--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:212917 - loss:CosineSimilarityLoss base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 widget: - source_sentence: statistik neraca arus dana indonesia sentences: - Statistik Kelapa Sawit Indonesia 2012 - Distribusi Perdagangan Komoditas Kedelai Indonesia 2023 - Data Runtun Statistik Konstruksi 1990-2010 - source_sentence: Seberapa besar kenaikan produksi IBS pada Triwulan IV Tahun 2013 dibandingkan Triwulan IV Tahun Sebelumnya? sentences: - Pertumbuhan PDB 2013 Mencapai 5,78 Persen - Statistik Komuter Gerbangkertosusila Hasil Survei Komuter Gerbangkertosusila 2017 - Statistik Penduduk Lanjut Usia Provinsi Jawa Timur 2010-Hasil Sensus Penduduk 2010 - source_sentence: 'Penduduk Papua: migrasi 2015' sentences: - Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi dan jenis pekerjaan utama, 2019 - Statistik Pemotongan Ternak 2010 dan 2011 - Statistik Harga Produsen Pertanian Sub Sektor Tanaman Pangan, Hortikultura dan Perkebunan Rakyat 2010 - source_sentence: statistik konstruksi 2022 sentences: - Studi Modal Sosial 2006 - BRS upah buruh agustus 2018 - Statistik Perdagangan Luar Negeri Indonesia Ekspor 2006 vol 1 - source_sentence: Statistik ekspor Indonesia Maret 2202 sentences: - Produk Domestik Bruto Indonesia Triwulanan 2006-2010 - Indeks Perilaku Anti Korupsi (IPAK) Indonesia 2023 sebesar 3,92, menurun dibandingkan IPAK 2022 - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Januari 2023 datasets: - yahyaabd/allstats-semantic-search-synthetic-dataset-v1 pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstats semantic search v1 dev type: allstats-semantic-search-v1-dev metrics: - type: pearson_cosine value: 0.9894566758405579 name: Pearson Cosine - type: spearman_cosine value: 0.9072484378842124 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstat semantic search v1 test type: allstat-semantic-search-v1-test metrics: - type: pearson_cosine value: 0.9895284407960067 name: Pearson Cosine - type: spearman_cosine value: 0.9074137706349162 name: Spearman Cosine --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("yahyaabd/allstats-semantic-search-model-v1") # Run inference sentences = [ 'Statistik ekspor Indonesia Maret 2202', 'Produk Domestik Bruto Indonesia Triwulanan 2006-2010', 'Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Januari 2023', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `allstats-semantic-search-v1-dev` and `allstat-semantic-search-v1-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | allstats-semantic-search-v1-dev | allstat-semantic-search-v1-test | |:--------------------|:--------------------------------|:--------------------------------| | pearson_cosine | 0.9895 | 0.9895 | | **spearman_cosine** | **0.9072** | **0.9074** | ## Training Details ### Training Dataset #### allstats-semantic-search-synthetic-dataset-v1 * Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [06f849a](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/06f849af5602fea6ce00e5ecdd9a99cd0cafc2de) * Size: 212,917 training samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | doc | label | |:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:------------------| | ringkasan aktivitas badan pusat statistik tahun 2018 | Statistik Harga Produsen sektor pertanian di indonesia 2008 | 0.1 | | indikator kesejahteraan petani rejang lebong 2015 | Diagram Timbang Nilai Tukar Petani Kabupaten Rejang Lebong 2015 | 0.82 | | Berapa persen kenaikan kunjungan wisatawan mancanegara pada April 2024? | Indeks Perilaku Anti Korupsi (IPAK) Indonesia 2023 sebesar 3,92, menurun dibandingkan IPAK 2022 | 0.