_examples: 13920 download_size: 1456211154 dataset_size: 1336465367 - config_name: fr features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 1471965014 num_examples: 392902 - name: validation num_bytes: 70413212 num_examples: 16059 - name: test num_bytes: 69660288 num_examples: 15828 download_size: 1849565564 dataset_size: 1612038514 - config_name: ru features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 257389497 num_examples: 25556 - name: validation num_bytes: 9128497 num_examples: 750 - name: test num_bytes: 9656398 num_examples: 757 download_size: 766226107 dataset_size: 276174392 - config_name: tu features: - name: text dtype: string - name: summary dtype: string - name: topic dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string splits: - name: train num_bytes: 641622783 num_examples: 249277 - name: validation num_bytes: 25530661 num_examples: 11565 - name: test num_bytes: 27830212 num_examples: 12775 download_size: 942308960 dataset_size: 694983656 config_names: - de - es - fr - ru - tu --- # Dataset Card for MLSUM ## 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:** []() - **Repository:** https://github.com/recitalAI/MLSUM - **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.647/ - **Point of Contact:** [email]([email protected]) - **Size of downloaded dataset files:** 1.83 GB - **Size of the generated dataset:** 4.86 GB - **Total amount of disk used:** 6.69 GB ### Dataset Summary We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual 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 #### de - **Size of downloaded dataset files:** 346.58 MB - **Size of the generated dataset:** 940.93 MB - **Total amount of disk used:** 1.29 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### es - **Size of downloaded dataset files:** 513.31 MB - **Size of the generated dataset:** 1.34 GB - **Total amount of disk used:** 1.85 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### fr - **Size of downloaded dataset files:** 619.99 MB - **Size of the generated dataset:** 1.61 GB - **Total amount of disk used:** 2.23 GB An example of 'validation' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### ru - **Size of downloaded dataset files:** 106.22 MB - **Size of the generated dataset:** 276.17 MB - **Total amount of disk used:** 382.39 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` #### tu - **Size of downloaded dataset files:** 247.50 MB - **Size of the generated dataset:** 694.99 MB - **Total amount of disk used:** 942.48 MB An example of 'train' looks as follows. ``` { "date": "01/01/2001", "summary": "A text", "text": "This is a text", "title": "A sample", "topic": "football", "url": "https://www.google.com" } ``` ### Data Fields The data fields are the same among all splits. #### de - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### es - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### fr - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### ru - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. #### tu - `text`: a `string` feature. - `summary`: a `string` feature. - `topic`: a `string` feature. - `url`: a `string` feature. - `title`: a `string` feature. - `date`: a `string` feature. ### Data Splits |name|train |validation|test | |----|-----:|---------:|----:| |de |220887| 11394|10701| |es |266367| 10358|13920| |fr |392902| 16059|15828| |ru | 25556| 750| 757| |tu |249277| 11565|12775| ## 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 Usage of dataset is restricted to non-commercial research purposes only. Copyright belongs to the original copyright holders. See https://github.com/recitalAI/MLSUM#mlsum ### Citation Information ``` @article{scialom2020mlsum, title={MLSUM: The Multilingual Summarization Corpus}, author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo}, journal={arXiv preprint arXiv:2004.14900}, year={2020} } ``` ### Contributions Thanks to [@RachelKer](https://github.com/RachelKer), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

nsmc

--- annotations_creators: - crowdsourced language_creators: - found language: - ko license: - cc-by-2.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: nsmc pretty_name: Naver Sentiment Movie Corpus dataset_info: features: - name: id dtype: string - name: document dtype: string - name: label dtype: class_label: names: '0': negative '1': positive splits: - name: train num_bytes: 16423803 num_examples: 150000 - name: test num_bytes: 5491417 num_examples: 50000 download_size: 19522142 dataset_size: 21915220 --- # Dataset Card for Naver sentiment movie 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:** [Github](https://github.com/e9t/nsmc/) - **Repository:** [Github](https://github.com/e9t/nsmc/) - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields Each instance is a movie review written by Korean internet users on Naver, the most commonly used search engine in Korea. Each row can be broken down into the following fields: - `id`: A unique review ID, provided by Naver - `document`: The actual movie review - `label`: Binary labels for sentiment analysis, where `0` denotes negative, and `1`, positive ### Data Splits [More Information Needed] ## 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 [More Information Needed] ### Citation Information ``` @InProceedings{Park:2016, title = "Naver Sentiment Movie Corpus", author = "Lucy Park", year = "2016", howpublished = {\\url{https://github.com/e9t/nsmc}} } ``` ### Contributions Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset.

castorini/mr-tydi-corpus

castorini

--- language: - ar - bn - en - fi - id - fi - ja - ko - ru - sw - te - th multilinguality: - multilingual task_categories: - text-retrieval license: apache-2.0 --- # Dataset Summary Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations. This dataset stores documents of Mr. TyDi. To access the queries and judgments, please refer to [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi). # Dataset Structure The only configuration here is the `language`. As all three folds (train, dev and test) share the same corpus, there is only one fold 'train' under each language, unlike [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi). An example of document data entry looks as follows: ``` { 'docid': '25#0', 'title': 'Autism', 'text': 'Autism is a developmental disorder characterized by difficulties with social interaction and communication, ...' } ``` # Load Dataset An example to load the dataset: ``` language = 'english' dataset = load_dataset('castorini/mr-tydi-corpus', language, 'train') ``` # Citation Information ``` @article{mrtydi, title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin}, year={2021}, journal={arXiv:2108.08787}, } ```

esnli

--- language: - en paperswithcode_id: e-snli pretty_name: e-SNLI dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: explanation_1 dtype: string - name: explanation_2 dtype: string - name: explanation_3 dtype: string config_name: plain_text splits: - name: test num_bytes: 3387169 num_examples: 9824 - name: train num_bytes: 108024142 num_examples: 549367 - name: validation num_bytes: 3423725 num_examples: 9842 download_size: 204516010 dataset_size: 114835036 --- # Dataset Card for "esnli" ## 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://github.com/OanaMariaCamburu/e-SNLI](https://github.com/OanaMariaCamburu/e-SNLI) - **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:** 204.51 MB - **Size of the generated dataset:** 114.84 MB - **Total amount of disk used:** 319.35 MB ### Dataset Summary The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to include human-annotated natural language explanations of the entailment relations. ### 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 #### plain_text - **Size of downloaded dataset files:** 204.51 MB - **Size of the generated dataset:** 114.84 MB - **Total amount of disk used:** 319.35 MB An example of 'validation' looks as follows. ``` { "explanation_1": "A woman must be present to smile.", "explanation_2": "A woman smiling implies that she is present.", "explanation_3": "A smiling woman is also present.", "hypothesis": "A woman is present.", "label": 0, "premise": "A woman smiles at the child." } ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2). - `explanation_1`: a `string` feature. - `explanation_2`: a `string` feature. - `explanation_3`: a `string` feature. ### Data Splits | name |train |validation|test| |----------|-----:|---------:|---:| |plain_text|549367| 9842|9824| ## 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 ``` @incollection{NIPS2018_8163, title = {e-SNLI: Natural Language Inference with Natural Language Explanations}, author = {Camburu, Oana-Maria and Rockt"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil}, booktitle = {Advances in Neural Information Processing Systems 31}, editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett}, pages = {9539--9549}, year = {2018}, publisher = {Curran Associates, Inc.}, url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

kilt_tasks

--- annotations_creators: - crowdsourced - found - machine-generated language_creators: - crowdsourced - found language: - en license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - 1M<n<10M source_datasets: - extended|natural_questions - extended|other-aidayago - extended|other-fever - extended|other-hotpotqa - extended|other-trex - extended|other-triviaqa - extended|other-wizardsofwikipedia - extended|other-wned-cweb - extended|other-wned-wiki - extended|other-zero-shot-re - original task_categories: - fill-mask - question-answering - text-classification - text-generation - text-retrieval - text2text-generation task_ids: - abstractive-qa - dialogue-modeling - document-retrieval - entity-linking-retrieval - extractive-qa - fact-checking - fact-checking-retrieval - open-domain-abstractive-qa - open-domain-qa - slot-filling paperswithcode_id: kilt pretty_name: KILT dataset_info: - config_name: triviaqa_support_only features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 72024147 num_examples: 61844 - name: validation num_bytes: 6824774 num_examples: 5359 - name: test num_bytes: 341964 num_examples: 6586 download_size: 111546348 dataset_size: 79190885 - config_name: fever features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 23941622 num_examples: 104966 - name: validation num_bytes: 3168503 num_examples: 10444 - name: test num_bytes: 1042660 num_examples: 10100 download_size: 45954548 dataset_size: 28152785 - config_name: aidayago2 features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 68944642 num_examples: 18395 - name: validation num_bytes: 20743548 num_examples: 4784 - name: test num_bytes: 14211859 num_examples: 4463 download_size: 105637528 dataset_size: 103900049 - config_name: wned features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: validation num_bytes: 12659894 num_examples: 3396 - name: test num_bytes: 13082096 num_examples: 3376 download_size: 26163472 dataset_size: 25741990 - config_name: cweb features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: validation num_bytes: 89819628 num_examples: 5599 - name: test num_bytes: 99209665 num_examples: 5543 download_size: 190444736 dataset_size: 189029293 - config_name: trex features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 1190269126 num_examples: 2284168 - name: validation num_bytes: 2573820 num_examples: 5000 - name: test num_bytes: 758742 num_examples: 5000 download_size: 1757029516 dataset_size: 1193601688 - config_name: structured_zeroshot features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 47171201 num_examples: 147909 - name: validation num_bytes: 1612499 num_examples: 3724 - name: test num_bytes: 1141537 num_examples: 4966 download_size: 74927220 dataset_size: 49925237 - config_name: nq features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 30388752 num_examples: 87372 - name: validation num_bytes: 6190493 num_examples: 2837 - name: test num_bytes: 334178 num_examples: 1444 download_size: 60166499 dataset_size: 36913423 - config_name: hotpotqa features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 33598679 num_examples: 88869 - name: validation num_bytes: 2371638 num_examples: 5600 - name: test num_bytes: 888476 num_examples: 5569 download_size: 57516638 dataset_size: 36858793 - config_name: eli5 features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 525586490 num_examples: 272634 - name: validation num_bytes: 13860153 num_examples: 1507 - name: test num_bytes: 108108 num_examples: 600 download_size: 562498660 dataset_size: 539554751 - config_name: wow features: - name: id dtype: string - name: input dtype: string - name: meta struct: - name: left_context dtype: string - name: mention dtype: string - name: right_context dtype: string - name: partial_evidence list: - name: start_paragraph_id dtype: int32 - name: end_paragraph_id dtype: int32 - name: title dtype: string - name: section dtype: string - name: wikipedia_id dtype: string - name: meta struct: - name: evidence_span list: string - name: obj_surface list: string - name: sub_surface list: string - name: subj_aliases list: string - name: template_questions list: string - name: output list: - name: answer dtype: string - name: meta struct: - name: score dtype: int32 - name: provenance list: - name: bleu_score dtype: float32 - name: start_character dtype: int32 - name: start_paragraph_id dtype: int32 - name: end_character dtype: int32 - name: end_paragraph_id dtype: int32 - name: meta struct: - name: fever_page_id dtype: string - name: fever_sentence_id dtype: int32 - name: annotation_id dtype: string - name: yes_no_answer dtype: string - name: evidence_span list: string - name: section dtype: string - name: title dtype: string - name: wikipedia_id dtype: string splits: - name: train num_bytes: 41873570 num_examples: 63734 - name: validation num_bytes: 2022128 num_examples: 3054 - name: test num_bytes: 1340818 num_examples: 2944 download_size: 52647339 dataset_size: 45236516 config_names: - aidayago2 - cweb - eli5 - fever - hotpotqa - nq - structured_zeroshot - trex - triviaqa_support_only - wned - wow --- # Dataset Card for KILT ## 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.facebook.com/tools/kilt/ - **Repository:** https://github.com/facebookresearch/KILT - **Paper:** https://arxiv.org/abs/2009.02252 - **Leaderboard:** https://eval.ai/web/challenges/challenge-page/689/leaderboard/ - **Point of Contact:** [Needs More Information] ### Dataset Summary KILT has been built from 11 datasets representing 5 types of tasks: - Fact-checking - Entity linking - Slot filling - Open domain QA - Dialog generation All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the predictions made by models, as well as the evidence they provide for their predictions. #### Loading the KILT knowledge source and task data The original KILT [release](https://github.com/facebookresearch/KILT) only provides question IDs for the TriviaQA task. Using the full dataset requires mapping those back to the TriviaQA questions, which can be done as follows: ```python from datasets import load_dataset # Get the pre-processed Wikipedia knowledge source for kild kilt_wiki = load_dataset("kilt_wikipedia") # Get the KILT task datasets kilt_triviaqa = load_dataset("kilt_tasks", name="triviaqa_support_only") # Most tasks in KILT already have all required data, but KILT-TriviaQA # only provides the question IDs, not the questions themselves. # Thankfully, we can get the original TriviaQA data with: trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext') # The KILT IDs can then be mapped to the TriviaQA questions with: triviaqa_map = {} def add_missing_data(x, trivia_qa_subset, triviaqa_map): i = triviaqa_map[x['id']] x['input'] = trivia_qa_subset[i]['question'] x['output']['original_answer'] = trivia_qa_subset[i]['answer']['value'] return x for k in ['train', 'validation', 'test']: triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(trivia_qa[k]['question_id'])]) kilt_triviaqa[k] = kilt_triviaqa[k].filter(lambda x: x['id'] in triviaqa_map) kilt_triviaqa[k] = kilt_triviaqa[k].map(add_missing_data, fn_kwargs=dict(trivia_qa_subset=trivia_qa[k], triviaqa_map=triviaqa_map)) ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure ### Data Instances An example of open-domain QA from the Natural Questions `nq` configuration looks as follows: ``` {'id': '-5004457603684974952', 'input': 'who is playing the halftime show at super bowl 2016', 'meta': {'left_context': '', 'mention': '', 'obj_surface': [], 'partial_evidence': [], 'right_context': '', 'sub_surface': [], 'subj_aliases': [], 'template_questions': []}, 'output': [{'answer': 'Coldplay', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 186, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 178, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Beyoncé', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 224, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 217, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Bruno Mars', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 1.0, 'end_character': 239, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': 229, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': 'Coldplay with special guest performers Beyoncé and Bruno Mars', 'meta': {'score': 0}, 'provenance': []}, {'answer': 'British rock group Coldplay with special guest performers Beyoncé and Bruno Mars', 'meta': {'score': 0}, 'provenance': []}, {'answer': '', 'meta': {'score': 0}, 'provenance': [{'bleu_score': 0.9657992720603943, 'end_character': 341, 'end_paragraph_id': 1, 'meta': {'annotation_id': '2430977867500315580', 'evidence_span': [], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': 'NONE'}, 'section': 'Section::::Abstract.', 'start_character': 0, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}, {'answer': '', 'meta': {'score': 0}, 'provenance': [{'bleu_score': -1.0, 'end_character': -1, 'end_paragraph_id': 1, 'meta': {'annotation_id': '-1', 'evidence_span': ['It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars', 'It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who previously had headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively.', "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars", "The Super Bowl 50 Halftime Show took place on February 7, 2016, at Levi's Stadium in Santa Clara, California as part of Super Bowl 50. It was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars,"], 'fever_page_id': '', 'fever_sentence_id': -1, 'yes_no_answer': ''}, 'section': 'Section::::Abstract.', 'start_character': -1, 'start_paragraph_id': 1, 'title': 'Super Bowl 50 halftime show', 'wikipedia_id': '45267196'}]}]} ``` ### Data Fields Examples from all configurations have the following features: - `input`: a `string` feature representing the query. - `output`: a `list` of features each containing information for an answer, made up of: - `answer`: a `string` feature representing a possible answer. - `provenance`: a `list` of features representing Wikipedia passages that support the `answer`, denoted by: - `title`: a `string` feature, the title of the Wikipedia article the passage was retrieved from. - `section`: a `string` feature, the title of the section in Wikipedia article. - `wikipedia_id`: a `string` feature, a unique identifier for the Wikipedia article. - `start_character`: a `int32` feature. - `start_paragraph_id`: a `int32` feature. - `end_character`: a `int32` feature. - `end_paragraph_id`: a `int32` feature. ### Data Splits The configurations have the following splits: | | Train | Validation | Test | | ----------- | ----------- | ----------- | ----------- | | triviaqa | 61844 | 5359 | 6586 | | fever | 104966 | 10444 | 10100 | | aidayago2 | 18395 | 4784 | 4463 | | wned | | 3396 | 3376 | | cweb | | 5599 | 5543 | | trex | 2284168 | 5000 | 5000 | | structured_zeroshot | 147909 | 3724 | 4966 | | nq | 87372 | 2837 | 1444 | | hotpotqa | 88869 | 5600 | 5569 | | eli5 | 272634 | 1507 | 600 | | wow | 94577 | 3058 | 2944 | ## 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 Cite as: ``` @inproceedings{kilt_tasks, author = {Fabio Petroni and Aleksandra Piktus and Angela Fan and Patrick S. H. Lewis and Majid Yazdani and Nicola De Cao and James Thorne and Yacine Jernite and Vladimir Karpukhin and Jean Maillard and Vassilis Plachouras and Tim Rockt{\"{a}}schel and Sebastian Riedel}, editor = {Kristina Toutanova and Anna Rumshisky and Luke Zettlemoyer and Dilek Hakkani{-}T{\"{u}}r and Iz Beltagy and Steven Bethard and Ryan Cotterell and Tanmoy Chakraborty and Yichao Zhou}, title = {{KILT:} a Benchmark for Knowledge Intensive Language Tasks}, booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, {NAACL-HLT} 2021, Online, June 6-11, 2021}, pages = {2523--2544}, publisher = {Association for Computational Linguistics}, year = {2021}, url = {https://www.aclweb.org/anthology/2021.naacl-main.200/} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset.

sst

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - sentiment-classification - sentiment-scoring paperswithcode_id: sst pretty_name: Stanford Sentiment Treebank dataset_info: - config_name: default features: - name: sentence dtype: string - name: label dtype: float32 - name: tokens dtype: string - name: tree dtype: string splits: - name: train num_bytes: 2818768 num_examples: 8544 - name: validation num_bytes: 366205 num_examples: 1101 - name: test num_bytes: 730154 num_examples: 2210 download_size: 7162356 dataset_size: 3915127 - config_name: dictionary features: - name: phrase dtype: string - name: label dtype: float32 splits: - name: dictionary num_bytes: 12121843 num_examples: 239232 download_size: 7162356 dataset_size: 12121843 - config_name: ptb features: - name: ptb_tree dtype: string splits: - name: train num_bytes: 2185694 num_examples: 8544 - name: validation num_bytes: 284132 num_examples: 1101 - name: test num_bytes: 566248 num_examples: 2210 download_size: 7162356 dataset_size: 3036074 config_names: - default - dictionary - ptb --- # Dataset Card for sst ## 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://nlp.stanford.edu/sentiment/index.html - **Repository:** [Needs More Information] - **Paper:** [Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank](https://www.aclweb.org/anthology/D13-1170/) - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. ### Supported Tasks and Leaderboards - `sentiment-scoring`: Each complete sentence is annotated with a `float` label that indicates its level of positive sentiment from 0.0 to 1.0. One can decide to use only complete sentences or to include the contributions of the sub-sentences (aka phrases). The labels for each phrase are included in the `dictionary` configuration. To obtain all the phrases in a sentence we need to visit the parse tree included with each example. In contrast, the `ptb` configuration explicitly provides all the labelled parse trees in Penn Treebank format. Here the labels are binned in 5 bins from 0 to 4. - `sentiment-classification`: We can transform the above into a binary sentiment classification task by rounding each label to 0 or 1. ### Languages The text in the dataset is in English ## Dataset Structure ### Data Instances For the `default` configuration: ``` {'label': 0.7222200036048889, 'sentence': 'Yet the act is still charming here .', 'tokens': 'Yet|the|act|is|still|charming|here|.', 'tree': '15|13|13|10|9|9|11|12|10|11|12|14|14|15|0'} ``` For the `dictionary` configuration: ``` {'label': 0.7361099720001221, 'phrase': 'still charming'} ``` For the `ptb` configuration: ``` {'ptb_tree': '(3 (2 Yet) (3 (2 (2 the) (2 act)) (3 (4 (3 (2 is) (3 (2 still) (4 charming))) (2 here)) (2 .))))'} ``` ### Data Fields - `sentence`: a complete sentence expressing an opinion about a film - `label`: the degree of "positivity" of the opinion, on a scale between 0.0 and 1.0 - `tokens`: a sequence of tokens that form a sentence - `tree`: a sentence parse tree formatted as a parent pointer tree - `phrase`: a sub-sentence of a complete sentence - `ptb_tree`: a sentence parse tree formatted in Penn Treebank-style, where each component's degree of positive sentiment is labelled on a scale from 0 to 4 ### Data Splits The set of complete sentences (both `default` and `ptb` configurations) is split into a training, validation and test set. The `dictionary` configuration has only one split as it is used for reference rather than for learning. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? Rotten Tomatoes reviewers. ### 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 ``` @inproceedings{socher-etal-2013-recursive, title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank", author = "Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher", booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", month = oct, year = "2013", address = "Seattle, Washington, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D13-1170", pages = "1631--1642", } ``` ### Contributions Thanks to [@patpizio](https://github.com/patpizio) for adding this dataset.