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### allstats-semantic-search-synthetic-dataset-v1 * Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [06f849a](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/06f849af5602fea6ce00e5ecdd9a99cd0cafc2de) * Size: 26,614 evaluation samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | doc | label | |:-----------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:------------------| | Laporan bulanan ekonomi Indonesia bulan November 201 | Laporan Bulanan Data Sosial Ekonomi November 2021 | 0.92 | | pekerjaan layak di indonesia tahun 2022: data dan analisis | Statistik Penduduk Lanjut Usia Provinsi Papua Barat 2010-Hasil Sensus Penduduk 2010 | 0.09 | | Tabel pendapatan rata-rata pekerja lepas berdasarkan provinsi dan pendidikan tahun 2021 | Nilai Impor Kendaraan Bermotor Menurut Negara Asal Utama (Nilai CIF:juta US$), 2018-2023 | 0.1 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-search-v1-dev_spearman_cosine | allstat-semantic-search-v1-test_spearman_cosine | |:------:|:-----:|:-------------:|:---------------:|:-----------------------------------------------:|:-----------------------------------------------:| | 0.0376 | 250 | 0.0683 | 0.0432 | 0.6955 | - | | 0.0751 | 500 | 0.0393 | 0.0322 | 0.7230 | - | | 0.1127 | 750 | 0.0321 | 0.0270 | 0.7476 | - | | 0.1503 | 1000 | 0.0255 | 0.0226 | 0.7789 | - | | 0.1879 | 1250 | 0.024 | 0.0213 | 0.7683 | - | | 0.2254 | 1500 | 0.022 | 0.0199 | 0.7727 | - | | 0.2630 | 1750 | 0.0219 | 0.0195 | 0.7853 | - | | 0.3006 | 2000 | 0.0202 | 0.0188 | 0.7795 | - | | 0.3381 | 2250 | 0.0191 | 0.0187 | 0.7943 | - | | 0.3757 | 2500 | 0.0198 | 0.0178 | 0.7842 | - | | 0.4133 | 2750 | 0.0179 | 0.0184 | 0.7974 | - | | 0.4509 | 3000 | 0.0179 | 0.0194 | 0.7810 | - | | 0.4884 | 3250 | 0.0182 | 0.0168 | 0.8080 | - | | 0.5260 | 3500 | 0.0174 | 0.0164 | 0.8131 | - | | 0.5636 | 3750 | 0.0174 | 0.0154 | 0.8113 | - | | 0.6011 | 4000 | 0.0169 | 0.0157 | 0.7981 | - | | 0.6387 | 4250 | 0.0152 | 0.0146 | 0.8099 | - | | 0.6763 | 4500 | 0.0148 | 0.0147 | 0.8091 | - | | 0.7139 | 4750 | 0.0145 | 0.0145 | 0.8178 | - | | 0.7514 | 5000 | 0.014 | 0.0139 | 0.8184 | - | | 0.7890 | 5250 | 0.0145 | 0.0130 | 0.8166 | - | | 0.8266 | 5500 | 0.0134 | 0.0129 | 0.8306 | - | | 0.8641 | 5750 | 0.013 | 0.0122 | 0.8251 | - | | 0.9017 | 6000 | 0.0136 | 0.0130 | 0.8265 | - | | 0.9393 | 6250 | 0.0123 | 0.0126 | 0.8224 | - | | 0.9769 | 6500 | 0.0113 | 0.0120 | 0.8305 | - | | 1.0144 | 6750 | 0.0129 | 0.0117 | 0.8204 | - | | 1.0520 | 7000 | 0.0106 | 0.0116 | 0.8284 | - | | 1.0896 | 7250 | 0.01 | 0.0116 | 0.8303 | - | | 1.1271 | 7500 | 0.0096 | 0.0110 | 0.8303 | - | | 1.1647 | 7750 | 0.01 | 0.0113 | 0.8305 | - | | 1.2023 | 8000 | 0.0116 | 0.0108 | 0.8300 | - | | 1.2399 | 8250 | 0.0095 | 0.0104 | 0.8432 | - | | 1.2774 | 8500 | 0.009 | 0.0104 | 0.8370 | - | | 1.3150 | 8750 | 0.0101 | 0.0102 | 0.8434 | - | | 1.3526 | 9000 | 0.01 | 0.0097 | 0.8450 | - | | 1.3901 | 9250 | 0.0097 | 0.0103 | 0.8286 | - | | 1.4277 | 9500 | 0.0092 | 0.0096 | 0.8393 | - | | 1.4653 | 9750 | 0.0093 | 0.0089 | 0.8480 | - | | 1.5029 | 10000 | 0.0088 | 0.0090 | 0.8439 | - | | 1.5404 | 10250 | 0.0087 | 0.0089 | 