guardian_authorship

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification - topic-classification pretty_name: GuardianAuthorship dataset_info: - config_name: cross_topic_1 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 677054 num_examples: 112 - name: test num_bytes: 1283126 num_examples: 207 - name: validation num_bytes: 374390 num_examples: 62 download_size: 3100749 dataset_size: 2334570 - config_name: cross_genre_1 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 406144 num_examples: 63 - name: test num_bytes: 1657512 num_examples: 269 - name: validation num_bytes: 677054 num_examples: 112 download_size: 3100749 dataset_size: 2740710 - config_name: cross_topic_2 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 677054 num_examples: 112 - name: test num_bytes: 1104764 num_examples: 179 - name: validation num_bytes: 552752 num_examples: 90 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_3 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 677054 num_examples: 112 - name: test num_bytes: 927138 num_examples: 152 - name: validation num_bytes: 730378 num_examples: 117 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_4 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 374390 num_examples: 62 - name: test num_bytes: 1283126 num_examples: 207 - name: validation num_bytes: 677054 num_examples: 112 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_5 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 374390 num_examples: 62 - name: test num_bytes: 1407428 num_examples: 229 - name: validation num_bytes: 552752 num_examples: 90 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_6 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 374390 num_examples: 62 - name: test num_bytes: 1229802 num_examples: 202 - name: validation num_bytes: 730378 num_examples: 117 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_7 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 552752 num_examples: 90 - name: test num_bytes: 1104764 num_examples: 179 - name: validation num_bytes: 677054 num_examples: 112 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_8 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 552752 num_examples: 90 - name: test num_bytes: 1407428 num_examples: 229 - name: validation num_bytes: 374390 num_examples: 62 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_9 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 552752 num_examples: 90 - name: test num_bytes: 1051440 num_examples: 174 - name: validation num_bytes: 730378 num_examples: 117 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_10 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 730378 num_examples: 117 - name: test num_bytes: 927138 num_examples: 152 - name: validation num_bytes: 677054 num_examples: 112 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_11 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 730378 num_examples: 117 - name: test num_bytes: 1229802 num_examples: 202 - name: validation num_bytes: 374390 num_examples: 62 download_size: 3100749 dataset_size: 2334570 - config_name: cross_topic_12 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 730378 num_examples: 117 - name: test num_bytes: 1051440 num_examples: 174 - name: validation num_bytes: 552752 num_examples: 90 download_size: 3100749 dataset_size: 2334570 - config_name: cross_genre_2 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 406144 num_examples: 63 - name: test num_bytes: 1960176 num_examples: 319 - name: validation num_bytes: 374390 num_examples: 62 download_size: 3100749 dataset_size: 2740710 - config_name: cross_genre_3 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 406144 num_examples: 63 - name: test num_bytes: 1781814 num_examples: 291 - name: validation num_bytes: 552752 num_examples: 90 download_size: 3100749 dataset_size: 2740710 - config_name: cross_genre_4 features: - name: author dtype: class_label: names: '0': catherinebennett '1': georgemonbiot '2': hugoyoung '3': jonathanfreedland '4': martinkettle '5': maryriddell '6': nickcohen '7': peterpreston '8': pollytoynbee '9': royhattersley '10': simonhoggart '11': willhutton '12': zoewilliams - name: topic dtype: class_label: names: '0': Politics '1': Society '2': UK '3': World '4': Books - name: article dtype: string splits: - name: train num_bytes: 406144 num_examples: 63 - name: test num_bytes: 1604188 num_examples: 264 - name: validation num_bytes: 730378 num_examples: 117 download_size: 3100749 dataset_size: 2740710 --- # Dataset Card for "guardian_authorship" ## 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.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf](http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf) - **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:** 49.61 MB - **Size of the generated dataset:** 38.98 MB - **Total amount of disk used:** 88.59 MB ### Dataset Summary A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ). 2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W). 3- The same-topic/genre scenario is created by grouping all the datasts as follows. For ex., to use same_topic and split the data 60-40 use: train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", split='train[:60%]+validation[:60%]+test[:60%]') tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", split='train[-40%:]+validation[-40%:]+test[-40%:]') IMPORTANT: train+validation+test[:60%] will generate the wrong splits because the data is imbalanced * See https://huggingface.co/docs/datasets/splits.html for detailed/more examples ### 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 #### cross_genre_1 - **Size of downloaded dataset files:** 3.10 MB - **Size of the generated dataset:** 2.74 MB - **Total amount of disk used:** 5.84 MB An example of 'train' looks as follows. ``` { "article": "File 1a\n", "author": 0, "topic": 4 } ``` #### cross_genre_2 - **Size of downloaded dataset files:** 3.10 MB - **Size of the generated dataset:** 2.74 MB - **Total amount of disk used:** 5.84 MB An example of 'validation' looks as follows. ``` { "article": "File 1a\n", "author": 0, "topic": 1 } ``` #### cross_genre_3 - **Size of downloaded dataset files:** 3.10 MB - **Size of the generated dataset:** 2.74 MB - **Total amount of disk used:** 5.84 MB An example of 'validation' looks as follows. ``` { "article": "File 1a\n", "author": 0, "topic": 2 } ``` #### cross_genre_4 - **Size of downloaded dataset files:** 3.10 MB - **Size of the generated dataset:** 2.74 MB - **Total amount of disk used:** 5.84 MB An example of 'validation' looks as follows. ``` { "article": "File 1a\n", "author": 0, "topic": 3 } ``` #### cross_topic_1 - **Size of downloaded dataset files:** 3.10 MB - **Size of the generated dataset:** 2.34 MB - **Total amount of disk used:** 5.43 MB An example of 'validation' looks as follows. ``` { "article": "File 1a\n", "author": 0, "topic": 1 } ``` ### Data Fields The data fields are the same among all splits. #### cross_genre_1 - `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4). - `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4). - `article`: a `string` feature. #### cross_genre_2 - `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4). - `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4). - `article`: a `string` feature. #### cross_genre_3 - `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4). - `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4). - `article`: a `string` feature. #### cross_genre_4 - `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4). - `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4). - `article`: a `string` feature. #### cross_topic_1 - `author`: a classification label, with possible values including `catherinebennett` (0), `georgemonbiot` (1), `hugoyoung` (2), `jonathanfreedland` (3), `martinkettle` (4). - `topic`: a classification label, with possible values including `Politics` (0), `Society` (1), `UK` (2), `World` (3), `Books` (4). - `article`: a `string` feature. ### Data Splits | name |train|validation|test| |-------------|----:|---------:|---:| |cross_genre_1| 63| 112| 269| |cross_genre_2| 63| 62| 319| |cross_genre_3| 63| 90| 291| |cross_genre_4| 63| 117| 264| |cross_topic_1| 112| 62| 207| ## 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 ``` @article{article, author = {Stamatatos, Efstathios}, year = {2013}, month = {01}, pages = {421-439}, title = {On the robustness of authorship attribution based on character n-gram features}, volume = {21}, journal = {Journal of Law and Policy} } @inproceedings{stamatatos2017authorship, title={Authorship attribution using text distortion}, author={Stamatatos, Efstathios}, booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics}, volume={1} pages={1138--1149}, year={2017} } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@eltoto1219](https://github.com/eltoto1219), [@malikaltakrori](https://github.com/malikaltakrori) for adding this dataset.

allenai/qasper

allenai

--- pretty_name: QASPER annotations_creators: - expert-generated language_creators: - expert-generated language: - en language_bcp47: - en-US license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|s2orc task_categories: - question-answering task_ids: - closed-domain-qa paperswithcode_id: qasper --- # Dataset Card for Qasper ## 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) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [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://allenai.org/data/qasper](https://allenai.org/data/qasper) - **Demo:** [https://qasper-demo.apps.allenai.org/](https://qasper-demo.apps.allenai.org/) - **Paper:** [https://arxiv.org/abs/2105.03011](https://arxiv.org/abs/2105.03011) - **Blogpost:** [https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c](https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c) - **Leaderboards:** [https://paperswithcode.com/dataset/qasper](https://paperswithcode.com/dataset/qasper) ### Dataset Summary QASPER is a dataset for question answering on scientific research papers. It consists of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. ### Supported Tasks and Leaderboards - `question-answering`: The dataset can be used to train a model for Question Answering. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 33.63 Token F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/question-answering-on-qasper) - `evidence-selection`: The dataset can be used to train a model for Evidence Selection. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 39.37 F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/evidence-selection-on-qasper) ### Languages English, as it is used in research papers. ## Dataset Structure ### Data Instances A typical instance in the dataset: ``` { 'id': "Paper ID (string)", 'title': "Paper Title", 'abstract': "paper abstract ...", 'full_text': { 'paragraphs':[["section1_paragraph1_text","section1_paragraph2_text",...],["section2_paragraph1_text","section2_paragraph2_text",...]], 'section_name':["section1_title","section2_title"],...}, 'qas': { 'answers':[{ 'annotation_id': ["q1_answer1_annotation_id","q1_answer2_annotation_id"] 'answer': [{ 'unanswerable':False, 'extractive_spans':["q1_answer1_extractive_span1","q1_answer1_extractive_span2"], 'yes_no':False, 'free_form_answer':"q1_answer1", 'evidence':["q1_answer1_evidence1","q1_answer1_evidence2",..], 'highlighted_evidence':["q1_answer1_highlighted_evidence1","q1_answer1_highlighted_evidence2",..] }, { 'unanswerable':False, 'extractive_spans':["q1_answer2_extractive_span1","q1_answer2_extractive_span2"], 'yes_no':False, 'free_form_answer':"q1_answer2", 'evidence':["q1_answer2_evidence1","q1_answer2_evidence2",..], 'highlighted_evidence':["q1_answer2_highlighted_evidence1","q1_answer2_highlighted_evidence2",..] }], 'worker_id':["q1_answer1_worker_id","q1_answer2_worker_id"] },{...["question2's answers"]..},{...["question3's answers"]..}], 'question':["question1","question2","question3"...], 'question_id':["question1_id","question2_id","question3_id"...], 'question_writer':["question1_writer_id","question2_writer_id","question3_writer_id"...], 'nlp_background':["question1_writer_nlp_background","question2_writer_nlp_background",...], 'topic_background':["question1_writer_topic_background","question2_writer_topic_background",...], 'paper_read': ["question1_writer_paper_read_status","question2_writer_paper_read_status",...], 'search_query':["question1_search_query","question2_search_query","question3_search_query"...], } } ``` ### Data Fields The following is an excerpt from the dataset README: Within "qas", some fields should be obvious. Here is some explanation about the others: #### Fields specific to questions: - "nlp_background" shows the experience the question writer had. The values can be "zero" (no experience), "two" (0 - 2 years of experience), "five" (2 - 5 years of experience), and "infinity" (> 5 years of experience). The field may be empty as well, indicating the writer has chosen not to share this information. - "topic_background" shows how familiar the question writer was with the topic of the paper. The values are "unfamiliar", "familiar", "research" (meaning that the topic is the research area of the writer), or null. - "paper_read", when specified shows whether the questionwriter has read the paper. - "search_query", if not empty, is the query the question writer used to find the abstract of the paper from a large pool of abstracts we made available to them. #### Fields specific to answers Unanswerable answers have "unanswerable" set to true. The remaining answers have exactly one of the following fields being non-empty. - "extractive_spans" are spans in the paper which serve as the answer. - "free_form_answer" is a written out answer. - "yes_no" is true iff the answer is Yes, and false iff the answer is No. "evidence" is the set of paragraphs, figures or tables used to arrive at the answer. Tables or figures start with the string "FLOAT SELECTED" "highlighted_evidence" is the set of sentences the answer providers selected as evidence if they chose textual evidence. The text in the "evidence" field is a mapping from these sentences to the paragraph level. That is, if you see textual evidence in the "evidence" field, it is guaranteed to be entire paragraphs, while that is not the case with "highlighted_evidence". ### Data Splits | | Train | Valid | | ----- | ------ | ----- | | Number of papers | 888 | 281 | | Number of questions | 2593 | 1005 | | Number of answers | 2675 | 1764 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data NLP papers: The full text of the papers is extracted from [S2ORC](https://huggingface.co/datasets/s2orc) (Lo et al., 2020) #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? "The annotators are NLP practitioners, not expert researchers, and it is likely that an expert would score higher" ### 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 Crowdsourced NLP practitioners ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) ### Citation Information ``` @inproceedings{Dasigi2021ADO, title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers}, author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner}, year={2021} } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.

un_multi

--- annotations_creators: - found language_creators: - found language: - ar - de - en - es - fr - ru - zh license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: multiun pretty_name: Multilingual Corpus from United Nation Documents dataset_info: - config_name: ar-de features: - name: translation dtype: translation: languages: - ar - de splits: - name: train num_bytes: 94466397 num_examples: 165090 download_size: 21869935 dataset_size: 94466397 - config_name: ar-en features: - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 4189852369 num_examples: 9759125 download_size: 1036296368 dataset_size: 4189852369 - config_name: ar-es features: - name: translation dtype: translation: languages: - ar - es splits: - name: train num_bytes: 4509675284 num_examples: 10119379 download_size: 1101206667 dataset_size: 4509675284 - config_name: ar-fr features: - name: translation dtype: translation: languages: - ar - fr splits: - name: train num_bytes: 4516850009 num_examples: 9929567 download_size: 1109705925 dataset_size: 4516850009 - config_name: ar-ru features: - name: translation dtype: translation: languages: - ar - ru splits: - name: train num_bytes: 5932866867 num_examples: 10206243 download_size: 1261123878 dataset_size: 5932866867 - config_name: ar-zh features: - name: translation dtype: translation: languages: - ar - zh splits: - name: train num_bytes: 3781658413 num_examples: 9832293 download_size: 1009696775 dataset_size: 3781658413 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 76684549 num_examples: 162981 download_size: 19468529 dataset_size: 76684549 - config_name: de-es features: - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 80936653 num_examples: 162078 download_size: 20266591 dataset_size: 80936653 - config_name: de-fr features: - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 81888435 num_examples: 164025 download_size: 20692837 dataset_size: 81888435 - config_name: de-ru features: - name: translation dtype: translation: languages: - de - ru splits: - name: train num_bytes: 111517934 num_examples: 164792 download_size: 23507789 dataset_size: 111517934 - config_name: de-zh features: - name: translation dtype: translation: languages: - de - zh splits: - name: train num_bytes: 70534818 num_examples: 176933 download_size: 19927209 dataset_size: 70534818 - config_name: en-es features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 4128141663 num_examples: 11350967 download_size: 1123164180 dataset_size: 4128141663 - config_name: en-fr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 4678055160 num_examples: 13172019 download_size: 1355002731 dataset_size: 4678055160 - config_name: en-ru features: - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 5632662839 num_examples: 11654416 download_size: 1285801078 dataset_size: 5632662839 - config_name: en-zh features: - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 2960376046 num_examples: 9564315 download_size: 900076520 dataset_size: 2960376046 - config_name: es-fr features: - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 4454712498 num_examples: 11441889 download_size: 1195733510 dataset_size: 4454712498 - config_name: es-ru features: - name: translation dtype: translation: languages: - es - ru splits: - name: train num_bytes: 5442655730 num_examples: 10605056 download_size: 1228045966 dataset_size: 5442655730 - config_name: es-zh features: - name: translation dtype: translation: languages: - es - zh splits: - name: train num_bytes: 3223871198 num_examples: 9847770 download_size: 953250084 dataset_size: 3223871198 - config_name: fr-ru features: - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 5979879089 num_examples: 11761738 download_size: 1364307157 dataset_size: 5979879089 - config_name: fr-zh features: - name: translation dtype: translation: languages: - fr - zh splits: - name: train num_bytes: 3241098333 num_examples: 9690914 download_size: 962824881 dataset_size: 3241098333 - config_name: ru-zh features: - name: translation dtype: translation: languages: - ru - zh splits: - name: train num_bytes: 4233875537 num_examples: 9557007 download_size: 1037881127 dataset_size: 4233875537 config_names: - ar-de - ar-en - ar-es - ar-fr - ar-ru - ar-zh - de-en - de-es - de-fr - de-ru - de-zh - en-es - en-fr - en-ru - en-zh - es-fr - es-ru - es-zh - fr-ru - fr-zh - ru-zh --- # 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 - **Homepage:**[MultiUN](http://www.euromatrixplus.net/multi-unp) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This is a collection of translated documents from the United Nations. This corpus is available in all 6 official languages of the UN consisting of around 300 million words per language ### Supported Tasks and Leaderboards The underlying task is machine translation. ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## 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 [More Information Needed] ### Citation Information ``` @inproceedings{eisele-chen-2010-multiun, title = "{M}ulti{UN}: A Multilingual Corpus from United Nation Documents", author = "Eisele, Andreas and Chen, Yu", booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)", month = may, year = "2010", address = "Valletta, Malta", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/686_Paper.pdf", abstract = "This paper describes the acquisition, preparation and properties of a corpus extracted from the official documents of the United Nations (UN). This corpus is available in all 6 official languages of the UN, consisting of around 300 million words per language. We describe the methods we used for crawling, document formatting, and sentence alignment. This corpus also includes a common test set for machine translation. We present the results of a French-Chinese machine translation experiment performed on this corpus.", } ``` ``` @InProceedings{TIEDEMANN12.463, author = {J�rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, } ``` ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

pragmeval

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification pretty_name: pragmeval dataset_info: - config_name: verifiability features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': experiential '1': unverifiable '2': non-experiential - name: idx dtype: int32 splits: - name: train num_bytes: 592520 num_examples: 5712 - name: validation num_bytes: 65215 num_examples: 634 - name: test num_bytes: 251799 num_examples: 2424 download_size: 5330724 dataset_size: 909534 - config_name: emobank-arousal features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 567660 num_examples: 5470 - name: validation num_bytes: 71221 num_examples: 684 - name: test num_bytes: 69276 num_examples: 683 download_size: 5330724 dataset_size: 708157 - config_name: switchboard features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': Response Acknowledgement '1': Uninterpretable '2': Or-Clause '3': Reject '4': Statement-non-opinion '5': 3rd-party-talk '6': Repeat-phrase '7': Hold Before Answer/Agreement '8': Signal-non-understanding '9': Offers, Options Commits '10': Agree/Accept '11': Dispreferred Answers '12': Hedge '13': Action-directive '14': Tag-Question '15': Self-talk '16': Yes-No-Question '17': Rhetorical-Question '18': No Answers '19': Open-Question '20': Conventional-closing '21': Other Answers '22': Acknowledge (Backchannel) '23': Wh-Question '24': Declarative Wh-Question '25': Thanking '26': Yes Answers '27': Affirmative Non-yes Answers '28': Declarative Yes-No-Question '29': Backchannel in Question Form '30': Apology '31': Downplayer '32': Conventional-opening '33': Collaborative Completion '34': Summarize/Reformulate '35': Negative Non-no Answers '36': Statement-opinion '37': Appreciation '38': Other '39': Quotation '40': Maybe/Accept-part - name: idx dtype: int32 splits: - name: train num_bytes: 1021220 num_examples: 18930 - name: validation num_bytes: 116058 num_examples: 2113 - name: test num_bytes: 34013 num_examples: 649 download_size: 5330724 dataset_size: 1171291 - config_name: persuasiveness-eloquence features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 153946 num_examples: 725 - name: validation num_bytes: 19376 num_examples: 91 - name: test num_bytes: 18379 num_examples: 90 download_size: 5330724 dataset_size: 191701 - config_name: mrda features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': Declarative-Question '1': Statement '2': Reject '3': Or-Clause '4': 3rd-party-talk '5': Continuer '6': Hold Before Answer/Agreement '7': Assessment/Appreciation '8': Signal-non-understanding '9': Floor Holder '10': Sympathy '11': Dispreferred Answers '12': Reformulate/Summarize '13': Exclamation '14': Interrupted/Abandoned/Uninterpretable '15': Expansions of y/n Answers '16': Action-directive '17': Tag-Question '18': Accept '19': Rhetorical-question Continue '20': Self-talk '21': Rhetorical-Question '22': Yes-No-question '23': Open-Question '24': Rising Tone '25': Other Answers '26': Commit '27': Wh-Question '28': Repeat '29': Follow Me '30': Thanking '31': Offer '32': About-task '33': Reject-part '34': Affirmative Non-yes Answers '35': Apology '36': Downplayer '37': Humorous Material '38': Accept-part '39': Collaborative Completion '40': Mimic Other '41': Understanding Check '42': Misspeak Self-Correction '43': Or-Question '44': Topic Change '45': Negative Non-no Answers '46': Floor Grabber '47': Correct-misspeaking '48': Maybe '49': Acknowledge-answer '50': Defending/Explanation - name: idx dtype: int32 splits: - name: train num_bytes: 963913 num_examples: 14484 - name: validation num_bytes: 111813 num_examples: 1630 - name: test num_bytes: 419797 num_examples: 6459 download_size: 5330724 dataset_size: 1495523 - config_name: gum features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': preparation '1': evaluation '2': circumstance '3': solutionhood '4': justify '5': result '6': evidence '7': purpose '8': concession '9': elaboration '10': background '11': condition '12': cause '13': restatement '14': motivation '15': antithesis '16': no_relation - name: idx dtype: int32 splits: - name: train num_bytes: 270401 num_examples: 1700 - name: validation num_bytes: 35405 num_examples: 259 - name: test num_bytes: 40334 num_examples: 248 download_size: 5330724 dataset_size: 346140 - config_name: emergent features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': observing '1': for '2': against - name: idx dtype: int32 splits: - name: train num_bytes: 313257 num_examples: 2076 - name: validation num_bytes: 38948 num_examples: 259 - name: test num_bytes: 38842 num_examples: 259 download_size: 5330724 dataset_size: 391047 - config_name: persuasiveness-relevance features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 153158 num_examples: 725 - name: validation num_bytes: 19663 num_examples: 91 - name: test num_bytes: 18880 num_examples: 90 download_size: 5330724 dataset_size: 191701 - config_name: persuasiveness-specificity features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 106594 num_examples: 504 - name: validation num_bytes: 13766 num_examples: 62 - name: test num_bytes: 12712 num_examples: 62 download_size: 5330724 dataset_size: 133072 - config_name: persuasiveness-strength features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 79679 num_examples: 371 - name: validation num_bytes: 10052 num_examples: 46 - name: test num_bytes: 10225 num_examples: 46 download_size: 5330724 dataset_size: 99956 - config_name: emobank-dominance features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 660303 num_examples: 6392 - name: validation num_bytes: 86802 num_examples: 798 - name: test num_bytes: 83319 num_examples: 798 download_size: 5330724 dataset_size: 830424 - config_name: squinky-implicature features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 471552 num_examples: 3724 - name: validation num_bytes: 58087 num_examples: 465 - name: test num_bytes: 56549 num_examples: 465 download_size: 5330724 dataset_size: 586188 - config_name: sarcasm features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': notsarc '1': sarc - name: idx dtype: int32 splits: - name: train num_bytes: 2177332 num_examples: 3754 - name: validation num_bytes: 257834 num_examples: 469 - name: test num_bytes: 269724 num_examples: 469 download_size: 5330724 dataset_size: 2704890 - config_name: squinky-formality features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 459721 num_examples: 3622 - name: validation num_bytes: 59921 num_examples: 453 - name: test num_bytes: 58242 num_examples: 452 download_size: 5330724 dataset_size: 577884 - config_name: stac features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': Comment '1': Contrast '2': Q_Elab '3': Parallel '4': Explanation '5': Narration '6': Continuation '7': Result '8': Acknowledgement '9': Alternation '10': Question_answer_pair '11': Correction '12': Clarification_question '13': Conditional '14': Sequence '15': Elaboration '16': Background '17': no_relation - name: idx dtype: int32 splits: - name: train num_bytes: 645969 num_examples: 11230 - name: validation num_bytes: 71400 num_examples: 1247 - name: test num_bytes: 70451 num_examples: 1304 download_size: 5330724 dataset_size: 787820 - config_name: pdtb features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': Synchrony '1': Contrast '2': Asynchronous '3': Conjunction '4': List '5': Condition '6': Pragmatic concession '7': Restatement '8': Pragmatic cause '9': Alternative '10': Pragmatic condition '11': Pragmatic contrast '12': Instantiation '13': Exception '14': Cause '15': Concession - name: idx dtype: int32 splits: - name: train num_bytes: 2968638 num_examples: 12907 - name: validation num_bytes: 276997 num_examples: 1204 - name: test num_bytes: 235851 num_examples: 1085 download_size: 5330724 dataset_size: 3481486 - config_name: persuasiveness-premisetype features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': testimony '1': warrant '2': invented_instance '3': common_knowledge '4': statistics '5': analogy '6': definition '7': real_example - name: idx dtype: int32 splits: - name: train num_bytes: 122631 num_examples: 566 - name: validation num_bytes: 15920 num_examples: 71 - name: test num_bytes: 14395 num_examples: 70 download_size: 5330724 dataset_size: 152946 - config_name: squinky-informativeness features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 464855 num_examples: 3719 - name: validation num_bytes: 60447 num_examples: 465 - name: test num_bytes: 56872 num_examples: 464 download_size: 5330724 dataset_size: 582174 - config_name: persuasiveness-claimtype features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': Value '1': Fact '2': Policy - name: idx dtype: int32 splits: - name: train num_bytes: 31259 num_examples: 160 - name: validation num_bytes: 3803 num_examples: 20 - name: test num_bytes: 3717 num_examples: 19 download_size: 5330724 dataset_size: 38779 - config_name: emobank-valence features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': low '1': high - name: idx dtype: int32 splits: - name: train num_bytes: 539652 num_examples: 5150 - name: validation num_bytes: 62809 num_examples: 644 - name: test num_bytes: 66178 num_examples: 643 download_size: 5330724 dataset_size: 668639 config_names: - emergent - emobank-arousal - emobank-dominance - emobank-valence - gum - mrda - pdtb - persuasiveness-claimtype - persuasiveness-eloquence - persuasiveness-premisetype - persuasiveness-relevance - persuasiveness-specificity - persuasiveness-strength - sarcasm - squinky-formality - squinky-implicature - squinky-informativeness - stac - switchboard - verifiability --- # Dataset Card for pragmeval ## 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 - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@sileod](https://github.com/sileod) for adding this dataset.

Open-Orca/SlimOrca

Open-Orca

--- language: - en license: mit task_categories: - conversational - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - summarization - feature-extraction - text-generation - text2text-generation pretty_name: SlimOrca size_categories: - 100K<n<1M --- # Overview This is a new curated subset of our OpenOrca data. This release provides an efficient means of reaching performance on-par with using larger slices of our data, while only including ~500k GPT-4 completions. The key change in this dataset is that we've done an additional pass, using GPT-4 to remove answers which appear wrong based on the human annotations from the FLAN dataset. This reduces the dataset size to only ~500k entries, allowing training to a similar quality level to our previous releases with 2/3 the compute requirement. # Demo Models * https://huggingface.co/openaccess-ai-collective/jackalope-7b * https://huggingface.co/Open-Orca/Mistral-7B-SlimOrca # Citation ```bibtex @misc{SlimOrca, title = {SlimOrca: An Open Dataset of GPT-4 Augmented FLAN Reasoning Traces, with Verification}, author = {Wing Lian and Guan Wang and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"}, year = {2023}, publisher = {HuggingFace}, url = {https://https://huggingface.co/Open-Orca/SlimOrca} } ``` ```bibtex @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```

ncbi_disease

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: ncbi-disease-1 pretty_name: NCBI Disease dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-Disease '2': I-Disease config_name: ncbi_disease splits: - name: train num_bytes: 2355516 num_examples: 5433 - name: validation num_bytes: 413900 num_examples: 924 - name: test num_bytes: 422842 num_examples: 941 download_size: 1546492 dataset_size: 3192258 train-eval-index: - config: ncbi_disease task: token-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: tokens: text ner_tags: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for NCBI Disease ## Table of Contents - [Dataset Card for NCBI Disease](#dataset-card-for-ncbi-disease) - [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) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [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:** [NCBI](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease) - **Repository:** [Github](https://github.com/spyysalo/ncbi-disease) - **Paper:** [NCBI disease corpus: A resource for disease name recognition and concept normalization](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655) - **Leaderboard:** [Named Entity Recognition on NCBI-disease](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ncbi-disease) - **Point of Contact:** [email]([email protected]) ### Dataset Summary This dataset contains the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. ### Supported Tasks and Leaderboards Named Entity Recognition: [Leaderboard](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ncbi-disease) ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances Instances of the dataset contain an array of `tokens`, `ner_tags` and an `id`. An example of an instance of the dataset: ``` { 'tokens': ['Identification', 'of', 'APC2', ',', 'a', 'homologue', 'of', 'the', 'adenomatous', 'polyposis', 'coli', 'tumour', 'suppressor', '.'], 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0], 'id': '0' } ``` ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens. ### Data Splits The data is split into a train (5433 instances), validation (924 instances) and test set (941 instances). ## Dataset Creation ### Curation Rationale The goal of the dataset consists on improving the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks. ### Source Data #### Initial Data Collection and Normalization The dataset consists on abstracts extracted from PubMed. #### Who are the source language producers? The source language producers are the authors of publication abstracts hosted in PubMed. ### Annotations #### Annotation process Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency. #### Who are the annotators? The annotator group consisted of 14 people with backgrounds in biomedical informatics research and experience in biomedical text corpus annotation. ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information. This dataset provides an annotated corpora that can be used to develop highly effective tools to automatically detect central biomedical concepts such as diseases. ### Discussion of Biases To avoid annotator bias, pairs of annotators were chosen randomly for each set, so that each pair of annotators overlapped for at most two sets. ### Other Known Limitations A handful of disease concepts were discovered that were not included in MEDIC. For those, we decided to include the appropriate OMIM identifiers. In addition, certain disease mentions were found to not be easily represented using the standard categorizations. Also, each PMID document was pre-annotated using the Inference Method developed for disease name normalization, which properly handles abbreviation recognition, robust string matching, etc. As such, human annotators were given the pre-annotated documents as a starting point and allowed to see each pre-annotation with a computed confidence. ## Additional Information ### Dataset Curators Rezarta Islamaj Doğan, Robert Leaman, Zhiyong Lu ### Licensing Information ``` PUBLIC DOMAIN NOTICE This work is a "United States Government Work" under the terms of the United States Copyright Act. It was written as part of the authors' official duties as a United States Government employee and thus cannot be copyrighted within the United States. The data is freely available to the public for use. The National Library of Medicine and the U.S. Government have not placed any restriction on its use or reproduction. Although all reasonable efforts have been taken to ensure the accuracy and reliability of the data and its source code, the NLM and the U.S. Government do not and cannot warrant the performance or results that may be obtained by using it. The NLM and the U.S. Government disclaim all warranties, express or implied, including warranties of performance, merchantability or fitness for any particular purpose. Please cite the authors in any work or product based on this material: An improved corpus of disease mentions in PubMed citations http://aclweb.org/anthology-new/W/W12/W12-2411.pdf NCBI Disease Corpus: A Resource for Disease Name Recognition and Normalization http://www.ncbi.nlm.nih.gov/pubmed/24393765 Disease Name Normalization with Pairwise Learning to Rank http://www.ncbi.nlm.nih.gov/pubmed/23969135 ``` ### Citation Information ``` @article{dougan2014ncbi, title={NCBI disease corpus: a resource for disease name recognition and concept normalization}, author={Do{\u{g}}an, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong}, journal={Journal of biomedical informatics}, volume={47}, pages={1--10}, year={2014}, publisher={Elsevier} } ``` ### Contributions Thanks to [@edugp](https://github.com/edugp) for adding this dataset.

tyqiangz/multilingual-sentiments

tyqiangz

--- language: - de - en - es - fr - ja - zh - id - ar - hi - it - ms - pt license: apache-2.0 multilinguality: - monolingual - multilingual size_categories: - 100K<n<1M - 1M<n<10M task_categories: - text-classification task_ids: - sentiment-analysis - sentiment-classification --- # Multilingual Sentiments Dataset A collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative. Most multilingual sentiment datasets are either 2-class positive or negative, 5-class ratings of products reviews (e.g. Amazon multilingual dataset) or multiple classes of emotions. However, to an average person, sometimes positive, negative and neutral classes suffice and are more straightforward to perceive and annotate. Also, a positive/negative classification is too naive, most of the text in the world is actually neutral in sentiment. Furthermore, most multilingual sentiment datasets don't include Asian languages (e.g. Malay, Indonesian) and are dominated by Western languages (e.g. English, German). Git repo: https://github.com/tyqiangz/multilingual-sentiment-datasets ## Dataset Description - **Webpage:** https://github.com/tyqiangz/multilingual-sentiment-datasets

lama

--- pretty_name: 'LAMA: LAnguage Model Analysis' annotations_creators: - crowdsourced - expert-generated - machine-generated language_creators: - crowdsourced - expert-generated - machine-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K - 1K<n<10K - 1M<n<10M - n<1K source_datasets: - extended|conceptnet5 - extended|squad task_categories: - text-retrieval - text-classification task_ids: - fact-checking-retrieval - text-scoring paperswithcode_id: lama tags: - probing dataset_info: - config_name: trex features: - name: uuid dtype: string - name: obj_uri dtype: string - name: obj_label dtype: string - name: sub_uri dtype: string - name: sub_label dtype: string - name: predicate_id dtype: string - name: sub_surface dtype: string - name: obj_surface dtype: string - name: masked_sentence dtype: string - name: template dtype: string - name: template_negated dtype: string - name: label dtype: string - name: description dtype: string - name: type dtype: string splits: - name: train num_bytes: 656913189 num_examples: 1304391 download_size: 74652201 dataset_size: 656913189 - config_name: squad features: - name: id dtype: string - name: sub_label dtype: string - name: obj_label dtype: string - name: negated dtype: string - name: masked_sentence dtype: string splits: - name: train num_bytes: 57188 num_examples: 305 download_size: 74639115 dataset_size: 57188 - config_name: google_re features: - name: pred dtype: string - name: sub dtype: string - name: obj dtype: string - name: evidences dtype: string - name: judgments dtype: string - name: sub_w dtype: string - name: sub_label dtype: string - name: sub_aliases dtype: string - name: obj_w dtype: string - name: obj_label dtype: string - name: obj_aliases dtype: string - name: uuid dtype: string - name: masked_sentence dtype: string - name: template dtype: string - name: template_negated dtype: string splits: - name: train num_bytes: 7638657 num_examples: 6106 download_size: 74639115 dataset_size: 7638657 - config_name: conceptnet features: - name: uuid dtype: string - name: sub dtype: string - name: obj dtype: string - name: pred dtype: string - name: obj_label dtype: string - name: masked_sentence dtype: string - name: negated dtype: string splits: - name: train num_bytes: 4130000 num_examples: 29774 download_size: 74639115 dataset_size: 4130000 config_names: - conceptnet - google_re - squad - trex --- # Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models. ## 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://github.com/facebookresearch/LAMA - **Repository:** https://github.com/facebookresearch/LAMA - **Paper:** @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} } @inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} } ### Dataset Summary This dataset provides the data for LAMA. The dataset include a subset of Google_RE (https://code.google.com/archive/p/relation-extraction-corpus/), TRex (subset of wikidata triples), Conceptnet (https://github.com/commonsense/conceptnet5/wiki) and Squad. There are configs for each of "google_re", "trex", "conceptnet" and "squad", respectively. The dataset includes some cleanup, and addition of a masked sentence and associated answers for the [MASK] token. The accuracy in predicting the [MASK] token shows how well the language model knows facts and common sense information. The [MASK] tokens are only for the "object" slots. This version of the dataset includes "negated" sentences as well as the masked sentence. Also, certain of the config includes "template" and "template_negated" fields of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively of certain relations. See the paper for more details. For more information, also see: https://github.com/facebookresearch/LAMA ### Languages en ## Dataset Structure ### Data Instances The trex config has the following fields: `` {'description': 'the item (an institution, law, public office ...) or statement belongs to or has power over or applies to the value (a territorial jurisdiction: a country, state, municipality, ...)', 'label': 'applies to jurisdiction', 'masked_sentence': 'It is known as a principality as it is a monarchy headed by two Co-Princes – the Spanish/Roman Catholic Bishop of Urgell and the President of [MASK].', 'obj_label': 'France', 'obj_surface': 'France', 'obj_uri': 'Q142', 'predicate_id': 'P1001', 'sub_label': 'president of the French Republic', 'sub_surface': 'President', 'sub_uri': 'Q191954', 'template': '[X] is a legal term in [Y] .', 'template_negated': '[X] is not a legal term in [Y] .', 'type': 'N-M', 'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'} `` The conceptnet config has the following fields: `` {'masked_sentence': 'One of the things you do when you are alive is [MASK].', 'negated': '', 'obj': 'think', 'obj_label': 'think', 'pred': 'HasSubevent', 'sub': 'alive', 'uuid': 'd4f11631dde8a43beda613ec845ff7d1'} `` The squad config has the following fields: `` {'id': '56be4db0acb8001400a502f0_0', 'masked_sentence': 'To emphasize the 50th anniversary of the Super Bowl the [MASK] color was used.', 'negated': "['To emphasize the 50th anniversary of the Super Bowl the [MASK] color was not used.']", 'obj_label': 'gold', 'sub_label': 'Squad'} `` The google_re config has the following fields: `` {'evidences': '[{\'url\': \'http://en.wikipedia.org/wiki/Peter_F._Martin\', \'snippet\': "Peter F. Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives. He has represented the 75th District Newport since 6 January 2009. He is currently serves on the House Committees on Judiciary, Municipal Government, and Veteran\'s Affairs. During his first term of office he served on the House Committees on Small Business and Separation of Powers & Government Oversight. In August 2010, Representative Martin was appointed as a Commissioner on the Atlantic States Marine Fisheries Commission", \'considered_sentences\': [\'Peter F Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives .\']}]', 'judgments': "[{'rater': '18349444711114572460', 'judgment': 'yes'}, {'rater': '17595829233063766365', 'judgment': 'yes'}, {'rater': '4593294093459651288', 'judgment': 'yes'}, {'rater': '7387074196865291426', 'judgment': 'yes'}, {'rater': '17154471385681223613', 'judgment': 'yes'}]", 'masked_sentence': 'Peter F Martin (born [MASK]) is an American politician who is a Democratic member of the Rhode Island House of Representatives .', 'obj': '1941', 'obj_aliases': '[]', 'obj_label': '1941', 'obj_w': 'None', 'pred': '/people/person/date_of_birth', 'sub': '/m/09gb0bw', 'sub_aliases': '[]', 'sub_label': 'Peter F. Martin', 'sub_w': 'None', 'template': '[X] (born [Y]).', 'template_negated': '[X] (not born [Y]).', 'uuid': '18af2dac-21d3-4c42-aff5-c247f245e203'} `` ### Data Fields The trex config has the following fields: * uuid: the id * obj_uri: a uri for the object slot * obj_label: a label for the object slot * sub_uri: a uri for the subject slot * sub_label: a label for the subject slot * predicate_id: the predicate/relationship * sub_surface: the surface text for the subject * obj_surface: The surface text for the object. This is the word that should be predicted by the [MASK] token. * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. template may be missing and replaced with an empty string. * template_negated: Same as above, except the [Y] is not the object. template_negated may be missing and replaced with empty strings. * label: the label for the relationship/predicate. label may be missing and replaced with an empty string. * description': a description of the relationship/predicate. description may be missing and replaced with an empty string. * type: a type id for the relationship/predicate. type may be missing and replaced with an empty string. The conceptnet config has the following fields: * uuid: the id * sub: the subject. subj may be missing and replaced with an empty string. * obj: the object to be predicted. obj may be missing and replaced with an empty string. * pred: the predicate/relationship * obj_label: the object label * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings. The squad config has the following fields: * id: the id * sub_label: the subject label * obj_label: the object label that is being predicted * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings. The google_re config has the following fields: * uuid: the id * pred: the predicate * sub: the subject. subj may be missing and replaced with an empty string. * obj: the object. obj may be missing and replaced with an empty string. * evidences: flattened json string that provides evidence for predicate. parse this json string to get more 'snippet' information. * judgments: data about judgments * sub_q: unknown * sub_label: label for the subject * sub_aliases: unknown * obj_w: unknown * obj_label: label for the object * obj_aliases: unknown * masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK] * template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. * template_negated: Same as above, except the [Y] is not the object. ### Data Splits There are no data splits. ## Dataset Creation ### Curation Rationale This dataset was gathered and created to probe what language models understand. ### Source Data #### Initial Data Collection and Normalization See the reaserch paper and website for more detail. The dataset was created gathered from various other datasets with cleanups for probing. #### Who are the source language producers? The LAMA authors and the original authors of the various configs. ### Annotations #### Annotation process Human annotations under the original datasets (conceptnet), and various machine annotations. #### Who are the annotators? Human annotations and machine annotations. ### Personal and Sensitive Information Unkown, but likely names of famous people. ## Considerations for Using the Data ### Social Impact of Dataset The goal for the work is to probe the understanding of language models. ### Discussion of Biases Since the data is from human annotators, there is likely to be baises. [More Information Needed] ### Other Known Limitations The original documentation for the datafields are limited. ## Additional Information ### Dataset Curators The authors of LAMA at Facebook and the authors of the original datasets. ### Licensing Information The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE ### Citation Information @inproceedings{petroni2019language, title={Language Models as Knowledge Bases?}, author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel}, booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019}, year={2019} } @inproceedings{petroni2020how, title={How Context Affects Language Models' Factual Predictions}, author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel}, booktitle={Automated Knowledge Base Construction}, year={2020}, url={https://openreview.net/forum?id=025X0zPfn} } ### Contributions Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset.

amphora/haerae_bench

amphora

--- dataset_info: features: - name: query dtype: string - name: o1 dtype: string - name: o2 dtype: string - name: o3 dtype: string - name: o4 dtype: string - name: o5 dtype: string - name: gold dtype: int64 - name: split dtype: string splits: - name: test num_bytes: 658698 num_examples: 1538 download_size: 333543 dataset_size: 658698 --- # Dataset Card for "HaeRae_Bench" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

JeanKaddour/minipile

JeanKaddour

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5906108510 num_examples: 1000000 - name: validation num_bytes: 2779386 num_examples: 500 - name: test num_bytes: 58558191 num_examples: 10000 download_size: 3177432813 dataset_size: 5967446087 annotations_creators: - no-annotation language_creators: - found language: - en license: other multilinguality: - monolingual pretty_name: MiniPile size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: minipile --- # Dataset Card for MiniPile ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description [The MiniPile Challenge for Data-Efficient Language Models](https://arxiv.org/abs/2304.08442) ### Dataset Summary MiniPile is a 6GB subset of the [deduplicated The Pile corpus](https://huggingface.co/datasets/EleutherAI/the_pile_deduplicated). To curate MiniPile, we perform a simple, three-step data filtering process: we (1) infer embeddings for all documents of the Pile, (2) cluster the embedding space using k-means, and (3) filter out low-quality clusters. The primary motivation for curating MiniPile is that (i) diverse pre-training datasets (like the Pile) are often too large for academic budgets and (ii) most smaller-scale datasets are fairly homogeneous and thereby unrepresentative of contemporary general-purpose language models. MiniPile aims to fill this gap and thereby facilitate data-efficient research on model architectures, training procedures, optimizers, etc. More details on the MiniPile curation procedure and some pre-training results be found in the [MiniPile paper](https://arxiv.org/abs/2304.08442). For more details on the Pile corpus, we refer the reader to [the Pile datasheet](https://arxiv.org/abs/2201.07311). ### Languages English (`EN`) ## Additional Information ### Dataset Curators MiniPile is a subset of the Pile, curated by Jean Kaddour. The Pile was created by Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, Connor Leahy. ### Licensing Information Since MiniPile is a subset of the Pile, the same MIT License holds. ### Citation Information ``` @article{kaddour2023minipile, title={The MiniPile Challenge for Data-Efficient Language Models}, author={Kaddour, Jean}, journal={arXiv preprint arXiv:2304.08442}, year={2023} } @article{gao2020pile, title={The {P}ile: An 800{GB} dataset of diverse text for language modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ```

GATE-engine/automated_cardiac_diagnosis_competition.ACDC

GATE-engine

--- dataset_info: features: - name: four_d_img sequence: sequence: sequence: sequence: float32 - name: frame_data list: - name: img sequence: sequence: sequence: float32 - name: label sequence: sequence: sequence: int64 splits: - name: train num_bytes: 7089368208 num_examples: 100 - name: test num_bytes: 3489827928 num_examples: 50 download_size: 363153048 dataset_size: 10579196136 --- # Dataset Card for "automated_cardiac_diagnosis_competition.ACDC" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

speechcolab/gigaspeech

speechcolab

--- annotations_creators: [] language_creators: [] language: - en license: - apache-2.0 multilinguality: - monolingual pretty_name: Gigaspeech source_datasets: [] task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio extra_gated_prompt: >- SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through the Hub under certain conditions and terms. Terms of Access: The "Researcher" has requested permission to use the GigaSpeech database (the "Database") at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, 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 audio files that he or she may create from the Database. 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. 5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time. 6. 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. !!! Please also fill out the Google Form https://forms.gle/UuGQAPyscGRrUMLq6 to request access to the Gigaspeech dataset. extra_gated_fields: Name: text Email: text Organization: text Address: text I hereby confirm that I have requested access via the Google Form provided above: checkbox I accept the terms of access: checkbox --- # Dataset Card for Gigaspeech ## 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) - [Terms of Access](#terms-of-access) ## Dataset Description - **Homepage:** https://github.com/SpeechColab/GigaSpeech - **Repository:** https://github.com/SpeechColab/GigaSpeech - **Paper:** https://arxiv.org/abs/2106.06909 - **Leaderboard:** https://github.com/SpeechColab/GigaSpeech#leaderboard - **Point of Contact:** [[email protected]](mailto:[email protected]) ## Dataset Description GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training. The transcribed audio data is collected from audiobooks, podcasts and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, sports, etc. ### Example Usage The training split has several configurations of various size: XS, S, M, L, XL. See the Section on "Data Splits" for more information. To download the XS configuration: ```python from datasets import load_dataset gs = load_dataset("speechcolab/gigaspeech", "xs", use_auth_token=True) # see structure print(gs) # load audio sample on the fly audio_input = gs["train"][0]["audio"] # first decoded audio sample transcription = gs["train"][0]["text"] # first transcription ``` It is possible to download only the development or test data: ```python gs_dev = load_dataset("speechcolab/gigaspeech", "dev", use_auth_token=True) gs_test = load_dataset("speechcolab/gigaspeech", "test", use_auth_token=True) ``` ### Supported Tasks and Leaderboards - `automatic-speech-recognition`: 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://github.com/SpeechColab/GigaSpeech#leaderboard 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 Gigaspeech contains audio and transcription data in English. ## Dataset Structure ### Data Instances ```python { 'segment_id': 'YOU0000000315_S0000660', 'speaker': 'N/A', 'text': "AS THEY'RE LEAVING <COMMA> CAN KASH PULL ZAHRA ASIDE REALLY QUICKLY <QUESTIONMARK>", 'audio': { # in streaming mode 'path' will be 'xs_chunks_0000/YOU0000000315_S0000660.wav' 'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/9d48cf31/xs_chunks_0000/YOU0000000315_S0000660.wav', 'array': array([0.0005188 , 0.00085449, 0.00012207, ..., 0.00125122, 0.00076294, 0.00036621], dtype=float32), 'sampling_rate': 16000 }, 'begin_time': 2941.889892578125, 'end_time': 2945.070068359375, 'audio_id': 'YOU0000000315', 'title': 'Return to Vasselheim | Critical Role: VOX MACHINA | Episode 43', 'url': 'https://www.youtube.com/watch?v=zr2n1fLVasU', 'source': 2, 'category': 24, 'original_full_path': 'audio/youtube/P0004/YOU0000000315.opus' } ``` ### Data Fields * segment_id (string) - string id of the segment. * speaker (string) - string id of the speaker (can be "N/A"). * text (string) - transcription of the segment. * begin_time (float) - start time of the segment in an original full audio. * end_time (float32) - end time of the segment in an original full audio. * audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path point to the locally extracted audio. In streaming mode, the path is the relative path of an audio. segment inside its archive (as files are not downloaded and extracted locally). * audio_id (string) - string idea of the original full audio. * title (string) - title of the original full audio. * url (string) - url of the original full audio. * source (ClassLabel) - id of the audio source. Sources are audiobook (0), podcast (1), and YouYube (2). * category (ClassLabel) - id of the audio category, categories are listed below. * original_full_path (string) - the relative path to the original full audio sample in the original data directory. Categories are assigned from the following labels: "People and Blogs", "Business", "Nonprofits and Activism", "Crime", "History", "Pets and Animals", "News and Politics", "Travel and Events", "Kids and Family", "Leisure", "N/A", "Comedy", "News and Politics", "Sports", "Arts", "Science and Technology", "Autos and Vehicles", "Science and Technology", "People and Blogs", "Music", "Society and Culture", "Education", "Howto and Style", "Film and Animation", "Gaming", "Entertainment", "Travel and Events", "Health and Fitness", "audiobook". ### Data Splits The dataset has three splits: train, evaluation (dev) and test. The train split has five configurations of various sizes: XS, S, M, L, XL. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset. #### Transcribed Training Subsets Size | Subset | Hours | Remarks | |:---------------:|:-------------:|:-------------| | XS | 10 | System building and debugging | | S | 250 | Quick research experiments | | M | 1,000 | Large-scale research experiments | | L | 2,500 | Medium-scale industrial experiments | | XL | 10,000 | Large-scale industrial experiments | #### Transcribed Evaluation Subsets | Subset | Hours | Remarks | |:------:|:-----:|:--------| | Dev | 12 | Randomly selected from the crawled Podcast and YouTube Data | | Test | 40 | Part of the subset was randomly selected from the crawled Podcast and YouTube data; part of it was manually collected through other channels to have better coverage. | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data | Audio Source | Transcribed Hours | Acoustic Condition | |:-------------|:----------------------:|:-------------------| | Audiobook | 2,655 | <li>Reading</li><li>Various ages and accents</li> | | Podcast | 3,498 | <li>Clean or background music</li><li>Indoor</li><li>Near-field</li><li>Spontaneous</li><li>Various ages and accents</li>| | YouTube | 3,845 | <li>Clean and noisy</li><li>Indoor and outdoor</li><li>Near- and far-field</li><li>Reading and spontaneous</li><li>Various ages and accents</li> | | ***Total*** | ***10,000*** || #### 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? Development and test subsets are annotated by professional human annotators. ### 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 SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through our site under certain conditions and terms. In general, when training a machine learning model on a given dataset, the license of the model is **independent** to that of the dataset. That is to say, speech recognition models trained on the GigaSpeech dataset may be eligible for commercial license, provided they abide to the 'Fair Use' terms of the underlying data and do not violate any explicit copyright restrictions. This is likely to be true in most use-cases. However, it is your responsiblity to verify the appropriate model license for your specific use-case by confirming that the dataset usage abides by the Fair Use terms. SpeechColab is not responsible for the license of any machine learning model trained on the GigaSpeech dataset. ### Citation Information Please cite this paper if you find this work useful: ```bibtext @inproceedings{GigaSpeech2021, title={GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio}, booktitle={Proc. Interspeech 2021}, year=2021, author={Guoguo Chen, Shuzhou Chai, Guanbo Wang, Jiayu Du, Wei-Qiang Zhang, Chao Weng, Dan Su, Daniel Povey, Jan Trmal, Junbo Zhang, Mingjie Jin, Sanjeev Khudanpur, Shinji Watanabe, Shuaijiang Zhao, Wei Zou, Xiangang Li, Xuchen Yao, Yongqing Wang, Yujun Wang, Zhao You, Zhiyong Yan} } ``` ### Contributions Thanks to [@polinaeterna](https://github.com/polinaeterna) and [@sanchit-gandhi](https://github.com/sanchit-gandhi) for adding this dataset. ## Terms of Access The "Researcher" has requested permission to use the GigaSpeech database (the "Database") at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, 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 audio files that he or she may create from the Database. 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. 5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time. 6. 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.

katanaml-org/invoices-donut-data-v1

katanaml-org

--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 234024421 num_examples: 425 - name: test num_bytes: 14512665 num_examples: 26 - name: validation num_bytes: 27661738 num_examples: 50 download_size: 197512750 dataset_size: 276198824 license: mit task_categories: - feature-extraction language: - en pretty_name: Sparrow Invoice Dataset size_categories: - n<1K --- # Dataset Card for Invoices (Sparrow) This dataset contains 500 invoice documents annotated and processed to be ready for Donut ML model fine-tuning. Annotation and data preparation task was done by [Katana ML](https://www.katanaml.io) team. [Sparrow](https://github.com/katanaml/sparrow/tree/main) - open-source data extraction solution by Katana ML. Original dataset [info](https://data.mendeley.com/datasets/tnj49gpmtz): Kozłowski, Marek; Weichbroth, Paweł (2021), “Samples of electronic invoices”, Mendeley Data, V2, doi: 10.17632/tnj49gpmtz.2

big_patent

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - summarization task_ids: [] paperswithcode_id: bigpatent pretty_name: Big Patent tags: - patent-summarization dataset_info: - config_name: all features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 38367048389 num_examples: 1207222 - name: validation num_bytes: 2115827002 num_examples: 67068 - name: test num_bytes: 2129505280 num_examples: 67072 download_size: 10142923776 dataset_size: 42612380671 - config_name: a features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 5683460620 num_examples: 174134 - name: validation num_bytes: 313324505 num_examples: 9674 - name: test num_bytes: 316633277 num_examples: 9675 download_size: 10142923776 dataset_size: 6313418402 - config_name: b features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4236070976 num_examples: 161520 - name: validation num_bytes: 234425138 num_examples: 8973 - name: test num_bytes: 231538734 num_examples: 8974 download_size: 10142923776 dataset_size: 4702034848 - config_name: c features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 4506249306 num_examples: 101042 - name: validation num_bytes: 244684775 num_examples: 5613 - name: test num_bytes: 252566793 num_examples: 5614 download_size: 10142923776 dataset_size: 5003500874 - config_name: d features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 264717412 num_examples: 10164 - name: validation num_bytes: 14560482 num_examples: 565 - name: test num_bytes: 14403430 num_examples: 565 download_size: 10142923776 dataset_size: 293681324 - config_name: e features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 881101433 num_examples: 34443 - name: validation num_bytes: 48646158 num_examples: 1914 - name: test num_bytes: 48586429 num_examples: 1914 download_size: 10142923776 dataset_size: 978334020 - config_name: f features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 2146383473 num_examples: 85568 - name: validation num_bytes: 119632631 num_examples: 4754 - name: test num_bytes: 119596303 num_examples: 4754 download_size: 10142923776 dataset_size: 2385612407 - config_name: g features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8877854206 num_examples: 258935 - name: validation num_bytes: 492581177 num_examples: 14385 - name: test num_bytes: 496324853 num_examples: 14386 download_size: 10142923776 dataset_size: 9866760236 - config_name: h features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 8075621958 num_examples: 257019 - name: validation num_bytes: 447602356 num_examples: 14279 - name: test num_bytes: 445460513 num_examples: 14279 download_size: 10142923776 dataset_size: 8968684827 - config_name: y features: - name: description dtype: string - name: abstract dtype: string splits: - name: train num_bytes: 3695589005 num_examples: 124397 - name: validation num_bytes: 200369780 num_examples: 6911 - name: test num_bytes: 204394948 num_examples: 6911 download_size: 10142923776 dataset_size: 4100353733 config_names: - a - all - b - c - d - e - f - g - h - y --- # Dataset Card for Big Patent ## 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:** [Big Patent](https://evasharma.github.io/bigpatent/) - **Repository:** - **Paper:** [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741) - **Leaderboard:** - **Point of Contact:** [Lu Wang](mailto:[email protected]) ### Dataset Summary BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Each US patent application is filed under a Cooperative Patent Classification (CPC) code. There are nine such classification categories: - a: Human Necessities - b: Performing Operations; Transporting - c: Chemistry; Metallurgy - d: Textiles; Paper - e: Fixed Constructions - f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting - g: Physics - h: Electricity - y: General tagging of new or cross-sectional technology Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes: ```python from datasets import load_dataset ds = load_dataset("big_patent") # default is 'all' CPC codes ds = load_dataset("big_patent", "all") # the same as above ds = load_dataset("big_patent", "a") # only 'a' CPC codes ds = load_dataset("big_patent", codes=["a", "b"]) ``` To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`: ```python ds = load_dataset("big_patent", codes="all", version="1.0.0") ds = load_dataset("big_patent", codes="a", version="1.0.0") ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0") ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section. ``` { 'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...', 'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...' } ``` ### Data Fields - `description`: detailed description of patent. - `abstract`: Patent abastract. ### Data Splits | | train | validation | test | |:----|------------------:|-------------:|-------:| | all | 1207222 | 67068 | 67072 | | a | 174134 | 9674 | 9675 | | b | 161520 | 8973 | 8974 | | c | 101042 | 5613 | 5614 | | d | 10164 | 565 | 565 | | e | 34443 | 1914 | 1914 | | f | 85568 | 4754 | 4754 | | g | 258935 | 14385 | 14386 | | h | 257019 | 14279 | 14279 | | y | 124397 | 6911 | 6911 | ## 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 [More Information Needed] ### Citation Information ```bibtex @article{DBLP:journals/corr/abs-1906-03741, author = {Eva Sharma and Chen Li and Lu Wang}, title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent Summarization}, journal = {CoRR}, volume = {abs/1906.03741}, year = {2019}, url = {http://arxiv.org/abs/1906.03741}, eprinttype = {arXiv}, eprint = {1906.03741}, timestamp = {Wed, 26 Jun 2019 07:14:58 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.

yahoo_answers_topics

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - extended|other-yahoo-answers-corpus task_categories: - text-classification task_ids: - topic-classification pretty_name: YahooAnswersTopics dataset_info: features: - name: id dtype: int32 - name: topic dtype: class_label: names: '0': Society & Culture '1': Science & Mathematics '2': Health '3': Education & Reference '4': Computers & Internet '5': Sports '6': Business & Finance '7': Entertainment & Music '8': Family & Relationships '9': Politics & Government - name: question_title dtype: string - name: question_content dtype: string - name: best_answer dtype: string config_name: yahoo_answers_topics splits: - name: train num_bytes: 760460695 num_examples: 1400000 - name: test num_bytes: 32661362 num_examples: 60000 download_size: 319476345 dataset_size: 793122057 train-eval-index: - config: yahoo_answers_topics task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: question_content: text topic: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for "Yahoo Answers Topics" ## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]() - **Repository:** https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset - **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]() - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]() ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## 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? [More Information Needed] ### Annotations [More Information Needed] #### 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

tau/zero_scrolls

tau

--- language: - en task_categories: - question-answering - summarization - text-generation task_ids: - multiple-choice-qa tags: - query-based-summarization - long-texts --- ## Dataset Description - **Homepage:** [ZeroSCROLLS](https://www.zero.scrolls-benchmark.com/) - **Leaderboard:** [Leaderboard](https://www.zero.scrolls-benchmark.com/leaderboard) - **Point of Contact:** [[email protected]]([email protected]) # Dataset Card for ZeroSCROLLS ## Overview ZeroSCROLLS is a zero-shot benchmark for natural language understanding over long texts. The validation sets contain only ~20 examples per task and are meant for eyeballing alone. ## Leaderboard The ZeroSCROLLS benchmark leaderboard can be found [here](https://www.zero.scrolls-benchmark.com/leaderboard). ## Tasks ZeroSCROLLS contains 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., 2022](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. #### SQuALITY ([Wang et al., 2022](https://arxiv.org/pdf/2205.11465.pdf)) SQuALITY (Wang et al., 2022) is a question-focused summarization dataset, where given a story from Project Gutenberg, the task is to produce a summary of the story or aspects of it based on a guiding question. The questions and summaries are original and crowdsourced; experienced writers were guided to design questions that require reading significant parts of the story to answer correctly. #### 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. #### 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). #### QuALITY ([Pang et al., 2022](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. #### MuSiQue ([Trivedi et al., 2022](https://arxiv.org/pdf/2108.00573.pdf)) MuSiQue is a multi-hop question answering dataset, where the inputs are 20 Wikipedia paragraphs and a question that requires multiple hops between different paragraphs. In the original dataset, each question also has an unanswerable twin question, where the correct answer is not present in the paragraphs. #### SpaceDigest (New) SpaceDigest is a new sentiment aggregation task. Given 50 hotel reviews (without their ratings) from the Space dataset (Angelidis et al., 2021), the task is to determine the percentage of positive reviews. #### BookSumSort (New) BookSumSort is a new task based on the BookSum dataset (Kry ́sci ́nski et al., 2022), which contains summaries of chapters (or parts) of novels, plays, and long poems from various sources. Given a shuffled list of chapter summaries, the task is to reorder them according to the original order of summaries in BookSum. ## Data Fields Most datasets in the benchmark are in the same input-output format - `input`: a `string` feature. The input document. - `output`: this feature is always None, as ZeroSCROLLS contains only test sets. - `id`: a `string` feature. Unique per input. - `pid`: a `string` feature, identical to 'id`. Facilitates evaluating tasks with multiple refrences per input. - `document_start_index`: an `int32` feature. Character index that enables easy parsing of the context document. - `document_end_index`: an `int32` feature. Character index that enables easy parsing of the context document. - `query_start_index`: an `int32` feature. Character index that enables easy parsing of the query, if exists. - `query_end_index`: an `int32` feature. Character index that enables easy parsing of the query, if exists. - `truncation_seperator`: a `string` feature. The string used to append to a trimmed context document, mentioning the context was trimmed. Datasets containing multiple documents inside the `input` feature are MuSiQue, SpaceDigest, and BookSumSort. They also have the following feature: - `inner_docs_start_indices`: a sequence of `int32` feature. Character indexes that enables easy parsing of the the inner documents, e.g. Reviews, of Summaries. ## Citation If you use the ZeroSCROLLS data, **please make sure to cite all of the original dataset papers.** [[bibtex](https://zero-scrolls-tau.s3.us-east-2.amazonaws.com/zero_scrolls_datasets.bib)] ``` @misc{shaham2023zeroscrolls, title={ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding}, author={Uri Shaham and Maor Ivgi and Avia Efrat and Jonathan Berant and Omer Levy}, year={2023}, eprint={2305.14196}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

GBaker/MedQA-USMLE-4-options

GBaker

--- license: cc-by-4.0 language: - en --- Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large) <h4>Citation information:</h4> @article{jin2020disease, title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={arXiv preprint arXiv:2009.13081}, year={2020} }

deepset/prompt-injections

deepset

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 71720 num_examples: 546 - name: test num_bytes: 15981 num_examples: 116 download_size: 51215 dataset_size: 87701 license: cc-by-4.0 --- # Dataset Card for "deberta-v3-base-injection-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

c-s-ale/alpaca-gpt4-data

c-s-ale

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 40178951 num_examples: 52002 download_size: 24027484 dataset_size: 40178951 license: cc-by-4.0 language: - en pretty_name: Instruction Tuning with GPT-4 size_categories: - 10K<n<100K task_categories: - text-generation tags: - gpt - alpaca - fine-tune - instruct-tune - instruction --- # Dataset Description - **Project Page:** https://instruction-tuning-with-gpt-4.github.io - **Repo:** https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM - **Paper:** https://arxiv.org/abs/2304.03277 # Dataset Card for "alpaca-gpt4-data" All of the work is done by [this team](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM). # Usage and License Notices The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. # Chinese Dataset [Found here](https://huggingface.co/datasets/c-s-ale/alpaca-gpt4-data-zh) # Citation ``` @article{peng2023gpt4llm, title={Instruction Tuning with GPT-4}, author={Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao}, journal={arXiv preprint arXiv:2304.03277}, year={2023} } ```

stanfordnlp/SHP

stanfordnlp

--- task_categories: - text-generation - question-answering tags: - human feedback - rlhf - preferences - reddit - preference model - RL - NLG - evaluation size_categories: - 100K<n<1M language: - en --- # 🚢 Stanford Human Preferences Dataset (SHP) **If you mention this dataset in a paper, please cite the paper:** [Understanding Dataset Difficulty with V-Usable Information (ICML 2022)](https://proceedings.mlr.press/v162/ethayarajh22a.html). ## Summary SHP is a dataset of **385K collective human preferences** over responses to questions/instructions in 18 different subject areas, from cooking to legal advice. The preferences are meant to reflect the helpfulness of one response over another, and are intended to be used for training RLHF reward models and NLG evaluation models (e.g., [SteamSHP](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-xl)). Each example is a Reddit post with a question/instruction and a pair of top-level comments for that post, where one comment is more preferred by Reddit users (collectively). SHP exploits the fact that if comment A was written *after* comment B but has a higher score nonetheless, then A is ostensibly more preferred to B. If A had been written before B, then we could not conclude this, since its higher score could have been the result of more visibility. We chose data where the preference label is intended to reflect which response is more *helpful* rather than which is less *harmful*, the latter being the focus of much past work. How is SHP different from [Anthropic's HH-RLHF dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf)? Most notably, all the data in SHP is naturally occurring and human-written, whereas the responses in HH-RLHF are machine-written, giving us two very different distributions that can complement each other. | Dataset | Size | Input | Label | Domains | Data Format | Length | | -------------------- | ---- | -------------------------- | ---------------------------- | ------------------------- | ------------------------------------- | --------------- | | SHP | 385K | Naturally occurring human-written responses | Collective Human Preference | 18 (labelled) | Question/Instruction + Response (Single-turn) | up to 10.1K T5 tokens | | HH-RLHF | 91K | Dialogue with LLM | Individual Human Preference | not labelled | Live Chat (Multi-turn) | up to 1.5K T5 tokens | How is SHP different from other datasets that have scraped Reddit, like [ELI5](https://huggingface.co/datasets/eli5#source-data)? SHP uses the timestamp information to infer preferences, while ELI5 only provides comments and scores -- the latter are not enough to infer preferences since comments made earlier tend to get higher scores from more visibility. It also contains data from more domains: | Dataset | Size | Comments + Scores | Preferences | Number of Domains | | -------------------- | ---- | ------------------ | -------------| ------------------ | | SHP | 385K | Yes | Yes | 18 | | ELI5 | 270K | Yes | No | 3 | ## Data Structure There are 18 directories, one for each subreddit, and each directory contains a JSONL file for the training, validation, and test data. Here's how to get the data using Huggingface's `datasets` library: ```python from datasets import load_dataset # Load all the data dataset = load_dataset("stanfordnlp/shp") # Load one of the subreddits dataset = load_dataset("stanfordnlp/shp", data_dir="askculinary") ``` Here's an example from `askculinary/train.json`: ``` { `post_id`:"qt3nxl", `domain`:"askculinary_train", `upvote_ratio`:0.98, `history`:"What's the best way to disassemble raspberries? Like this, but down to the individual seeds: https:\/\/i.imgur.com\/Z0c6ZKE.jpg I've been pulling them apart with tweezers and it's really time consuming. I have about 10 pounds to get through this weekend.", `c_root_id_A`:"hkh25sc", `c_root_id_B`:"hkh25lp", `created_at_utc_A`:1636822112, `created_at_utc_B`:1636822110, `score_A`:340, `score_B`:166, `human_ref_A`:"Pectinex, perhaps? It's an enzyme that breaks down cellulose. With citrus, you let it sit in a dilute solution of pectinex overnight to break down the connective tissues. You end up with perfect citrus supremes. If you let the raspberries sit for a shorter time, I wonder if it would separate the seeds the same way...? Here's an example: https:\/\/www.chefsteps.com\/activities\/perfect-citrus-supreme", `human_ref_B`:"Raspberry juice will make a bright stain at first, but in a matter of weeks it will start to fade away to almost nothing. It is what is known in the natural dye world as a fugitive dye, it will fade even without washing or exposure to light. I hope she gets lots of nice photos of these stains on her dress, because soon that will be all she has left of them!", `labels`:1, `seconds_difference`:2.0, `score_ratio`:2.0481927711 } ``` where the fields are: - ```post_id```: the ID of the Reddit post (string) - ```domain```: the subreddit and split the example is drawn from, separated by an underscore (string) - ```upvote_ratio```: the percent of votes received by the post that were positive (aka upvotes) (float) - ```history```: the post title concatented to the post body (string) - ```c_root_id_A```: the ID of comment A (string) - ```c_root_id_B```: the ID of comment B (string) - ```created_at_utc_A```: utc timestamp of when comment A was created (integer) - ```created_at_utc_B```: utc timestamp of when comment B was created (integer) - ```score_A```: (# positive votes - # negative votes + 1) received by comment A (integer) - ```score_B```: (# positive votes - # negative votes + 1) received by comment B (integer) - ```human_ref_A```: text of comment A (string) - ```human_ref_B```: text of comment B (string) - ```labels```: the preference label -- it is 1 if A is preferred to B; 0 if B is preferred to A. This was randomized such that the label distribution is roughly 50/50. (integer) - ```seconds_difference```: how many seconds after the less preferred comment the more preferred one was created (will always be >= 0) (integer) - ```score_ratio```: the ratio of the more preferred comment's score to the less preferred comment's score (will be >= 1) (float) ## Dataset Design ### Domain Selection The data is sourced from Reddit, which is a public forum organized into topic-specific fora called *subreddits*. For example, the `askculinary` subreddit is where users ask cooking-related questions and are answered by other users. SHP contains a train, validation, and test split for comments scraped from 18 different subreddits. We chose subreddits based on: 1. whether they were well-known (subscriber count >= 100K) 2. whether posts were expected to pose a question or instruction 3. whether responses were valued based on how *helpful* they were 4. whether comments had to be rooted in some objectivity, instead of being entirely about personal experiences (e.g., `askscience` vs. `AskAmericans`) The train/validation/test splits were created by splitting the post IDs of a subreddit in 90%/5%/5% proportions respectively, so that no post would appear in multiple splits. Since different posts have different numbers of comments, the number of preferences in each split is not exactly 90%/5%/5%: | subreddit | train | validation | test | total | | ------------------ | -------: | ---------: | ---: | ----: | | askacademia | 31450 | 2095 | 1708 | 35253 | | askanthropology | 3910 | 203 | 268 | 4381 | | askbaking | 44007 | 2096 | 1544 | 47647 | | askcarguys | 3227 | 159 | 117 | 3503 | | askculinary | 45710 | 2094 | 2563 | 50367 | | askdocs | 6449 | 315 | 455 | 7219 | | askengineers | 57096 | 3154 | 2638 | 62888 | | askhistorians | 3264 | 113 | 164 | 3541 | | askhr | 8295 | 641 | 395 | 9331 | | askphilosophy | 10307 | 608 | 677 | 11592 | | askphysics | 7364 | 409 | 587 | 8360 | | askscience | 13316 | 899 | 977 | 15192 | | asksciencefiction | 29382 | 1576 | 1987 | 32945 | | asksocialscience | 2706 | 147 | 188 | 3041 | | askvet | 3300 | 170 | 224 | 3694 | | changemyview | 38173 | 1637 | 1836 | 41646 | | explainlikeimfive | 19592 | 1014 | 1070 | 21676 | | legaladvice | 21170 | 1106 | 1011 | 23287 | | ALL | 348718 | 18436 | 18409 | 385563 | ### Data Selection The score of a post/comment is 1 plus the number of upvotes (approvals) it gets from users, minus the number of downvotes (disapprovals) it gets. The value of a score is relative; in subreddits(posts) with more traffic, there will be more higher-scoring posts(comments). Within a post, comments posted earlier will tend to have a higher score simply due to having more exposure, which is why using timestamp information is essential when inferring preferences. Given a post P and two comments (A,B) we only included the preference A > B in the dataset if 1. A was written *no later than* B and A has a higher score than B. 2. The post is a self-post (i.e., a body of text and not a link to another page) made before 2023, was not edited, and is not NSFW (over 18). 3. Neither comment was made by a deleted user, a moderator, or the post creator. The post was not made by a deleted user or moderator. 4. The post has a score >= 10 and each comment has a score >= 2 (upvoted at least once). A post with `n` comments could have up to (`n` choose `2`) preferences in the data. Since the number of comments per post is Pareto-distributed, to prevent a relatively small number of posts from dominating the data, we limited the scraping to 50 comments per post. This means that each post could have up to (`50` choose `2`) comments in the dataset, though this is a much smaller number in practice, since all the criteria above need to be met. Reddit makes it very difficult to get anything beyond the top 1000 posts for each subreddit. We started with the top-scoring 1000 posts (of all time) and searched for the 25 most similar posts to each one using Reddit's search function to get up to 7500 unique post IDs per subreddit. ### Preprocessing We tried to keep preprocessing to a minimum. Subreddit-specific abbreviations were expanded (e.g., "CMV" to "Change my view that"). In hyperlinks, only the referring text was kept and the URL was removed (if the URL was written out, then it was kept). ## Building a Preference Model ### Finetuning If you want to finetune a model to predict human preferences (e.g., for NLG evaluation or an RLHF reward model), here are some helpful tips: 1. **Preprocess the data.** The total input length should fit under the model's token limit (usually 512 tokens). Although models like FLAN-T5 use positional embeddings, we found that the loss would not converge if we finetuned it on inputs over 512 tokens. To avoid this, truncate the post text (in the `history` field) as much as possible, such that the whole input is under 512 tokens (do not truncate the comment(s) however). If this is still over 512 tokens, simply skip the example. 2. **Use a sufficiently large model.** Finetuning a single FLAN-T5-xl model across all the training data should give you a test accuracy between 72-73% (across all domains on examples where the entire input fits within the token limit), ranging from 65-80% on individual subreddits. 3. **Do in-domain prediction.** Out-of-domain performance will be poor if the subreddits are unrelated (e.g., if you fine-tune on `askculinary` preferences and test on `askcarguys` preferences). 4. **Train for fewer epochs.** The InstructGPT paper paper suggests training a reward model for only 1 epoch. Since the same comment appears in multiple preferences, it is easy to overfit to the data. 5. **Training on less data may help.** Preferences with a large `score_ratio` (e.g., comment A having 2x the score of comment B) will provide a stronger signal for finetuning the model, so you may only want to consider preferences above a certain `score_ratio`. The number of preferences per post is Pareto-distributed, so to prevent the model from over-fitting to certain posts, you may want to limit the number of preferences from a particular post. ### Evaluating Since it is easier to predict strongly-held preferences than weakly-held ones, instead of reporting a single accuracy value, we recommend reporting a performance curve as a function of the `score_ratio`. For example, here is the accuracy curve for a FLAN-T5-xl model trained on the askculinary data using the suggestions above. The orange line is from finetuning only on preferences with a 2+ score ratio and using no more than 5 preferences from each post to prevent overfitting:  We see that finetuning on less -- but higher quality -- data leads to higher accuracies on test data with a score ratio below 3.5, with no real downsides! Note that any examples whose inputs did not fit within the token limit were left out of the experiment, since the model could not be expected to handle them. ### SteamSHP - An Open-Source Preference Model We have finetuned two FLAN-T5 models on both the SHP dataset and the helpfulness data from Anthropic's HH-RLHF. They are - [SteamSHP-XL](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-xl), a 3B parameter model that achieves 72.8% on the test data. - [SteamSHP-Large](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-large), a 780M parameter model that achieves 72.0% on the test data. We encourage you to use SteamSHP for NLG evaluation, for building reward models for RLHF, or for another purpose you deem fit! ## Biases and Limitations ### Biases Although we filtered out posts with NSFW (over 18) content, chose subreddits that were well-moderated and had policies against harassment and bigotry, some of the data may contain discriminatory or harmful language. The data does not reflect the views of the dataset creators. Reddit users on these subreddits are also not representative of the broader population. Although subreddit-specific demographic information is not available, Reddit users overall are disproportionately male and from developed, Western, and English-speaking countries ([Pew Research](https://www.pewresearch.org/internet/2013/07/03/6-of-online-adults-are-reddit-users/)). Please keep this in mind before using any models trained on this data. ### Limitations The preference label in SHP is intended to reflect how *helpful* one response is relative to another, given an instruction/question. SHP is not intended for use in harm-minimization, as it was not designed to include the toxic content that would be necessary to learn a good toxicity detector. If you are looking for data where the preference label denotes less harm, we would recommend the harmfulness split of [Anthropic's HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf). Another limitation is that the more preferred response in SHP is not necessarily the more factual one. Though some comments do provide citations to justify their response, most do not. There are exceptions to this, such as the `askhistorians` subreddit, which is heavily moderated and answers are expected to provide citations. Note that the collective preference label in SHP is not necessarily what we would get if we asked users to independently vote on each comment before taking an unweighted sum. This is because comment scores on Reddit are public and are known to influence user preferences; a high score increases the likelihood of getting more positive votes [(Muchnik et al., 2013)](https://pubmed.ncbi.nlm.nih.gov/23929980/). Whether this "herding effect" temporarily or permanently shifts a user's preference is unclear. Therefore, while SHP does reflect collective human preferences, models trained on SHP may not generalize to settings where individual preferences are aggregated differently (e.g., users vote independently without ever seeing the current comment score, users vote after conferring, etc.). Thanks to Greg Stoddard for pointing this out. ## License Last updated: 03/01/2023 This dataset was made by scraping Reddit in accordance with the [Reddit API Terms of Use](https://docs.google.com/a/reddit.com/forms/d/e/1FAIpQLSezNdDNK1-P8mspSbmtC2r86Ee9ZRbC66u929cG2GX0T9UMyw/viewform), without any direct communication or written agreements with Reddit. According to the Terms of Use, "User Content" is owned by the users themselves -- not by Reddit -- and Reddit grants a "non-exclusive, non-transferable, non-sublicensable, and revocable license to copy and display the User Content". Datasets made by scraping Reddit are widely used in the research community: for example, Facebook AI Research used data scraped from Reddit to make the [ELI5](https://huggingface.co/datasets/eli5#source-data) dataset in 2019, which was made available without a license. Anthropic AI has also [attested to scraping Reddit](https://arxiv.org/pdf/2112.00861.pdf) for preferences using a different methodology, though this data was not made public. The [PushShift Reddit dataset](https://arxiv.org/abs/2001.08435), which makes entire dumps of Reddit available on a regular schedule, is also made available without a license (to our knowledge). We take no responsibility for and we do not expressly or implicitly endorse any downstream use of this dataset. We reserve the right to modify the SHP dataset and this license at any point in the future. ## Contact Please contact [email protected] if you have any questions about the data. This dataset was created by Kawin Ethayarajh, Heidi (Chenyu) Zhang, Yizhong Wang, and Dan Jurafsky. ## Citation SHP was created using the techniques proposed in the following paper. Please cite this work if you use SHP or the SteamSHP models: ``` @InProceedings{pmlr-v162-ethayarajh22a, title = {Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information}, author = {Ethayarajh, Kawin and Choi, Yejin and Swayamdipta, Swabha}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {5988--6008}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, } ``` ## References Ethayarajh, K., Choi, Y. &amp; Swayamdipta, S. (2022). Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information. <i>Proceedings of the 39th International Conference on Machine Learning</i>, in <i>Proceedings of Machine Learning Research</i>. 162:5988-6008 Available from https://proceedings.mlr.press/v162/ethayarajh22a.html.

large_spanish_corpus

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - es license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M - 100M<n<1B - 10K<n<100K - 10M<n<100M - 1M<n<10M source_datasets: - original task_categories: - other task_ids: [] paperswithcode_id: null pretty_name: The Large Spanish Corpus tags: [] dataset_info: - config_name: JRC features: - name: text dtype: string splits: - name: train num_bytes: 380895504 num_examples: 3410620 download_size: 4099166669 dataset_size: 380895504 - config_name: EMEA features: - name: text dtype: string splits: - name: train num_bytes: 100259598 num_examples: 1221233 download_size: 4099166669 dataset_size: 100259598 - config_name: GlobalVoices features: - name: text dtype: string splits: - name: train num_bytes: 114435784 num_examples: 897075 download_size: 4099166669 dataset_size: 114435784 - config_name: ECB features: - name: text dtype: string splits: - name: train num_bytes: 336285757 num_examples: 1875738 download_size: 4099166669 dataset_size: 336285757 - config_name: DOGC features: - name: text dtype: string splits: - name: train num_bytes: 898279656 num_examples: 10917053 download_size: 4099166669 dataset_size: 898279656 - config_name: all_wikis features: - name: text dtype: string splits: - name: train num_bytes: 3782280549 num_examples: 28109484 download_size: 4099166669 dataset_size: 3782280549 - config_name: TED features: - name: text dtype: string splits: - name: train num_bytes: 15858148 num_examples: 157910 download_size: 4099166669 dataset_size: 15858148 - config_name: multiUN features: - name: text dtype: string splits: - name: train num_bytes: 2327269369 num_examples: 13127490 download_size: 4099166669 dataset_size: 2327269369 - config_name: Europarl features: - name: text dtype: string splits: - name: train num_bytes: 359897865 num_examples: 2174141 download_size: 4099166669 dataset_size: 359897865 - config_name: NewsCommentary11 features: - name: text dtype: string splits: - name: train num_bytes: 48350573 num_examples: 288771 download_size: 4099166669 dataset_size: 48350573 - config_name: UN features: - name: text dtype: string splits: - name: train num_bytes: 23654590 num_examples: 74067 download_size: 4099166669 dataset_size: 23654590 - config_name: EUBookShop features: - name: text dtype: string splits: - name: train num_bytes: 1326861077 num_examples: 8214959 download_size: 4099166669 dataset_size: 1326861077 - config_name: ParaCrawl features: - name: text dtype: string splits: - name: train num_bytes: 1840430234 num_examples: 15510649 download_size: 4099166669 dataset_size: 1840430234 - config_name: OpenSubtitles2018 features: - name: text dtype: string splits: - name: train num_bytes: 7477281776 num_examples: 213508602 download_size: 4099166669 dataset_size: 7477281776 - config_name: DGT features: - name: text dtype: string splits: - name: train num_bytes: 396217351 num_examples: 3168368 download_size: 4099166669 dataset_size: 396217351 - config_name: combined features: - name: text dtype: string splits: - name: train num_bytes: 19428257807 num_examples: 302656160 download_size: 4099166669 dataset_size: 19428257807 config_names: - DGT - DOGC - ECB - EMEA - EUBookShop - Europarl - GlobalVoices - JRC - NewsCommentary11 - OpenSubtitles2018 - ParaCrawl - TED - UN - all_wikis - combined - multiUN --- # Dataset Card for The Large Spanish 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://github.com/josecannete/spanish-corpora](https://github.com/josecannete/spanish-corpora) - **Repository:** [https://github.com/josecannete/spanish-corpora](https://github.com/josecannete/spanish-corpora) - **Paper:** - **Data:** https://doi.org/10.5281/zenodo.3247731 - **Leaderboard:** - **Point of Contact:** [José Cañete](mailto:[email protected]) (corpus creator) or [Lewis Tunstall](mailto:[email protected]) (corpus submitter) ### Dataset Summary The Large Spanish Corpus is a compilation of 15 unlabelled Spanish corpora spanning Wikipedia to European parliament notes. Each config contains the data corresponding to a different corpus. For example, `all_wiki` only includes examples from Spanish Wikipedia: ```python from datasets import load_dataset all_wiki = load_dataset('large_spanish_corpus', name='all_wiki') ``` By default, the config is set to "combined" which loads all the corpora. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Spanish ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits The following is taken from the corpus' source repsository: * Spanish Wikis: Which include Wikipedia, Wikinews, Wikiquotes and more. These were first processed with wikiextractor (https://github.com/josecannete/wikiextractorforBERT) using the wikis dump of 20/04/2019. * ParaCrawl: Spanish portion of ParaCrawl (http://opus.nlpl.eu/ParaCrawl.php) * EUBookshop: Spanish portion of EUBookshop (http://opus.nlpl.eu/EUbookshop.php) * MultiUN: Spanish portion of MultiUN (http://opus.nlpl.eu/MultiUN.php) * OpenSubtitles: Spanish portion of OpenSubtitles2018 (http://opus.nlpl.eu/OpenSubtitles-v2018.php) * DGC: Spanish portion of DGT (http://opus.nlpl.eu/DGT.php) * DOGC: Spanish portion of DOGC (http://opus.nlpl.eu/DOGC.php) * ECB: Spanish portion of ECB (http://opus.nlpl.eu/ECB.php) * EMEA: Spanish portion of EMEA (http://opus.nlpl.eu/EMEA.php) * Europarl: Spanish portion of Europarl (http://opus.nlpl.eu/Europarl.php) * GlobalVoices: Spanish portion of GlobalVoices (http://opus.nlpl.eu/GlobalVoices.php) * JRC: Spanish portion of JRC (http://opus.nlpl.eu/JRC-Acquis.php) * News-Commentary11: Spanish portion of NCv11 (http://opus.nlpl.eu/News-Commentary-v11.php) * TED: Spanish portion of TED (http://opus.nlpl.eu/TED2013.php) * UN: Spanish portion of UN (http://opus.nlpl.eu/UN.php) ## 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.

app_reviews

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - sentiment-scoring paperswithcode_id: null pretty_name: AppReviews dataset_info: features: - name: package_name dtype: string - name: review dtype: string - name: date dtype: string - name: star dtype: int8 splits: - name: train num_bytes: 32769079 num_examples: 288065 download_size: 42592679 dataset_size: 32769079 --- # 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 - **Homepage:** [Home Page](https://github.com/sealuzh/user_quality) - **Repository:** [Repo Link](https://github.com/sealuzh/user_quality) - **Paper:** [Link](https://giograno.me/assets/pdf/workshop/wama17.pdf) - **Leaderboard: - **Point of Contact:** [Darshan Gandhi]([email protected]) ### Dataset Summary It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches) ### Supported Tasks and Leaderboards The dataset we provide comprises 395 different apps from F-Droid repository, including code quality indicators of 629 versions of these apps. It also encloses app reviews related to each of these versions, which have been automatically categorized classifying types of user feedback from a software maintenance and evolution perspective. ### Languages The dataset is a monolingual dataset which has the messages English. ## Dataset Structure ### Data Instances The dataset consists of a message in English. {'package_name': 'com.mantz_it.rfanalyzer', 'review': "Great app! The new version now works on my Bravia Android TV which is great as it's right by my rooftop aerial cable. The scan feature would be useful...any ETA on when this will be available? Also the option to import a list of bookmarks e.g. from a simple properties file would be useful.", 'date': 'October 12 2016', 'star': 4} ### Data Fields * package_name : Name of the Software Application Package * review : Message of the user * date : date when the user posted the review * star : rating provied by the user for the application ### Data Splits There is training data, with a total of : 288065 ## 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 With the help of this dataset one can try to understand more about software applications and what are the views and opinions of the users about them. This helps to understand more about which type of software applications are prefeered by the users and how do these applications facilitate the user to help them solve their problems and issues. ### Discussion of Biases The reviews are only for applications which are in the open-source software applications, the other sectors have not been considered here ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Giovanni Grano - (University of Zurich), Sebastiano Panichella - (University of Zurich), Andrea di Sorbo - (University of Sannio) ### Licensing Information [More Information Needed] ### Citation Information @InProceedings{Zurich Open Repository and Archive:dataset, title = {Software Applications User Reviews}, authors={Grano, Giovanni; Di Sorbo, Andrea; Mercaldo, Francesco; Visaggio, Corrado A; Canfora, Gerardo; Panichella, Sebastiano}, year={2017} } ### Contributions Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset.

para_pat

--- annotations_creators: - machine-generated language_creators: - expert-generated language: - cs - de - el - en - es - fr - hu - ja - ko - pt - ro - ru - sk - uk - zh license: - cc-by-4.0 multilinguality: - translation size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask - translation task_ids: - language-modeling - masked-language-modeling paperswithcode_id: parapat pretty_name: Parallel Corpus of Patents Abstracts dataset_info: - config_name: el-en features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - el - en splits: - name: train num_bytes: 24818840 num_examples: 10855 download_size: 24894705 dataset_size: 24818840 - config_name: cs-en features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 117555722 num_examples: 78977 download_size: 118010340 dataset_size: 117555722 - config_name: en-hu features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - en - hu splits: - name: train num_bytes: 80637157 num_examples: 42629 download_size: 80893995 dataset_size: 80637157 - config_name: en-ro features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - en - ro splits: - name: train num_bytes: 80290819 num_examples: 48789 download_size: 80562562 dataset_size: 80290819 - config_name: en-sk features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - en - sk splits: - name: train num_bytes: 31510348 num_examples: 23410 download_size: 31707728 dataset_size: 31510348 - config_name: en-uk features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - en - uk splits: - name: train num_bytes: 136808871 num_examples: 89226 download_size: 137391928 dataset_size: 136808871 - config_name: es-fr features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 53767035 num_examples: 32553 download_size: 53989438 dataset_size: 53767035 - config_name: fr-ru features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 33915203 num_examples: 10889 download_size: 33994490 dataset_size: 33915203 - config_name: de-fr features: - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 655742822 num_examples: 1167988 download_size: 204094654 dataset_size: 655742822 - config_name: en-ja features: - name: translation dtype: translation: languages: - en - ja splits: - name: train num_bytes: 3100002828 num_examples: 6170339 download_size: 1093334863 dataset_size: 3100002828 - config_name: en-es features: - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 337690858 num_examples: 649396 download_size: 105202237 dataset_size: 337690858 - config_name: en-fr features: - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 6103179552 num_examples: 12223525 download_size: 1846098331 dataset_size: 6103179552 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 1059631418 num_examples: 2165054 download_size: 339299130 dataset_size: 1059631418 - config_name: en-ko features: - name: translation dtype: translation: languages: - en - ko splits: - name: train num_bytes: 1466703472 num_examples: 2324357 download_size: 475152089 dataset_size: 1466703472 - config_name: fr-ja features: - name: translation dtype: translation: languages: - fr - ja splits: - name: train num_bytes: 211127021 num_examples: 313422 download_size: 69038401 dataset_size: 211127021 - config_name: en-zh features: - name: translation dtype: translation: languages: - en - zh splits: - name: train num_bytes: 2297993338 num_examples: 4897841 download_size: 899568201 dataset_size: 2297993338 - config_name: en-ru features: - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 1974874480 num_examples: 4296399 download_size: 567240359 dataset_size: 1974874480 - config_name: fr-ko features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - fr - ko splits: - name: train num_bytes: 222006786 num_examples: 120607 download_size: 64621605 dataset_size: 222006786 - config_name: ru-uk features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - ru - uk splits: - name: train num_bytes: 163442529 num_examples: 85963 download_size: 38709524 dataset_size: 163442529 - config_name: en-pt features: - name: index dtype: int32 - name: family_id dtype: int32 - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 37372555 num_examples: 23121 download_size: 12781082 dataset_size: 37372555 --- # Dataset Card for ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts ## 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:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://figshare.com/articles/ParaPat_The_Multi-Million_Sentences_Parallel_Corpus_of_Patents_Abstracts/12627632) - **Repository:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://github.com/soares-f/parapat) - **Paper:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://www.aclweb.org/anthology/2020.lrec-1.465/) - **Point of Contact:** [Felipe Soares]([email protected]) ### Dataset Summary ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts This dataset contains the developed parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset contains samples in cs, de, el, en, es, fr, hu, ja, ko, pt, ro, ru, sk, uk, zh, hu ## Dataset Structure ### Data Instances They are of 2 types depending on the dataset: First type { "translation":{ "en":"A method for converting a series of m-bit information words to a modulated signal is described.", "es":"Se describe un método para convertir una serie de palabras de informacion de bits m a una señal modulada." } } Second type { "family_id":10944407, "index":844, "translation":{ "el":"αφές ο οποίος παρασκευάζεται με χαρμάνι ελληνικού καφέ είτε σε συσκευή καφέ εσπρέσο είτε σε συσκευή γαλλικού καφέ (φίλτρου) είτε κατά τον παραδοσιακό τρόπο του ελληνικού καφέ και διυλίζεται, κτυπιέται στη συνέχεια με πάγο σε χειροκίνητο ή ηλεκτρικόμίξερ ώστε να παγώσει ομοιόμορφα και να αποκτήσει πλούσιο αφρό και σερβίρεται σε ποτήρι. ΰ", "en":"offee prepared using the mix for Greek coffee either in an espresso - type coffee making machine, or in a filter coffee making machine or in the traditional way for preparing Greek coffee and is then filtered , shaken with ice manually or with an electric mixer so that it freezes homogeneously, obtains a rich froth and is served in a glass." } } ### Data Fields **index:** position in the corpus **family id:** for each abstract, such that researchers can use that information for other text mining purposes. **translation:** distionary containing source and target sentence for that example ### Data Splits No official train/val/test splits given. Parallel corpora aligned into sentence level |Language Pair|# Sentences|# Unique Tokens| |--------|-----|------| |EN/ZH|4.9M|155.8M| |EN/JA|6.1M|189.6M| |EN/FR|12.2M|455M| |EN/KO|2.3M|91.4M| |EN/DE|2.2M|81.7M| |EN/RU|4.3M|107.3M| |DE/FR|1.2M|38.8M| |FR/JA|0.3M|9.9M| |EN/ES|0.6M|24.6M| Parallel corpora aligned into abstract level |Language Pair|# Abstracts| |--------|-----| |FR/KO|120,607| |EN/UK|89,227| |RU/UK|85,963| |CS/EN|78,978| |EN/RO|48,789| |EN/HU|42,629| |ES/FR|32,553| |EN/SK|23,410| |EN/PT|23,122| |BG/EN|16,177| |FR/RU|10,889| ## Dataset Creation ### Curation Rationale The availability of parallel corpora is required by current Statistical and Neural Machine Translation systems (SMT and NMT). Acquiring a high-quality parallel corpus that is large enough to train MT systems, particularly NMT ones, is not a trivial task due to the need for correct alignment and, in many cases, human curation. In this context, the automated creation of parallel corpora from freely available resources is extremely important in Natural Language Pro- cessing (NLP). ### Source Data #### Initial Data Collection and Normalization Google makes patents data available under the Google Cloud Public Datasets. BigQuery is a Google service that supports the efficient storage and querying of massive datasets which are usually a challenging task for usual SQL databases. For instance, filtering the September 2019 release of the dataset, which contains more than 119 million rows, can take less than 1 minute for text fields. The on-demand billing for BigQuery is based on the amount of data processed by each query run, thus for a single query that performs a full-scan, the cost can be over USD 15.00, since the cost per TB is currently USD 5.00. #### Who are the source language producers? BigQuery is a Google service that supports the efficient storage and querying of massive datasets which are usually a challenging task for usual SQL databases. ### Annotations #### Annotation process The following steps describe the process of producing patent aligned abstracts: 1. Load the nth individual file 2. Remove rows where the number of abstracts with more than one language is less than 2 for a given family id. The family id attribute is used to group patents that refers to the same invention. By removing these rows, we remove abstracts that are available only in one language. 3. From the resulting set, create all possible parallel abstracts from the available languages. For instance, an abstract may be available in English, French and German, thus, the possible language pairs are English/French, English/German, and French/German. 4. Store the parallel patents into an SQL database for easier future handling and sampling. #### 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 Funded by Google Tensorflow Research Cloud. ### Licensing Information CC BY 4.0 ### Citation Information ``` @inproceedings{soares-etal-2020-parapat, title = "{P}ara{P}at: The Multi-Million Sentences Parallel Corpus of Patents Abstracts", author = "Soares, Felipe and Stevenson, Mark and Bartolome, Diego and Zaretskaya, Anna", booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.465", pages = "3769--3774", language = "English", ISBN = "979-10-95546-34-4", } ``` [DOI](https://doi.org/10.6084/m9.figshare.12627632) ### Contributions Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.

monash_tsf

--- annotations_creators: - no-annotation language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Monash Time Series Forecasting Repository size_categories: - 1K<n<10K source_datasets: - original task_categories: - time-series-forecasting task_ids: - univariate-time-series-forecasting - multivariate-time-series-forecasting dataset_info: - config_name: weather features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 176893738 num_examples: 3010 - name: test num_bytes: 177638713 num_examples: 3010 - name: validation num_bytes: 177266226 num_examples: 3010 download_size: 38820451 dataset_size: 531798677 - config_name: tourism_yearly features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 54264 num_examples: 518 - name: test num_bytes: 71358 num_examples: 518 - name: validation num_bytes: 62811 num_examples: 518 download_size: 36749 dataset_size: 188433 - config_name: tourism_quarterly features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 162738 num_examples: 427 - name: test num_bytes: 190920 num_examples: 427 - name: validation num_bytes: 176829 num_examples: 427 download_size: 93833 dataset_size: 530487 - config_name: tourism_monthly features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 391518 num_examples: 366 - name: test num_bytes: 463986 num_examples: 366 - name: validation num_bytes: 427752 num_examples: 366 download_size: 199791 dataset_size: 1283256 - config_name: cif_2016 features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 24731 num_examples: 72 - name: test num_bytes: 31859 num_examples: 72 - name: validation num_bytes: 28295 num_examples: 72 download_size: 53344 dataset_size: 84885 - config_name: london_smart_meters features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 684386194 num_examples: 5560 - name: test num_bytes: 687138394 num_examples: 5560 - name: validation num_bytes: 685762294 num_examples: 5560 download_size: 219673439 dataset_size: 2057286882 - config_name: australian_electricity_demand features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 4763162 num_examples: 5 - name: test num_bytes: 4765637 num_examples: 5 - name: validation num_bytes: 4764400 num_examples: 5 download_size: 5770526 dataset_size: 14293199 - config_name: wind_farms_minutely features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 710078918 num_examples: 339 - name: test num_bytes: 710246723 num_examples: 339 - name: validation num_bytes: 710162820 num_examples: 339 download_size: 71383130 dataset_size: 2130488461 - config_name: bitcoin features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 336511 num_examples: 18 - name: test num_bytes: 340966 num_examples: 18 - name: validation num_bytes: 338738 num_examples: 18 download_size: 220403 dataset_size: 1016215 - config_name: pedestrian_counts features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 12897120 num_examples: 66 - name: test num_bytes: 12923256 num_examples: 66 - name: validation num_bytes: 12910188 num_examples: 66 download_size: 4587054 dataset_size: 38730564 - config_name: vehicle_trips features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 105261 num_examples: 329 - name: test num_bytes: 186688 num_examples: 329 - name: validation num_bytes: 145974 num_examples: 329 download_size: 44914 dataset_size: 437923 - config_name: kdd_cup_2018 features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 12040046 num_examples: 270 - name: test num_bytes: 12146966 num_examples: 270 - name: validation num_bytes: 12093506 num_examples: 270 download_size: 2456948 dataset_size: 36280518 - config_name: nn5_daily features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 314828 num_examples: 111 - name: test num_bytes: 366110 num_examples: 111 - name: validation num_bytes: 340469 num_examples: 111 download_size: 287708 dataset_size: 1021407 - config_name: nn5_weekly features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 48344 num_examples: 111 - name: test num_bytes: 55670 num_examples: 111 - name: validation num_bytes: 52007 num_examples: 111 download_size: 62043 dataset_size: 156021 - config_name: kaggle_web_traffic features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 415494391 num_examples: 145063 - name: test num_bytes: 486103806 num_examples: 145063 - name: validation num_bytes: 450799098 num_examples: 145063 download_size: 145485324 dataset_size: 1352397295 - config_name: kaggle_web_traffic_weekly features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 64242469 num_examples: 145063 - name: test num_bytes: 73816627 num_examples: 145063 - name: validation num_bytes: 69029548 num_examples: 145063 download_size: 28930900 dataset_size: 207088644 - config_name: solar_10_minutes features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 29640033 num_examples: 137 - name: test num_bytes: 29707848 num_examples: 137 - name: validation num_bytes: 29673941 num_examples: 137 download_size: 4559353 dataset_size: 89021822 - config_name: solar_weekly features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 28614 num_examples: 137 - name: test num_bytes: 34265 num_examples: 137 - name: validation num_bytes: 31439 num_examples: 137 download_size: 24375 dataset_size: 94318 - config_name: car_parts features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 396653 num_examples: 2674 - name: test num_bytes: 661379 num_examples: 2674 - name: validation num_bytes: 529016 num_examples: 2674 download_size: 39656 dataset_size: 1587048 - config_name: fred_md features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 314514 num_examples: 107 - name: test num_bytes: 325107 num_examples: 107 - name: validation num_bytes: 319811 num_examples: 107 download_size: 169107 dataset_size: 959432 - config_name: traffic_hourly features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 62071974 num_examples: 862 - name: test num_bytes: 62413326 num_examples: 862 - name: validation num_bytes: 62242650 num_examples: 862 download_size: 22868806 dataset_size: 186727950 - config_name: traffic_weekly features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 344154 num_examples: 862 - name: test num_bytes: 401046 num_examples: 862 - name: validation num_bytes: 372600 num_examples: 862 download_size: 245126 dataset_size: 1117800 - config_name: hospital features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 217625 num_examples: 767 - name: test num_bytes: 293558 num_examples: 767 - name: validation num_bytes: 255591 num_examples: 767 download_size: 78110 dataset_size: 766774 - config_name: covid_deaths features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 176352 num_examples: 266 - name: test num_bytes: 242187 num_examples: 266 - name: validation num_bytes: 209270 num_examples: 266 download_size: 27335 dataset_size: 627809 - config_name: sunspot features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 304726 num_examples: 1 - name: test num_bytes: 304974 num_examples: 1 - name: validation num_bytes: 304850 num_examples: 1 download_size: 68865 dataset_size: 914550 - config_name: saugeenday features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 97722 num_examples: 1 - name: test num_bytes: 97969 num_examples: 1 - name: validation num_bytes: 97845 num_examples: 1 download_size: 28721 dataset_size: 293536 - config_name: us_births features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 29923 num_examples: 1 - name: test num_bytes: 30171 num_examples: 1 - name: validation num_bytes: 30047 num_examples: 1 download_size: 16332 dataset_size: 90141 - config_name: solar_4_seconds features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 30513083 num_examples: 1 - name: test num_bytes: 30513578 num_examples: 1 - name: validation num_bytes: 30513331 num_examples: 1 download_size: 794502 dataset_size: 91539992 - config_name: wind_4_seconds features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 30512774 num_examples: 1 - name: test num_bytes: 30513269 num_examples: 1 - name: validation num_bytes: 30513021 num_examples: 1 download_size: 2226184 dataset_size: 91539064 - config_name: rideshare features: - name: start dtype: timestamp[s] - name: target sequence: sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 4249051 num_examples: 156 - name: test num_bytes: 5161435 num_examples: 156 - name: validation num_bytes: 4705243 num_examples: 156 download_size: 1031826 dataset_size: 14115729 - config_name: oikolab_weather features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 3299142 num_examples: 8 - name: test num_bytes: 3302310 num_examples: 8 - name: validation num_bytes: 3300726 num_examples: 8 download_size: 1326101 dataset_size: 9902178 - config_name: temperature_rain features: - name: start dtype: timestamp[s] - name: target sequence: sequence: float32 - name: feat_static_cat sequence: uint64 - name: feat_dynamic_real sequence: sequence: float32 - name: item_id dtype: string splits: - name: train num_bytes: 88121466 num_examples: 422 - name: test num_bytes: 96059286 num_examples: 422 - name: validation num_bytes: 92090376 num_examples: 422 download_size: 25747139 dataset_size: 276271128 --- # Dataset Card for Monash Time Series Forecasting Repository ## 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 - **Homepage:** [Monash Time Series Forecasting Repository](https://forecastingdata.org/) - **Repository:** [Monash Time Series Forecasting Repository code repository](https://github.com/rakshitha123/TSForecasting) - **Paper:** [Monash Time Series Forecasting Archive](https://openreview.net/pdf?id=wEc1mgAjU-) - **Leaderboard:** [Baseline Results](https://forecastingdata.org/#results) - **Point of Contact:** [Rakshitha Godahewa](mailto:[email protected]) ### Dataset Summary The first comprehensive time series forecasting repository containing datasets of related time series to facilitate the evaluation of global forecasting models. All datasets are intended to use only for research purpose. Our repository contains 30 datasets including both publicly available time series datasets (in different formats) and datasets curated by us. Many datasets have different versions based on the frequency and the inclusion of missing values, making the total number of dataset variations to 58. Furthermore, it includes both real-world and competition time series datasets covering varied domains. The following table shows a list of datasets available: | Name | Domain | No. of series | Freq. | Pred. Len. | Source | |-------------------------------|-----------|---------------|--------|------------|-------------------------------------------------------------------------------------------------------------------------------------| | weather | Nature | 3010 | 1D | 30 | [Sparks et al., 2020](https://cran.r-project.org/web/packages/bomrang) | | tourism_yearly | Tourism | 1311 | 1Y | 4 | [Athanasopoulos et al., 2011](https://doi.org/10.1016/j.ijforecast.2010.04.009) | | tourism_quarterly | Tourism | 1311 | 1Q-JAN | 8 | [Athanasopoulos et al., 2011](https://doi.org/10.1016/j.ijforecast.2010.04.009) | | tourism_monthly | Tourism | 1311 | 1M | 24 | [Athanasopoulos et al., 2011](https://doi.org/10.1016/j.ijforecast.2010.04.009) | | cif_2016 | Banking | 72 | 1M | 12 | [Stepnicka and Burda, 2017](https://doi.org/10.1109/FUZZ-IEEE.2017.8015455) | | london_smart_meters | Energy | 5560 | 30T | 60 | [Jean-Michel, 2019](https://www.kaggle.com/jeanmidev/smart-meters-in-london) | | australian_electricity_demand | Energy | 5 | 30T | 60 | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU-) | | wind_farms_minutely | Energy | 339 | 1T | 60 | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- ) | | bitcoin | Economic | 18 | 1D | 30 | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- ) | | pedestrian_counts | Transport | 66 | 1H | 48 | [City of Melbourne, 2020](https://data.melbourne.vic.gov.au/Transport/Pedestrian-Counting-System-Monthly-counts-per-hour/b2ak-trbp) | | vehicle_trips | Transport | 329 | 1D | 30 | [fivethirtyeight, 2015](https://github.com/fivethirtyeight/uber-tlc-foil-response) | | kdd_cup_2018 | Nature | 270 | 1H | 48 | [KDD Cup, 2018](https://www.kdd.org/kdd2018/kdd-cup) | | nn5_daily | Banking | 111 | 1D | 56 | [Ben Taieb et al., 2012](https://doi.org/10.1016/j.eswa.2012.01.039) | | nn5_weekly | Banking | 111 | 1W-MON | 8 | [Ben Taieb et al., 2012](https://doi.org/10.1016/j.eswa.2012.01.039) | | kaggle_web_traffic | Web | 145063 | 1D | 59 | [Google, 2017](https://www.kaggle.com/c/web-traffic-time-series-forecasting) | | kaggle_web_traffic_weekly | Web | 145063 | 1W-WED | 8 | [Google, 2017](https://www.kaggle.com/c/web-traffic-time-series-forecasting) | | solar_10_minutes | Energy | 137 | 10T | 60 | [Solar, 2020](https://www.nrel.gov/grid/solar-power-data.html) | | solar_weekly | Energy | 137 | 1W-SUN | 5 | [Solar, 2020](https://www.nrel.gov/grid/solar-power-data.html) | | car_parts | Sales | 2674 | 1M | 12 | [Hyndman, 2015](https://cran.r-project.org/web/packages/expsmooth/) | | fred_md | Economic | 107 | 1M | 12 | [McCracken and Ng, 2016](https://doi.org/10.1080/07350015.2015.1086655) | | traffic_hourly | Transport | 862 | 1H | 48 | [Caltrans, 2020](http://pems.dot.ca.gov/) | | traffic_weekly | Transport | 862 | 1W-WED | 8 | [Caltrans, 2020](http://pems.dot.ca.gov/) | | hospital | Health | 767 | 1M | 12 | [Hyndman, 2015](https://cran.r-project.org/web/packages/expsmooth/) | | covid_deaths | Health | 266 | 1D | 30 | [Johns Hopkins University, 2020](https://github.com/CSSEGISandData/COVID-19) | | sunspot | Nature | 1 | 1D | 30 | [Sunspot, 2015](http://www.sidc.be/silso/newdataset) | | saugeenday | Nature | 1 | 1D | 30 | [McLeod and Gweon, 2013](http://www.jenvstat.org/v04/i11) | | us_births | Health | 1 | 1D | 30 | [Pruim et al., 2020](https://cran.r-project.org/web/packages/mosaicData) | | solar_4_seconds | Energy | 1 | 4S | 60 | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- ) | | wind_4_seconds | Energy | 1 | 4S | 60 | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- ) | | rideshare | Transport | 2304 | 1H | 48 | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- ) | | oikolab_weather | Nature | 8 | 1H | 48 | [Oikolab](https://oikolab.com/) | | temperature_rain | Nature | 32072 | 1D | 30 | [Godahewa et al. 2021](https://openreview.net/pdf?id=wEc1mgAjU- ) ### Dataset Usage To load a particular dataset just specify its name from the table above e.g.: ```python load_dataset("monash_tsf", "nn5_daily") ``` > Notes: > - Data might contain missing values as in the original datasets. > - The prediction length is either specified in the dataset or a default value depending on the frequency is used as in the original repository benchmark. ### Supported Tasks and Leaderboards #### `time-series-forecasting` ##### `univariate-time-series-forecasting` The univariate time series forecasting tasks involves learning the future one dimensional `target` values of a time series in a dataset for some `prediction_length` time steps. The performance of the forecast models can then be validated via the ground truth in the `validation` split and tested via the `test` split. ##### `multivariate-time-series-forecasting` The multivariate time series forecasting task involves learning the future vector of `target` values of a time series in a dataset for some `prediction_length` time steps. Similar to the univariate setting the performance of a multivariate model can be validated via the ground truth in the `validation` split and tested via the `test` split. ### Languages ## Dataset Structure ### Data Instances A sample from the training set is provided below: ```python { 'start': datetime.datetime(2012, 1, 1, 0, 0), 'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, ...], 'feat_static_cat': [0], 'feat_dynamic_real': [[0.3, 0.4], [0.1, 0.6], ...], 'item_id': '0' } ``` ### Data Fields For the univariate regular time series each series has the following keys: * `start`: a datetime of the first entry of each time series in the dataset * `target`: an array[float32] of the actual target values * `feat_static_cat`: an array[uint64] which contains a categorical identifier of each time series in the dataset * `feat_dynamic_real`: optional array of covariate features * `item_id`: a string identifier of each time series in a dataset for reference For the multivariate time series the `target` is a vector of the multivariate dimension for each time point. ### Data Splits The datasets are split in time depending on the prediction length specified in the datasets. In particular for each time series in a dataset there is a prediction length window of the future in the validation split and another prediction length more in the test split. ## Dataset Creation ### Curation Rationale To facilitate the evaluation of global forecasting models. All datasets in our repository are intended for research purposes and to evaluate the performance of new forecasting algorithms. ### Source Data #### Initial Data Collection and Normalization Out of the 30 datasets, 23 were already publicly available in different platforms with different data formats. The original sources of all datasets are mentioned in the datasets table above. After extracting and curating these datasets, we analysed them individually to identify the datasets containing series with different frequencies and missing observations. Nine datasets contain time series belonging to different frequencies and the archive contains a separate dataset per each frequency. #### Who are the source language producers? The data comes from the datasets listed in the table above. ### Annotations #### Annotation process The annotations come from the datasets listed in the table above. #### 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 * [Rakshitha Godahewa](mailto:[email protected]) * [Christoph Bergmeir](mailto:[email protected]) * [Geoff Webb](mailto:[email protected]) * [Rob Hyndman](mailto:[email protected]) * [Pablo Montero-Manso](mailto:[email protected]) ### Licensing Information [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) ### Citation Information ```tex @InProceedings{godahewa2021monash, author = "Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoffrey I. and Hyndman, Rob J. and Montero-Manso, Pablo", title = "Monash Time Series Forecasting Archive", booktitle = "Neural Information Processing Systems Track on Datasets and Benchmarks", year = "2021", note = "forthcoming" } ``` ### Contributions Thanks to [@kashif](https://github.com/kashif) for adding this dataset.

codeparrot/instructhumaneval

codeparrot

--- dataset_info: features: - name: task_id dtype: string - name: prompt dtype: string - name: canonical_solution dtype: string - name: test dtype: string - name: entry_point dtype: string - name: signature dtype: string - name: docstring dtype: string - name: context dtype: string - name: instruction dtype: string splits: - name: test num_bytes: 335913 num_examples: 164 download_size: 161076 dataset_size: 335913 --- # Instruct HumanEval ## Summary InstructHumanEval is a modified version of OpenAI HumanEval. For a given prompt, we extracted its signature, its docstring as well as its header to create a flexing setting which would allow to evaluation instruction-tuned LLM. The delimiters used in the instruction-tuning procedure can be use to build and instruction that would allow the model to elicit its best capabilities. Here is an example of use The prompt can be built as follows, depending on the model's instruction tuning delimiters ```python from datasets import load_dataset ds = load_dataset("codeparrot/instructhumaneval", split="test", use_auth_token=True) prompt_0 = "Human\n" + ds[0]["instruction"] + "\nAssistant\n" + ds[0]["context"] print(prompt_0) ``` Output ``` Human: Write a function has_close_elements(numbers: List[float], threshold: float) -> bool to solve the following problem: Check if in given list of numbers, are any two numbers closer to each other than given threshold. >>> has_close_elements([1.0, 2.0, 3.0], 0.5) False >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) True Assistant: from typing import List def has_close_elements(numbers: List[float], threshold: float) -> bool: ``` The model can therefore complete the instruction and yield better results because it fits its training procedure. You can also find the code to evaluate models on the dataset in the [BigCode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main). The following sections provide more details on the dataset. ## Dataset description This dataset is a modified version of [OpenAI HumanEval](https://huggingface.co/datasets/openai_humaneval) that is designed to adapt the benchmark to instruction fine-tuned models. As a matter of fact, HumanEval evaluates the ability to complete a code given its signature, its docstring and potentially some auxiliary functions. ## Dataset construction In order to build an instruction version of HumanEval we extracted relevant information from the **prompt** column of the original version - **signature** : this is the signature of the function to complete. It looks like `def function_name(args:type):-> return_type`. - **docstring** : this is the docstring of the function. It is the text which describes the purpose of the function. - **context** : this represents every additional information that is provided in order to help the model complete the function. It includes the imports and the auxiliary functions. Our idea was to move from the original format of HumanEval ``` <context> <signature> <docstring> ``` And build and **instruction** that would be ``` Write a function <signature> to solve the following problem: <docstring> ``` From this instruction, we can design an evaluation pipeline for instruction fine-tuned languages models. ## Evaluation Instruction fine-tuned LLM are built by fine-tuning a base LLM on an instruction dataset. This instruction dataset contains several <input, output> pairs where each represent an instruction submitted by a user together with the right answer to it. These pairs are framed into a multi-turn conversation with the help of special tokens which design each member of the interaction e.g. Q user_token `Human:`, an assistant_token `Assistant:` and and `end_token` `\n` that designates the end of each turn. ### Code completion In this case, the LLM is provided with the following prompt ``` user_token + <instruction> + <end_token> + <assistant_token> + <context> ``` It is the expected to complete the function to solve the problem formulated by the `instruction`. It is very similar to the original evaluation with the advantage that it puts the model in the best condition to understand the task that it is asked to solve. The evaluation is done on the part generated after `<assistant_token>`. ### Docstring to code This setting is more complicated as it requires to model to account for the information contained in the instruction such as the function signature. The LLM is provided with the following prompt ``` user_token + <instruction> + <end_token> + <assistant_token> ``` The model has to generate a function with the correct signature that solve adequately the problem. The evaluation is done by identifying the content of the function in the generation (by search for the right `entry_point`/`function_name`) and concatenating it with the `<context>` provided. ## How to use the dataset ```python from datasets import load_dataset ds = load_dataset("codeparrot/instructhumaneval") ``` ``` ds DatasetDict({ test: Dataset({ features: ['task_id', 'prompt', 'canonical_solution', 'test', 'entry_point', 'signature', 'docstring', 'context', 'instruction'], num_rows: 164 }) }) ```

scan

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - bsd multilinguality: - monolingual pretty_name: SCAN size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: scan tags: - multi-turn dataset_info: - config_name: simple features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3217770 num_examples: 16728 - name: test num_bytes: 799912 num_examples: 4182 download_size: 4080388 dataset_size: 4017682 - config_name: addprim_jump features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2535625 num_examples: 14670 - name: test num_bytes: 1508445 num_examples: 7706 download_size: 4111174 dataset_size: 4044070 - config_name: addprim_turn_left features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3908891 num_examples: 21890 - name: test num_bytes: 170063 num_examples: 1208 download_size: 4148216 dataset_size: 4078954 - config_name: filler_num0 features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2513034 num_examples: 15225 - name: test num_bytes: 330087 num_examples: 1173 download_size: 2892291 dataset_size: 2843121 - config_name: filler_num1 features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2802865 num_examples: 16290 - name: test num_bytes: 330087 num_examples: 1173 download_size: 3185317 dataset_size: 3132952 - config_name: filler_num2 features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3106220 num_examples: 17391 - name: test num_bytes: 330087 num_examples: 1173 download_size: 3491975 dataset_size: 3436307 - config_name: filler_num3 features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3412704 num_examples: 18528 - name: test num_bytes: 330087 num_examples: 1173 download_size: 3801870 dataset_size: 3742791 - config_name: length features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2672464 num_examples: 16990 - name: test num_bytes: 1345218 num_examples: 3920 download_size: 4080388 dataset_size: 4017682 - config_name: template_around_right features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2513034 num_examples: 15225 - name: test num_bytes: 1229757 num_examples: 4476 download_size: 3801870 dataset_size: 3742791 - config_name: template_jump_around_right features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3412704 num_examples: 18528 - name: test num_bytes: 330087 num_examples: 1173 download_size: 3801870 dataset_size: 3742791 - config_name: template_opposite_right features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2944398 num_examples: 15225 - name: test num_bytes: 857943 num_examples: 4476 download_size: 3861420 dataset_size: 3802341 - config_name: template_right features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3127623 num_examples: 15225 - name: test num_bytes: 716403 num_examples: 4476 download_size: 3903105 dataset_size: 3844026 config_names: - addprim_jump - addprim_turn_left - filler_num0 - filler_num1 - filler_num2 - filler_num3 - length - simple - template_around_right - template_jump_around_right - template_opposite_right - template_right --- # Dataset Card for "scan" ## 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://github.com/brendenlake/SCAN](https://github.com/brendenlake/SCAN) - **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:** 224.18 MB - **Size of the generated dataset:** 44.53 MB - **Total amount of disk used:** 268.71 MB ### Dataset Summary SCAN tasks with various splits. SCAN is a set of simple language-driven navigation tasks for studying compositional learning and zero-shot generalization. See https://github.com/brendenlake/SCAN for a description of the splits. Example usage: data = datasets.load_dataset('scan/length') ### 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 #### addprim_jump - **Size of downloaded dataset files:** 18.69 MB - **Size of the generated dataset:** 4.05 MB - **Total amount of disk used:** 22.73 MB An example of 'train' looks as follows. ``` ``` #### addprim_turn_left - **Size of downloaded dataset files:** 18.69 MB - **Size of the generated dataset:** 4.09 MB - **Total amount of disk used:** 22.76 MB An example of 'train' looks as follows. ``` ``` #### filler_num0 - **Size of downloaded dataset files:** 18.69 MB - **Size of the generated dataset:** 2.85 MB - **Total amount of disk used:** 21.53 MB An example of 'train' looks as follows. ``` ``` #### filler_num1 - **Size of downloaded dataset files:** 18.69 MB - **Size of the generated dataset:** 3.14 MB - **Total amount of disk used:** 21.82 MB An example of 'train' looks as follows. ``` ``` #### filler_num2 - **Size of downloaded dataset files:** 18.69 MB - **Size of the generated dataset:** 3.44 MB - **Total amount of disk used:** 22.12 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### addprim_jump - `commands`: a `string` feature. - `actions`: a `string` feature. #### addprim_turn_left - `commands`: a `string` feature. - `actions`: a `string` feature. #### filler_num0 - `commands`: a `string` feature. - `actions`: a `string` feature. #### filler_num1 - `commands`: a `string` feature. - `actions`: a `string` feature. #### filler_num2 - `commands`: a `string` feature. - `actions`: a `string` feature. ### Data Splits | name |train|test| |-----------------|----:|---:| |addprim_jump |14670|7706| |addprim_turn_left|21890|1208| |filler_num0 |15225|1173| |filler_num1 |16290|1173| |filler_num2 |17391|1173| ## 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{Lake2018GeneralizationWS, title={Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks}, author={Brenden M. Lake and Marco Baroni}, booktitle={ICML}, year={2018}, url={https://arxiv.org/pdf/1711.00350.pdf}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

kunishou/databricks-dolly-15k-ja

kunishou

--- license: cc-by-sa-3.0 --- This dataset was created by automatically translating "databricks-dolly-15k" into Japanese. This dataset is licensed under CC-BY-SA-3.0 Last Update : 2023-05-11 databricks-dolly-15k-ja https://github.com/kunishou/databricks-dolly-15k-ja databricks-dolly-15k https://github.com/databrickslabs/dolly/tree/master/data

skg/toxigen-data

skg

--- annotations_creators: - expert-generated language_creators: - machine-generated languages: - en-US licenses: [] multilinguality: - monolingual pretty_name: ToxiGen size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - hate-speech-detection --- # Dataset Card for ToxiGen ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-instances) - [Additional Information](#additional-information) - [Citation Information](#citation-information) ## Sign up for Data Access To access ToxiGen, first fill out [this form](https://forms.office.com/r/r6VXX8f8vh). ## Dataset Description - **Repository:** https://github.com/microsoft/toxigen - **Paper:** https://arxiv.org/abs/2203.09509 - **Point of Contact #1:** [Tom Hartvigsen]([email protected]) - **Point of Contact #2:** [Saadia Gabriel]([email protected]) ### Dataset Summary This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in [our paper](https://arxiv.org/abs/2203.09509). ### Languages All text is written in English. ## Dataset Structure ### Data Fields We release TOXIGEN as a dataframe with the following fields: - **prompt** is the prompt used for **generation**. - **generation** is the TOXIGEN generated text. - **generation_method** denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used. - **prompt_label** is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign). - **group** indicates the target group of the prompt. - **roberta_prediction** is the probability predicted by our corresponding RoBERTa model for each instance. ### Citation Information ```bibtex @inproceedings{hartvigsen2022toxigen, title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection}, author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece}, booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}, year={2022} } ```

sayakpaul/nyu_depth_v2

sayakpaul

--- license: apache-2.0 language: - en multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - depth-estimation task_ids: [] pretty_name: NYU Depth V2 tags: - depth-estimation paperswithcode_id: nyuv2 dataset_info: features: - name: image dtype: image - name: depth_map dtype: image splits: - name: train num_bytes: 20212097551 num_examples: 47584 - name: validation num_bytes: 240785762 num_examples: 654 download_size: 35151124480 dataset_size: 20452883313 --- # Dataset Card for NYU Depth V2 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Visualization](#visualization) - [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:** [NYU Depth Dataset V2 homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html) - **Repository:** Fast Depth [repository](https://github.com/dwofk/fast-depth) which was used to source the dataset in this repository. It is a preprocessed version of the original NYU Depth V2 dataset linked above. It is also used in [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/nyu_depth_v2). - **Papers:** [Indoor Segmentation and Support Inference from RGBD Images](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) and [FastDepth: Fast Monocular Depth Estimation on Embedded Systems](https://arxiv.org/abs/1903.03273) - **Point of Contact:** [Nathan Silberman](mailto:silberman@@cs.nyu.edu) and [Diana Wofk](mailto:[email protected]) ### Dataset Summary As per the [dataset homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html): The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft [Kinect](http://www.xbox.com/kinect). It features: * 1449 densely labeled pairs of aligned RGB and depth images * 464 new scenes taken from 3 cities * 407,024 new unlabeled frames * Each object is labeled with a class and an instance number (cup1, cup2, cup3, etc) The dataset has several components: * Labeled: A subset of the video data accompanied by dense multi-class labels. This data has also been preprocessed to fill in missing depth labels. * Raw: The raw rgb, depth and accelerometer data as provided by the Kinect. * Toolbox: Useful functions for manipulating the data and labels. ### Supported Tasks - `depth-estimation`: Depth estimation is the task of approximating the perceived depth of a given image. In other words, it's about measuring the distance of each image pixel from the camera. - `semantic-segmentation`: Semantic segmentation is the task of associating every pixel of an image to a class label. There are other tasks supported by this dataset as well. You can find more about them by referring to [this resource](https://paperswithcode.com/dataset/nyuv2). ### Languages English. ## Dataset Structure ### Data Instances A data point comprises an image and its annotation depth map for both the `train` and `validation` splits. ``` { 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB at 0x1FF32A3EDA0>, 'depth_map': <PIL.PngImagePlugin.PngImageFile image mode=L at 0x1FF32E5B978>, } ``` ### 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]`. - `depth_map`: A `PIL.Image.Image` object containing the annotation depth map. ### Data Splits The data is split into training, and validation splits. The training data contains 47584 images, and the validation data contains 654 images. ## Visualization You can use the following code snippet to visualize samples from the dataset: ```py from datasets import load_dataset import numpy as np import matplotlib.pyplot as plt cmap = plt.cm.viridis ds = load_dataset("sayakpaul/nyu_depth_v2") def colored_depthmap(depth, d_min=None, d_max=None): if d_min is None: d_min = np.min(depth) if d_max is None: d_max = np.max(depth) depth_relative = (depth - d_min) / (d_max - d_min) return 255 * cmap(depth_relative)[:,:,:3] # H, W, C def merge_into_row(input, depth_target): input = np.array(input) depth_target = np.squeeze(np.array(depth_target)) d_min = np.min(depth_target) d_max = np.max(depth_target) depth_target_col = colored_depthmap(depth_target, d_min, d_max) img_merge = np.hstack([input, depth_target_col]) return img_merge random_indices = np.random.choice(len(ds["train"]), 9).tolist() train_set = ds["train"] plt.figure(figsize=(15, 6)) for i, idx in enumerate(random_indices): ax = plt.subplot(3, 3, i + 1) image_viz = merge_into_row( train_set[idx]["image"], train_set[idx]["depth_map"] ) plt.imshow(image_viz.astype("uint8")) plt.axis("off") ``` ## Dataset Creation ### Curation Rationale The rationale from [the paper](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) that introduced the NYU Depth V2 dataset: > We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation. ### Source Data #### Initial Data Collection > The dataset consists of 1449 RGBD images, gathered from a wide range of commercial and residential buildings in three different US cities, comprising 464 different indoor scenes across 26 scene classes.A dense per-pixel labeling was obtained for each image using Amazon Mechanical Turk. ### Annotations #### Annotation process This is an involved process. Interested readers are referred to Sections 2, 3, and 4 of the [original paper](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf). #### Who are the annotators? AMT annotators. ### 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 * Original NYU Depth V2 dataset: Nathan Silberman, Derek Hoiem, Pushmeet Kohli, Rob Fergus * Preprocessed version: Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, Vivienne Sze ### Licensing Information The preprocessed NYU Depth V2 dataset is licensed under a [MIT License](https://github.com/dwofk/fast-depth/blob/master/LICENSE). ### Citation Information ```bibtex @inproceedings{Silberman:ECCV12, author = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus}, title = {Indoor Segmentation and Support Inference from RGBD Images}, booktitle = {ECCV}, year = {2012} } @inproceedings{icra_2019_fastdepth, author = {{Wofk, Diana and Ma, Fangchang and Yang, Tien-Ju and Karaman, Sertac and Sze, Vivienne}}, title = {{FastDepth: Fast Monocular Depth Estimation on Embedded Systems}}, booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}}, year = {{2019}} } ``` ### Contributions Thanks to [@sayakpaul](https://huggingface.co/sayakpaul) for adding this dataset.

bigheiniuJ/JimmyLuAug

bigheiniuJ

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: output dtype: string - name: input dtype: string - name: seed dtype: string - name: split dtype: string - name: task dtype: string - name: id dtype: int64 - name: aug_type dtype: string - name: aug_time dtype: int64 - name: options sequence: string splits: - name: train num_bytes: 390852959.61794406 num_examples: 914133 download_size: 95170713 dataset_size: 390852959.61794406 --- # Dataset Card for "JimmyLuAug" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

americas_nli

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - ay - bzd - cni - gn - hch - nah - oto - qu - shp - tar license: - unknown multilinguality: - multilingual - translation size_categories: - unknown source_datasets: - extended|xnli task_categories: - text-classification task_ids: - natural-language-inference pretty_name: 'AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages.' dataset_info: - config_name: aym features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 117538 num_examples: 743 - name: test num_bytes: 115259 num_examples: 750 download_size: 2256093 dataset_size: 232797 - config_name: bzd features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 143362 num_examples: 743 - name: test num_bytes: 127684 num_examples: 750 download_size: 2256093 dataset_size: 271046 - config_name: cni features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 113264 num_examples: 658 - name: test num_bytes: 116292 num_examples: 750 download_size: 2256093 dataset_size: 229556 - config_name: gn features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 115143 num_examples: 743 - name: test num_bytes: 101956 num_examples: 750 download_size: 2256093 dataset_size: 217099 - config_name: hch features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 127974 num_examples: 743 - name: test num_bytes: 120865 num_examples: 750 download_size: 2256093 dataset_size: 248839 - config_name: nah features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 50749 num_examples: 376 - name: test num_bytes: 102961 num_examples: 738 download_size: 2256093 dataset_size: 153710 - config_name: oto features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 27018 num_examples: 222 - name: test num_bytes: 119658 num_examples: 748 download_size: 2256093 dataset_size: 146676 - config_name: quy features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 125644 num_examples: 743 - name: test num_bytes: 112758 num_examples: 750 download_size: 2256093 dataset_size: 238402 - config_name: shp features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 124508 num_examples: 743 - name: test num_bytes: 118942 num_examples: 750 download_size: 2256093 dataset_size: 243450 - config_name: tar features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 139504 num_examples: 743 - name: test num_bytes: 122632 num_examples: 750 download_size: 2256093 dataset_size: 262136 - config_name: all_languages features: - name: language dtype: string - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction splits: - name: validation num_bytes: 1129092 num_examples: 6457 - name: test num_bytes: 1210591 num_examples: 7486 download_size: 2256093 dataset_size: 2339683 --- # Dataset Card for AmericasNLI ## 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:** [Needs More Information] - **Repository:** https://github.com/nala-cub/AmericasNLI - **Paper:** https://arxiv.org/abs/2104.08726 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels). ### Supported Tasks and Leaderboards [Needs More Information] ### Languages - aym - bzd - cni - gn - hch - nah - oto - quy - shp - tar ## Dataset Structure ### Data Instances #### all_languages An example of the test split looks as follows: ``` {'language': 'aym', 'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix p arlxapxti.', 'label': 2} ``` #### aym An example of the test split looks as follows: ``` {'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix parlxapxti.', 'label ': 2} ``` #### bzd An example of the test split looks as follows: ``` {'premise': "Bua', kèq ye' kũ e' bikeitsök erë ye' chkénãwã tã ye' ujtémĩne ie' tã páxlĩnẽ.", 'hypothesis': "Kèq ye' ùtẽnẽ ie' tã páxlĩ.", 'label': 2} ``` #### cni An example of the test split looks as follows: ``` {'premise': 'Kameetsa, tee nokenkeshireajeroji, iro kantaincha tee nomateroji aisati nintajaro noñanatajiri iroakera.', 'hypothesis': 'Tee noñatajeriji.', 'label': 2} ``` #### gn An example of the test split looks as follows: ``` {'premise': "Néi, ni napensaikurihína upéva rehe, ajepichaiterei ha añepyrûjey añe'ê hendive.", 'hypothesis': "Nañe'êvéi hendive.", 'label': 2} ``` #### hch An example of the test split looks as follows: ``` {'premise': 'mu hekwa.', 'hypothesis': 'neuka tita xatawe m+k+ mat+a.', 'label': 2} ``` #### nah An example of the test split looks as follows: ``` {'premise': 'Cualtitoc, na axnimoihliaya ino, nicualaniztoya queh naha nicamohuihqui', 'hypothesis': 'Ayoc nicamohuihtoc', 'label': 2} ``` #### oto An example of the test split looks as follows: ``` {'premise': 'mi-ga, nin mibⴘy mbô̮nitho ane guenu, guedi mibⴘy nho ⴘnmⴘy xi di mⴘdi o ñana nen nⴘua manaigui', 'hypothesis': 'hin din bi pengui nen nⴘa', 'label': 2} ``` #### quy An example of the test split looks as follows: ``` {'premise': 'Allinmi, manam chaypiqa hamutachkarqanichu, ichaqa manam allinchu tarikurqani chaymi kaqllamanta paywan rimarqani.', 'hypothesis': 'Manam paywanqa kaqllamantaqa rimarqani .', 'label': 2} ``` #### shp An example of the test split looks as follows: ``` {'premise': 'Jakon riki, ja shinanamara ea ike, ikaxbi kikin frustradara ea ike jakopira ea jabe yoyo iribake.', 'hypothesis': 'Eara jabe yoyo iribiama iki.', 'label': 2} ``` #### tar An example of the test split looks as follows: ``` {'premise': 'Ga’lá ju, ke tási newalayé nejé echi kítira, we ne majáli, a’lí ko uchécho ne yua ku ra’íchaki.', 'hypothesis': 'Tási ne uchecho yua ra’ícha échi rejói.', 'label': 2} ``` ### Data Fields #### all_languages - language: a multilingual string variable, with languages including ar, bg, de, el, en. - premise: a multilingual string variable, with languages including ar, bg, de, el, en. - hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### aym - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### bzd - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### cni - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### hch - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### nah - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### oto - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### quy - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### shp - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). #### tar - premise: a string feature. - hypothesis: a string feature. - label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). ### Data Splits | Language | ISO | Family | Dev | Test | |-------------------|-----|:-------------|-----:|-----:| | all_languages | -- | -- | 6457 | 7486 | | Aymara | aym | Aymaran | 743 | 750 | | Ashaninka | cni | Arawak | 658 | 750 | | Bribri | bzd | Chibchan | 743 | 750 | | Guarani | gn | Tupi-Guarani | 743 | 750 | | Nahuatl | nah | Uto-Aztecan | 376 | 738 | | Otomi | oto | Oto-Manguean | 222 | 748 | | Quechua | quy | Quechuan | 743 | 750 | | Raramuri | tar | Uto-Aztecan | 743 | 750 | | Shipibo-Konibo | shp | Panoan | 743 | 750 | | Wixarika | hch | Uto-Aztecan | 743 | 750 | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data The authors translate from the Spanish subset of XNLI. > AmericasNLI is the translation of a subset of XNLI (Conneau et al., 2018). As translators between Spanish and the target languages are more frequently available than those for English, we translate from the Spanish version. As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726). #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process The dataset comprises expert translations from Spanish XNLI. > Additionally, some translators reported that code-switching is often used to describe certain topics, and, while many words without an exact equivalence in the target language are worked in through translation or interpretation, others are kept in Spanish. To minimize the amount of Spanish vocabulary in the translated examples, we choose sentences from genres that we judged to be relatively easy to translate into the target languages: “face-to-face,” “letters,” and “telephone.” As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726). #### 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 ``` @article{DBLP:journals/corr/abs-2104-08726, author = {Abteen Ebrahimi and Manuel Mager and Arturo Oncevay and Vishrav Chaudhary and Luis Chiruzzo and Angela Fan and John Ortega and Ricardo Ramos and Annette Rios and Ivan Vladimir and Gustavo A. Gim{\'{e}}nez{-}Lugo and Elisabeth Mager and Graham Neubig and Alexis Palmer and Rolando A. Coto Solano and Ngoc Thang Vu and Katharina Kann}, title = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages}, journal = {CoRR}, volume = {abs/2104.08726}, year = {2021}, url = {https://arxiv.org/abs/2104.08726}, eprinttype = {arXiv}, eprint = {2104.08726}, timestamp = {Mon, 26 Apr 2021 17:25:10 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ### Contributions Thanks to [@fdschmidt93](https://github.com/fdschmidt93) for adding this dataset.

smangrul/code-chat-assistant-v1

smangrul

--- dataset_info: features: - name: content dtype: string splits: - name: train num_bytes: 25042064.0 num_examples: 10876 - name: test num_bytes: 1348088 num_examples: 818 download_size: 12246507 dataset_size: 26390152.0 --- # Dataset Card for "code-chat-assistant-v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tatoeba

--- annotations_creators: - found language_creators: - found language: - ab - acm - ady - af - afb - afh - aii - ain - ajp - akl - aln - am - an - ang - aoz - apc - ar - arq - ary - arz - as - ast - avk - awa - ayl - az - ba - bal - bar - be - ber - bg - bho - bjn - bm - bn - bo - br - brx - bs - bua - bvy - bzt - ca - cay - cbk - ce - ceb - ch - chg - chn - cho - chr - cjy - ckb - ckt - cmn - co - code - cpi - crh - crk - cs - csb - cv - cy - da - de - dng - drt - dsb - dtp - dv - dws - ee - egl - el - emx - en - enm - eo - es - et - eu - ext - fi - fj - fkv - fo - fr - frm - fro - frr - fuc - fur - fuv - fy - ga - gag - gan - gbm - gcf - gd - gil - gl - gn - gom - gos - got - grc - gsw - gu - gv - ha - hak - haw - hbo - he - hi - hif - hil - hnj - hoc - hr - hrx - hsb - hsn - ht - hu - hy - ia - iba - id - ie - ig - ii - ike - ilo - io - is - it - izh - ja - jam - jbo - jdt - jpa - jv - ka - kaa - kab - kam - kek - kha - kjh - kk - kl - km - kmr - kn - ko - koi - kpv - krc - krl - ksh - ku - kum - kw - kxi - ky - la - laa - lad - lb - ldn - lfn - lg - lij - liv - lkt - lld - lmo - ln - lo - lt - ltg - lut - lv - lzh - lzz - mad - mai - max - mdf - mfe - mg - mgm - mh - mhr - mi - mic - min - mk - ml - mn - mni - mnw - moh - mr - mt - mvv - mwl - mww - my - myv - na - nah - nan - nb - nch - nds - ngt - ngu - niu - nl - nlv - nn - nog - non - nov - npi - nst - nus - nv - ny - nys - oar - oc - ofs - ood - or - orv - os - osp - ota - otk - pa - pag - pal - pam - pap - pau - pcd - pdc - pes - phn - pi - pl - pms - pnb - ppl - prg - ps - pt - qu - quc - qya - rap - rif - rm - rn - ro - rom - ru - rue - rw - sa - sah - sc - scn - sco - sd - sdh - se - sg - sgs - shs - shy - si - sjn - sl - sm - sma - sn - so - sq - sr - stq - su - sux - sv - swg - swh - syc - ta - te - tet - tg - th - thv - ti - tig - tk - tl - tlh - tly - tmr - tmw - tn - to - toi - tok - tpi - tpw - tr - ts - tt - tts - tvl - ty - tyv - tzl - udm - ug - uk - umb - ur - uz - vec - vep - vi - vo - vro - wa - war - wo - wuu - xal - xh - xqa - yi - yo - yue - zlm - zsm - zu - zza license: - cc-by-2.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: tatoeba pretty_name: Tatoeba dataset_info: - config_name: en-mr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - mr splits: - name: train num_bytes: 6190484 num_examples: 53462 download_size: 1436200 dataset_size: 6190484 - config_name: eo-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - eo - nl splits: - name: train num_bytes: 8150048 num_examples: 93650 download_size: 3020382 dataset_size: 8150048 - config_name: es-pt features: - name: id dtype: string - name: translation dtype: translation: languages: - es - pt splits: - name: train num_bytes: 6180464 num_examples: 67782 download_size: 2340361 dataset_size: 6180464 - config_name: fr-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - ru splits: - name: train num_bytes: 19775390 num_examples: 195161 download_size: 5509784 dataset_size: 19775390 - config_name: es-gl features: - name: id dtype: string - name: translation dtype: translation: languages: - es - gl splits: - name: train num_bytes: 287683 num_examples: 3135 download_size: 128506 dataset_size: 287683 --- # Dataset Card for Tatoeba ## 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://opus.nlpl.eu/Tatoeba.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary Tatoeba is a collection of sentences and translations. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Tatoeba.php E.g. `dataset = load_dataset("tatoeba", lang1="en", lang2="he")` The default date is v2021-07-22, but you can also change the date with `dataset = load_dataset("tatoeba", lang1="en", lang2="he", date="v2020-11-09")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - ab - acm - ady - af - afb - afh - aii - ain - ajp - akl - aln - am - an - ang - aoz - apc - ar - arq - ary - arz - as - ast - avk - awa - ayl - az - ba - bal - bar - be - ber - bg - bho - bjn - bm - bn - bo - br - brx - bs - bua - bvy - bzt - ca - cay - cbk - ce - ceb - ch - chg - chn - cho - chr - cjy - ckb - ckt - cmn - co - code - cpi - crh - crk - cs - csb - cv - cy - da - de - dng - drt - dsb - dtp - dv - dws - ee - egl - el - emx - en - enm - eo - es - et - eu - ext - fi - fj - fkv - fo - fr - frm - fro - frr - fuc - fur - fuv - fy - ga - gag - gan - gbm - gcf - gd - gil - gl - gn - gom - gos - got - grc - gsw - gu - gv - ha - hak - haw - hbo - he - hi - hif - hil - hnj - hoc - hr - hrx - hsb - hsn - ht - hu - hy - ia - iba - id - ie - ig - ii - ike - ilo - io - is - it - izh - ja - jam - jbo - jdt - jpa - jv - ka - kaa - kab - kam - kek - kha - kjh - kk - kl - km - kmr - kn - ko - koi - kpv - krc - krl - ksh - ku - kum - kw - kxi - ky - kzj: Coastal Kadazan (deprecated tag; preferred value: Kadazan Dusun; Central Dusun (`dtp`)) - la - laa - lad - lb - ldn - lfn - lg - lij - liv - lkt - lld - lmo - ln - lo - lt - ltg - lut - lv - lzh - lzz - mad - mai - max - mdf - mfe - mg - mgm - mh - mhr - mi - mic - min - mk - ml - mn - mni - mnw - moh - mr - mt - mvv - mwl - mww - my - myv - na - nah - nan - nb - nch - nds - ngt - ngu - niu - nl - nlv - nn - nog - non - nov - npi - nst - nus - nv - ny - nys - oar - oc - ofs - ood - or - orv - os - osp - ota - otk - pa - pag - pal - pam - pap - pau - pcd - pdc - pes - phn - pi - pl - pms - pnb - ppl - prg - ps - pt - qu - quc - qya - rap - rif - rm - rn - ro - rom - ru - rue - rw - sa - sah - sc - scn - sco - sd - sdh - se - sg - sgs - shs - shy - si - sjn - sl - sm - sma - sn - so - sq - sr - stq - su - sux - sv - swg - swh - syc - ta - te - tet - tg - th - thv - ti - tig - tk - tl - tlh - tly - tmr - tmw - tn - to - toi - tok - tpi - tpw - tr - ts - tt - tts - tvl - ty - tyv - tzl - udm - ug - uk - umb - ur - uz - vec - vep - vi - vo - vro - wa - war - wo - wuu - xal - xh - xqa - yi - yo - yue - zlm - zsm - zu - zza ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## 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? [More Information Needed] ### Annotations [More Information Needed] #### 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 [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

quartz

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: quartz pretty_name: QuaRTz dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string - name: para dtype: string - name: para_id dtype: string - name: para_anno struct: - name: effect_prop dtype: string - name: cause_dir_str dtype: string - name: effect_dir_str dtype: string - name: cause_dir_sign dtype: string - name: effect_dir_sign dtype: string - name: cause_prop dtype: string - name: question_anno struct: - name: more_effect_dir dtype: string - name: less_effect_dir dtype: string - name: less_cause_prop dtype: string - name: more_effect_prop dtype: string - name: less_effect_prop dtype: string - name: less_cause_dir dtype: string splits: - name: test num_bytes: 351374 num_examples: 784 - name: train num_bytes: 1197525 num_examples: 2696 - name: validation num_bytes: 175871 num_examples: 384 download_size: 497354 dataset_size: 1724770 --- # Dataset Card for "quartz" ## 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://allenai.org/data/quartz](https://allenai.org/data/quartz) - **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:** 0.49 MB - **Size of the generated dataset:** 1.72 MB - **Total amount of disk used:** 2.22 MB ### Dataset Summary QuaRTz is a crowdsourced dataset of 3864 multiple-choice questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs). The QuaRTz dataset V1 contains 3864 questions about open domain qualitative relationships. Each question is paired with one of 405 different background sentences (sometimes short paragraphs). The dataset is split into train (2696), dev (384) and test (784). A background sentence will only appear in a single split. ### 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:** 0.49 MB - **Size of the generated dataset:** 1.72 MB - **Total amount of disk used:** 2.22 MB An example of 'train' looks as follows. ``` { "answerKey": "A", "choices": { "label": ["A", "B"], "text": ["higher", "lower"] }, "id": "QRQA-10116-3", "para": "Electrons at lower energy levels, which are closer to the nucleus, have less energy.", "para_anno": { "cause_dir_sign": "LESS", "cause_dir_str": "closer", "cause_prop": "distance from a nucleus", "effect_dir_sign": "LESS", "effect_dir_str": "less", "effect_prop": "energy" }, "para_id": "QRSent-10116", "question": "Electrons further away from a nucleus have _____ energy levels than close ones.", "question_anno": { "less_cause_dir": "electron energy levels", "less_cause_prop": "nucleus", "less_effect_dir": "lower", "less_effect_prop": "electron energy levels", "more_effect_dir": "higher", "more_effect_prop": "electron energy levels" } } ``` ### Data Fields The data fields are the same among all splits. #### default - `id`: a `string` feature. - `question`: a `string` feature. - `choices`: a dictionary feature containing: - `text`: a `string` feature. - `label`: a `string` feature. - `answerKey`: a `string` feature. - `para`: a `string` feature. - `para_id`: a `string` feature. - `effect_prop`: a `string` feature. - `cause_dir_str`: a `string` feature. - `effect_dir_str`: a `string` feature. - `cause_dir_sign`: a `string` feature. - `effect_dir_sign`: a `string` feature. - `cause_prop`: a `string` feature. - `more_effect_dir`: a `string` feature. - `less_effect_dir`: a `string` feature. - `less_cause_prop`: a `string` feature. - `more_effect_prop`: a `string` feature. - `less_effect_prop`: a `string` feature. - `less_cause_dir`: a `string` feature. ### Data Splits | name |train|validation|test| |-------|----:|---------:|---:| |default| 2696| 384| 784| ## 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 The dataset is licensed under Creative Commons [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). ### Citation Information ``` @InProceedings{quartz, author = {Oyvind Tafjord and Matt Gardner and Kevin Lin and Peter Clark}, title = {"QUARTZ: An Open-Domain Dataset of Qualitative Relationship Questions"}, year = {"2019"}, } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset.

eugenesiow/Div2k

eugenesiow

--- annotations_creators: - machine-generated language_creators: - found language: [] license: - other multilinguality: - monolingual size_categories: - unknown source_datasets: - original task_categories: - other task_ids: [] pretty_name: Div2k tags: - other-image-super-resolution --- # Dataset Card for Div2k ## 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 - **Homepage**: https://data.vision.ee.ethz.ch/cvl/DIV2K/ - **Repository**: https://huggingface.co/datasets/eugenesiow/Div2k - **Paper**: http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf - **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2 ### Dataset Summary DIV2K is a dataset of RGB images (2K resolution high quality images) with a large diversity of contents. The DIV2K dataset is divided into: - train data: starting from 800 high definition high resolution images we obtain corresponding low resolution images and provide both high and low resolution images for 2, 3, and 4 downscaling factors - validation data: 100 high definition high resolution images are used for genereting low resolution corresponding images, the low res are provided from the beginning of the challenge and are meant for the participants to get online feedback from the validation server; the high resolution images will be released when the final phase of the challenge starts. Install with `pip`: ```bash pip install datasets super-image ``` Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library: ```python from datasets import load_dataset from super_image import EdsrModel from super_image.data import EvalDataset, EvalMetrics dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x2', split='validation') eval_dataset = EvalDataset(dataset) model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2) EvalMetrics().evaluate(model, eval_dataset) ``` ### Supported Tasks and Leaderboards The dataset is commonly used for training and evaluation of the `image-super-resolution` task. Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for: - [Scale 2](https://github.com/eugenesiow/super-image#scale-x2) - [Scale 3](https://github.com/eugenesiow/super-image#scale-x3) - [Scale 4](https://github.com/eugenesiow/super-image#scale-x4) - [Scale 8](https://github.com/eugenesiow/super-image#scale-x8) ### Languages Not applicable. ## Dataset Structure ### Data Instances An example of `train` for `bicubic_x2` looks as follows. ``` { "hr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_HR/0801.png", "lr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_LR_bicubic/X2/0801x2.png" } ``` ### Data Fields The data fields are the same among all splits. - `hr`: a `string` to the path of the High Resolution (HR) `.png` image. - `lr`: a `string` to the path of the Low Resolution (LR) `.png` image. ### Data Splits | name |train |validation| |-------|-----:|---:| |bicubic_x2|800|100| |bicubic_x3|800|100| |bicubic_x4|800|100| |bicubic_x8|800|100| |unknown_x2|800|100| |unknown_x3|800|100| |unknown_x4|800|100| |realistic_mild_x4|800|100| |realistic_difficult_x4|800|100| |realistic_wild_x4|800|100| ## Dataset Creation ### Curation Rationale Please refer to the [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) section. ### Source Data #### Initial Data Collection and Normalization **Resolution and quality**: All the images are 2K resolution, that is they have 2K pixels on at least one of the axes (vertical or horizontal). All the images were processed using the same tools. For simplicity, since the most common magnification factors in the recent SR literature are of ×2, ×3 and ×4 we cropped the images to multiple of 12 pixels on both axes. Most of the crawled images were originally above 20M pixels. The images are of high quality both aesthetically and in the terms of small amounts of noise and other corruptions (like blur and color shifts). **Diversity**: The authors collected images from dozens of sites. A preference was made for sites with freely shared high quality photography (such as https://www.pexels.com/ ). Note that we did not use images from Flickr, Instagram, or other legally binding or copyright restricted images. We only seldom used keywords to assure the diversity for our dataset. DIV2K covers a large diversity of contents, ranging from people, handmade objects and environments (cities, villages), to flora and fauna, and natural sceneries including underwater and dim light conditions. **Partitions**: After collecting the DIV2K 1000 images the authors computed image entropy, bit per pixel (bpp) PNG compression rates and CORNIA scores (see Section 7.6) and applied bicubic downscaling ×3 and then upscaling ×3 with bicubic interpolation (imresize Matlab function), ANR [47] and A+ [48] methods and default settings. The authors randomly generated partitions of 800 train, 100 validation and 100 test images until they achieved a good balance firstly in visual contents and then on the average entropy, average bpp, average number of pixels per image (ppi), average CORNIA quality scores and also in the relative differences between the average PSNR scores of bicubic, ANR and A+ methods. Only the 800 train and 100 validation images are included in this dataset. #### Who are the source language producers? The authors manually crawled 1000 color RGB images from Internet paying special attention to the image quality, to the diversity of sources (sites and cameras), to the image contents and to the copyrights. ### Annotations #### Annotation process No annotations. #### Who are the annotators? No annotators. ### Personal and Sensitive Information All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset immediately. ## 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 - **Original Author**: [Radu Timofte](http://people.ee.ethz.ch/~timofter/) ### Licensing Information Please notice that this dataset is made available for academic research purpose only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform the authors, and they will remove it from the dataset immediately. ### Citation Information ```bibtex @InProceedings{Agustsson_2017_CVPR_Workshops, author = {Agustsson, Eirikur and Timofte, Radu}, title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf", month = {July}, year = {2017} } ``` ### Contributions Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.

LeoCordoba/CC-NEWS-ES

LeoCordoba

--- annotations_creators: - no-annotation language_creators: - found language: - es license: - mit multilinguality: - monolingual size_categories: - n<1K - 1K<n<10K - 10K<n<100K - 100K<n<1M - 1M<n<10M source_datasets: - cc-news task_categories: - summarization - text-generation task_ids: [] tags: - conditional-text-generation --- # Dataset Card for CC-NEWS-ES ## 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:** - **Repository:** [CC-NEWS-ES dataset repository](https://huggingface.co/datasets/LeoCordoba/CC-NEWS-ES) - **Paper:** - **Leaderboard:** - **Point of Contact:** [Leonardo Ignacio Córdoba](https://www.linkedin.com/in/leonardo-ignacio-c%C3%B3rdoba/) ### Dataset Summary CC-NEWS-ES is a Spanish-language dataset of news. The corpus was generated by extracting the Spanish articles from CC-NEWS (news index of Common Crawl) of 2019. For doing that FastText model was used for language prediction. It contains a total of 7,473,286 texts and 1,812,009,283 words distributed as follows: |domain | texts | words | |:----|-----------------:|-----------------:| | ar | 532703 | 1.45127e+08 | | bo | 29557 | 7.28996e+06 | | br | 107 | 14207 | | cl | 116661 | 3.34633e+07 | | co | 78662 | 1.92649e+07 | | com | 3650950 | 8.44094e+08 | | cr | 16542 | 3.82075e+06 | | es |1838790 | 4.82943e+08 | | gt | 4833 | 838121 | | hn | 36559 | 5.49933e+06 | | mx | 724908 | 1.62198e+08 | | ni | 40643 | 1.08501e+07 | | pa | 18447 | 4.34724e+06 | | pe | 230962 | 3.52123e+07 | | pr | 7756 | 1.6633e+06 | | py | 30651 | 2.08077e+07 | | sv | 454 | 353145 | | uy | 80948 | 2.72562e+07 | | ve | 33148 | 6.96578e+06 | ### Supported Tasks and Leaderboards TODO - ### Languages The text is in Spanish. The BCP-47 code for Spanish is es. ## Dataset Structure ### Data Instances Each data instance contains the following features: ... - country: top level domain, usually refers to a country (except in the case of .com). - text: body of the news - id: internal id An example from CC-NEWS-ES looks like the following: ``` {'country': 'py', 'text': '“La que asumió es una mujer que está en línea de sucesión. La policía, ni los militares están en el Palacio, lo que ella dijo fue que no se podía seguir reprimiendo al pueblo", manifestó este jueves el senador colorado, Enrique Riera, sobre la asunción presidencial en Bolivia de la senadora opositora, Jeanine Áñez,Riera agregó que Evo Morales el que "escapó y abandonó" a su pueblo al ir como asilado a México. En ese sentido, dijo que irónicamente, el expresidente boliviano no eligió como destino a Venezuela, Nicaragua ni a Cuba.Sostuvo que nos de debe utilizar a las instituciones democráticas y republicanas para llegar al poder, cambiando Constituciones y prorrogando mandatos una y otra vez. “El amigo Morales no respetó absolutamente nada”, subrayó.Por otra parte, el senador colorado mencionó que los fiscales y jueces bolivianos deberían tener el "coraje" de investigar el origen de la riqueza de Morales.Habló también sobre la situación en Venezuela y mencionó que Nicolás Maduro no cae, porque "toda la FFAA está contaminada de narcotráfico". El hombre cuenta con orden de prisión en su país por los ilícitos de Tráfico de Drogas y Asociación Criminal, según el Consejo Nacional de Justicia del Brasil.La agente fiscal Liliana Denice Duarte, titular de la Unidad Fiscal Nº 1 de Presidente Franco, requirió la expulsión del extranjero y la jueza Carina Frutos Recalde, mediante Auto Interlocutorio (A.I.) N° 2.153, dio curso favorable al pedido del Ministerio Público. Esto considerando la alta expectativa de pena que tiene el supuesto delincuente en su país.La detención ...', 'id': 7328086} Note: the text is shortened for simplicity. ``` ### Data Fields - ... - ... ### Data Splits ... ## Dataset Creation ### Curation Rationale [N/A] ### Source Data #### Initial Data Collection and Normalization TODO #### Who are the source language producers? Common Crawl: https://commoncrawl.org/ ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset ... ### Discussion of Biases [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators This dataset is maintained by [Leonardo Ignacio Córdoba](https://www.linkedin.com/in/leonardo-ignacio-c%C3%B3rdoba/) and was built with the help of [María Gaska](https://www.linkedin.com/in/mfgaska/). ### Licensing Information [N/A] ### Citation Information TODO ### Contributions [N/A]