0.8569 | - | | 1.5780 | 10500 | 0.0082 | 0.0088 | 0.8488 | - | | 1.6156 | 10750 | 0.009 | 0.0089 | 0.8493 | - | | 1.6531 | 11000 | 0.0086 | 0.0086 | 0.8499 | - | | 1.6907 | 11250 | 0.0076 | 0.0083 | 0.8600 | - | | 1.7283 | 11500 | 0.0076 | 0.0081 | 0.8621 | - | | 1.7659 | 11750 | 0.0079 | 0.0081 | 0.8611 | - | | 1.8034 | 12000 | 0.0082 | 0.0085 | 0.8540 | - | | 1.8410 | 12250 | 0.0074 | 0.0081 | 0.8620 | - | | 1.8786 | 12500 | 0.007 | 0.0080 | 0.8639 | - | | 1.9161 | 12750 | 0.0071 | 0.0083 | 0.8450 | - | | 1.9537 | 13000 | 0.007 | 0.0076 | 0.8585 | - | | 1.9913 | 13250 | 0.0072 | 0.0074 | 0.8640 | - | | 2.0289 | 13500 | 0.0055 | 0.0069 | 0.8699 | - | | 2.0664 | 13750 | 0.0056 | 0.0068 | 0.8673 | - | | 2.1040 | 14000 | 0.0052 | 0.0066 | 0.8723 | - | | 2.1416 | 14250 | 0.0059 | 0.0069 | 0.8644 | - | | 2.1791 | 14500 | 0.0055 | 0.0068 | 0.8670 | - | | 2.2167 | 14750 | 0.005 | 0.0065 | 0.8723 | - | | 2.2543 | 15000 | 0.0053 | 0.0066 | 0.8766 | - | | 2.2919 | 15250 | 0.0057 | 0.0065 | 0.8782 | - | | 2.3294 | 15500 | 0.0053 | 0.0064 | 0.8749 | - | | 2.3670 | 15750 | 0.0056 | 0.0070 | 0.8708 | - | | 2.4046 | 16000 | 0.0058 | 0.0065 | 0.8731 | - | | 2.4421 | 16250 | 0.0047 | 0.0064 | 0.8793 | - | | 2.4797 | 16500 | 0.0049 | 0.0063 | 0.8801 | - | | 2.5173 | 16750 | 0.0051 | 0.0063 | 0.8782 | - | | 2.5549 | 17000 | 0.0053 | 0.0060 | 0.8799 | - | | 2.5924 | 17250 | 0.0051 | 0.0059 | 0.8825 | - | | 2.6300 | 17500 | 0.0048 | 0.0060 | 0.8761 | - | | 2.6676 | 17750 | 0.0055 | 0.0055 | 0.8773 | - | | 2.7051 | 18000 | 0.0045 | 0.0053 | 0.8833 | - | | 2.7427 | 18250 | 0.0041 | 0.0053 | 0.8868 | - | | 2.7803 | 18500 | 0.0051 | 0.0054 | 0.8811 | - | | 2.8179 | 18750 | 0.004 | 0.0052 | 0.8881 | - | | 2.8554 | 19000 | 0.0043 | 0.0053 | 0.8764 | - | | 2.8930 | 19250 | 0.0047 | 0.0051 | 0.8874 | - | | 2.9306 | 19500 | 0.0038 | 0.0051 | 0.8922 | - | | 2.9681 | 19750 | 0.0047 | 0.0050 | 0.8821 | - | | 3.0057 | 20000 | 0.0037 | 0.0048 | 0.8911 | - | | 3.0433 | 20250 | 0.0031 | 0.0048 | 0.8911 | - | | 3.0809 | 20500 | 0.0032 | 0.0046 | 0.8934 | - | | 3.1184 | 20750 | 0.0034 | 0.0046 | 0.8942 | - | | 3.1560 | 21000 | 0.0028 | 0.0045 | 0.8976 | - | | 3.1936 | 21250 | 0.0034 | 0.0045 | 0.8932 | - | | 3.2311 | 21500 | 0.003 | 0.0044 | 0.8959 | - | | 3.2687 | 21750 | 0.0033 | 0.0044 | 0.8961 | - | | 3.3063 | 22000 | 0.0029 | 0.0043 | 0.8995 | - | | 3.3439 | 22250 | 0.0029 | 0.0044 | 0.8978 | - | | 3.3814 | 22500 | 0.0027 | 0.0043 | 0.8998 | - | | 3.4190 | 22750 | 0.003 | 0.0043 | 0.9019 | - | | 3.4566 | 23000 | 0.0027 | 0.0042 | 0.8982 | - | | 3.4941 | 23250 | 0.0027 | 0.0042 | 0.9014 | - | | 3.5317 | 23500 | 0.0034 | 0.0042 | 0.9025 | - | | 3.5693 | 23750 | 0.003 | 0.0041 | 0.9027 | - | | 3.6069 | 24000 | 0.0029 | 0.0041 | 0.9003 | - | | 3.6444 | 24250 | 0.0027 | 0.0040 | 0.9023 | - | | 3.6820 | 24500 | 0.0027 | 0.0040 | 0.9035 | - | | 3.7196 | 24750 | 0.0033 | 0.0040 | 0.9042 | - | | 3.7571 | 25000 | 0.0028 | 0.0039 | 0.9053 | - | | 3.7947 | 25250 | 0.0027 | 0.0039 | 0.9049 | - | | 3.8323 | 25500 | 0.0033 | 0.0039 | 0.9057 | - | | 3.8699 | 25750 | 0.0025 | 0.0039 | 0.9075 | - | | 3.9074 | 26000 | 0.003 | 0.0039 | 0.9068 | - | | 3.9450 | 26250 | 0.0026 | 0.0039 | 0.9073 | - | | 3.9826 | 26500 | 0.0023 | 0.0038 | 0.9072 | - | | 4.0 | 26616 | - | - | - | 0.9074 |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.2.2+cu121 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ```