| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:212930 |
| - loss:CosineSimilarityLoss |
| base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
| widget: |
| - source_sentence: analisis perekonomian indonesia triwulan i 2007 |
| sentences: |
| - Indikator Ekonomi Februari 2017 |
| - Perkembangan Harga Produsen Gabah Maret 2021 |
| - Hasil Survei Komoditas Perikanan Potensi 2021 Profil Rumah Tangga Usaha Budidaya |
| Rumput Laut |
| - source_sentence: Analisis indikator ekonomi Indonesia September 2023 |
| sentences: |
| - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan |
| Negara Oktober 2011 |
| - April 2015 Harga Grosir Naik 0,17% |
| - Direktori Eksportir Indonesia 2014 |
| - source_sentence: Ekonomi Indonesia awal 2009 |
| sentences: |
| - Statistik Hotel dan Akomodasi Lainnya di Indonesia 2005 |
| - Neraca Pemerintahan Pusat Indonesia Triwulanan 2007-2013:2 |
| - Persentase Rumah Tangga menurut Provinsi dan Sumber Penerangan Listrik PLN, 1993-2022 |
| - source_sentence: Berapa persen kenaikan ekspor Indonesia pada Oktober 2024 dibandingkan |
| September 2024? |
| sentences: |
| - Impor Indonesia mengalami penurunan sebesar 5% pada bulan Oktober 2024. |
| - Statistik Pendidikan 2009 |
| - Penduduk Jambi Hasil Sensus Penduduk SP2000 |
| - source_sentence: Berapa persen deflasi ysng terjadi paa Maret 2010? |
| sentences: |
| - Pada Bulan Maret 2010 Terjadi Deflasi Sebesar 0,14 Persen. |
| - Analisis Rumah Tangga Usaha Hortikultura di Indonesia Hasil Sensus Pertanian 2013 |
| - Inflasi September 2008 sebesar 0,97 persen. |
| 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 2 dev |
| type: allstats-semantic-search-v1-2-dev |
| metrics: |
| - type: pearson_cosine |
| value: 0.9923210175659568 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9293313388011538 |
| 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: 0.9929329799075536 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9283010769773397 |
| 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) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 --> |
| - **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) |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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-v1-2") |
| # Run inference |
| sentences = [ |
| 'Berapa persen deflasi ysng terjadi paa Maret 2010?', |
| 'Pada Bulan Maret 2010 Terjadi Deflasi Sebesar 0,14 Persen.', |
| 'Inflasi September 2008 sebesar 0,97 persen.', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Semantic Similarity |
|
|
| * Datasets: `allstats-semantic-search-v1-2-dev` and `allstat-semantic-search-v1-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | allstats-semantic-search-v1-2-dev | allstat-semantic-search-v1-test | |
| |:--------------------|:----------------------------------|:--------------------------------| |
| | pearson_cosine | 0.9923 | 0.9929 | |
| | **spearman_cosine** | **0.9293** | **0.9283** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## 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 [c477abf](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/c477abfd5da1a6968bad673a50c71e17b62f39b4) |
| * Size: 212,930 training samples |
| * Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 11.52 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.85 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|:------------------| |
| | <code>studi tentang kemiskinan urban</code> | <code>Perkembangan Mingguan Harga Eceran Beberapa Bahan Pokok di Ibukota Provinsi Seluruh Indonesia (Juli-Desember 2018)</code> | <code>0.1</code> | |
| | <code>Harga gabah di tingkat produsen bulan September</code> | <code>Upah Buruh Juli 2020</code> | <code>0.1</code> | |
| | <code>Data perusahaan konstruksi di wilayah timur Indonesia thn 2013</code> | <code>Direktori Perusahaan Konstruksi 2013 Buku 6 Maluku dan Papua</code> | <code>0.92</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](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 [c477abf](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/c477abfd5da1a6968bad673a50c71e17b62f39b4) |
| * Size: 26,616 evaluation samples |
| * Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 11.33 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.6 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:--------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------|:------------------| |
| | <code>Informasi potensi deas di Maluku 011</code> | <code>Statistik Potensi Desa Provinsi Maluku 2011</code> | <code>0.87</code> | |
| | <code>Berapa persen kenaikan jumlah penumpang angkutan udara internasional pada Januari 2024 dibandingkan Desember 2023?</code> | <code>Kenaikan jumlah penumpang bulan lainnya</code> | <code>0.0</code> | |
| | <code>informasi tentang potensi desa jambi tahun 2005</code> | <code>Statistik Potensi Desa Provinsi Jambi 2005</code> | <code>0.85</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](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`: 6 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: True |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `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`: 6 |
| - `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 |
|
|
| </details> |
|
|
| ### Training Logs |
| <details><summary>Click to expand</summary> |
|
|
| | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-search-v1-2-dev_spearman_cosine | allstat-semantic-search-v1-test_spearman_cosine | |
| |:------:|:-----:|:-------------:|:---------------:|:-------------------------------------------------:|:-----------------------------------------------:| |
| | 0.0376 | 250 | 0.0734 | 0.0464 | 0.6927 | - | |
| | 0.0751 | 500 | 0.043 | 0.0362 | 0.7146 | - | |
| | 0.1127 | 750 | 0.0353 | 0.0288 | 0.7364 | - | |
| | 0.1503 | 1000 | 0.0271 | 0.0274 | 0.7571 | - | |
| | 0.1878 | 1250 | 0.0241 | 0.0225 | 0.7738 | - | |
| | 0.2254 | 1500 | 0.0228 | 0.0203 | 0.7699 | - | |
| | 0.2630 | 1750 | 0.0207 | 0.0197 | 0.7881 | - | |
| | 0.3005 | 2000 | 0.0187 | 0.0191 | 0.7900 | - | |
| | 0.3381 | 2250 | 0.0194 | 0.0183 | 0.7794 | - | |
| | 0.3757 | 2500 | 0.0182 | 0.0178 | 0.7870 | - | |
| | 0.4132 | 2750 | 0.0198 | 0.0183 | 0.8009 | - | |
| | 0.4508 | 3000 | 0.0189 | 0.0182 | 0.7912 | - | |
| | 0.4884 | 3250 | 0.0177 | 0.0168 | 0.7963 | - | |
| | 0.5259 | 3500 | 0.0178 | 0.0173 | 0.7920 | - | |
| | 0.5635 | 3750 | 0.017 | 0.0183 | 0.8014 | - | |
| | 0.6011 | 4000 | 0.0186 | 0.0180 | 0.7777 | - | |
| | 0.6386 | 4250 | 0.0187 | 0.0167 | 0.7976 | - | |
| | 0.6762 | 4500 | 0.015 | 0.0154 | 0.8194 | - | |
| | 0.7137 | 4750 | 0.0158 | 0.0157 | 0.8062 | - | |
| | 0.7513 | 5000 | 0.0152 | 0.0148 | 0.8117 | - | |
| | 0.7889 | 5250 | 0.0148 | 0.0149 | 0.8115 | - | |
| | 0.8264 | 5500 | 0.0146 | 0.0141 | 0.8175 | - | |
| | 0.8640 | 5750 | 0.0154 | 0.0144 | 0.7951 | - | |
| | 0.9016 | 6000 | 0.0155 | 0.0152 | 0.8163 | - | |
| | 0.9391 | 6250 | 0.0145 | 0.0136 | 0.8216 | - | |
| | 0.9767 | 6500 | 0.0149 | 0.0149 | 0.8140 | - | |
| | 1.0143 | 6750 | 0.0132 | 0.0132 | 0.8179 | - | |
| | 1.0518 | 7000 | 0.0108 | 0.0124 | 0.8232 | - | |
| | 1.0894 | 7250 | 0.0109 | 0.0120 | 0.8330 | - | |
| | 1.1270 | 7500 | 0.0112 | 0.0132 | 0.8219 | - | |
| | 1.1645 | 7750 | 0.0116 | 0.0124 | 0.8226 | - | |
| | 1.2021 | 8000 | 0.0121 | 0.0120 | 0.8151 | - | |
| | 1.2397 | 8250 | 0.0109 | 0.0119 | 0.8384 | - | |
| | 1.2772 | 8500 | 0.0103 | 0.0114 | 0.8415 | - | |
| | 1.3148 | 8750 | 0.0105 | 0.0116 | 0.8191 | - | |
| | 1.3524 | 9000 | 0.0104 | 0.0122 | 0.8292 | - | |
| | 1.3899 | 9250 | 0.0108 | 0.0117 | 0.8292 | - | |
| | 1.4275 | 9500 | 0.011 | 0.0118 | 0.8339 | - | |
| | 1.4651 | 9750 | 0.0105 | 0.0106 | 0.8367 | - | |
| | 1.5026 | 10000 | 0.0093 | 0.0098 | 0.8467 | - | |
| | 1.5402 | 10250 | 0.0105 | 0.0101 | 0.8334 | - | |
| | 1.5778 | 10500 | 0.0102 | 0.0106 | 0.8324 | - | |
| | 1.6153 | 10750 | 0.01 | 0.0097 | 0.8472 | - | |
| | 1.6529 | 11000 | 0.0106 | 0.0098 | 0.8378 | - | |
| | 1.6905 | 11250 | 0.0088 | 0.0095 | 0.8531 | - | |
| | 1.7280 | 11500 | 0.0085 | 0.0095 | 0.8409 | - | |
| | 1.7656 | 11750 | 0.0089 | 0.0091 | 0.8431 | - | |
| | 1.8032 | 12000 | 0.0083 | 0.0088 | 0.8524 | - | |
| | 1.8407 | 12250 | 0.0082 | 0.0088 | 0.8591 | - | |
| | 1.8783 | 12500 | 0.0078 | 0.0092 | 0.8478 | - | |
| | 1.9159 | 12750 | 0.009 | 0.0085 | 0.8480 | - | |
| | 1.9534 | 13000 | 0.0082 | 0.0089 | 0.8465 | - | |
| | 1.9910 | 13250 | 0.0076 | 0.0085 | 0.8564 | - | |
| | 2.0285 | 13500 | 0.0059 | 0.0082 | 0.8602 | - | |
| | 2.0661 | 13750 | 0.0073 | 0.0081 | 0.8558 | - | |
| | 2.1037 | 14000 | 0.0075 | 0.0081 | 0.8492 | - | |
| | 2.1412 | 14250 | 0.0066 | 0.0077 | 0.8520 | - | |
| | 2.1788 | 14500 | 0.0066 | 0.0076 | 0.8599 | - | |
| | 2.2164 | 14750 | 0.007 | 0.0080 | 0.8589 | - | |
| | 2.2539 | 15000 | 0.0065 | 0.0076 | 0.8552 | - | |
| | 2.2915 | 15250 | 0.0071 | 0.0075 | 0.8604 | - | |
| | 2.3291 | 15500 | 0.0062 | 0.0073 | 0.8714 | - | |
| | 2.3666 | 15750 | 0.0058 | 0.0069 | 0.8714 | - | |
| | 2.4042 | 16000 | 0.0066 | 0.0072 | 0.8570 | - | |
| | 2.4418 | 16250 | 0.0058 | 0.0069 | 0.8757 | - | |
| | 2.4793 | 16500 | 0.0059 | 0.0067 | 0.8726 | - | |
| | 2.5169 | 16750 | 0.0057 | 0.0067 | 0.8663 | - | |
| | 2.5545 | 17000 | 0.0058 | 0.0068 | 0.8703 | - | |
| | 2.5920 | 17250 | 0.0058 | 0.0068 | 0.8765 | - | |
| | 2.6296 | 17500 | 0.006 | 0.0067 | 0.8729 | - | |
| | 2.6672 | 17750 | 0.0057 | 0.0067 | 0.8689 | - | |
| | 2.7047 | 18000 | 0.0055 | 0.0065 | 0.8750 | - | |
| | 2.7423 | 18250 | 0.0056 | 0.0066 | 0.8734 | - | |
| | 2.7799 | 18500 | 0.0053 | 0.0062 | 0.8745 | - | |
| | 2.8174 | 18750 | 0.0053 | 0.0062 | 0.8814 | - | |
| | 2.8550 | 19000 | 0.0048 | 0.0063 | 0.8839 | - | |
| | 2.8926 | 19250 | 0.005 | 0.0063 | 0.8741 | - | |
| | 2.9301 | 19500 | 0.0063 | 0.0061 | 0.8752 | - | |
| | 2.9677 | 19750 | 0.0052 | 0.0059 | 0.8790 | - | |
| | 3.0053 | 20000 | 0.0049 | 0.0058 | 0.8825 | - | |
| | 3.0428 | 20250 | 0.0042 | 0.0059 | 0.8787 | - | |
| | 3.0804 | 20500 | 0.0043 | 0.0056 | 0.8839 | - | |
| | 3.1180 | 20750 | 0.0036 | 0.0058 | 0.8870 | - | |
| | 3.1555 | 21000 | 0.004 | 0.0056 | 0.8825 | - | |
| | 3.1931 | 21250 | 0.0041 | 0.0056 | 0.8884 | - | |
| | 3.2307 | 21500 | 0.004 | 0.0054 | 0.8872 | - | |
| | 3.2682 | 21750 | 0.0044 | 0.0052 | 0.8838 | - | |
| | 3.3058 | 22000 | 0.0036 | 0.0053 | 0.8904 | - | |
| | 3.3434 | 22250 | 0.0036 | 0.0054 | 0.8898 | - | |
| | 3.3809 | 22500 | 0.0037 | 0.0051 | 0.8938 | - | |
| | 3.4185 | 22750 | 0.0036 | 0.0051 | 0.8953 | - | |
| | 3.4560 | 23000 | 0.0036 | 0.0051 | 0.8935 | - | |
| | 3.4936 | 23250 | 0.004 | 0.0049 | 0.8955 | - | |
| | 3.5312 | 23500 | 0.0033 | 0.0051 | 0.8912 | - | |
| | 3.5687 | 23750 | 0.0037 | 0.0048 | 0.8995 | - | |
| | 3.6063 | 24000 | 0.0037 | 0.0048 | 0.8887 | - | |
| | 3.6439 | 24250 | 0.0037 | 0.0048 | 0.8921 | - | |
| | 3.6814 | 24500 | 0.0034 | 0.0046 | 0.9001 | - | |
| | 3.7190 | 24750 | 0.0041 | 0.0048 | 0.9008 | - | |
| | 3.7566 | 25000 | 0.0037 | 0.0048 | 0.8928 | - | |
| | 3.7941 | 25250 | 0.0038 | 0.0049 | 0.8949 | - | |
| | 3.8317 | 25500 | 0.0037 | 0.0045 | 0.9029 | - | |
| | 3.8693 | 25750 | 0.0034 | 0.0057 | 0.8962 | - | |
| | 3.9068 | 26000 | 0.0035 | 0.0047 | 0.8963 | - | |
| | 3.9444 | 26250 | 0.0039 | 0.0044 | 0.9026 | - | |
| | 3.9820 | 26500 | 0.0034 | 0.0044 | 0.8994 | - | |
| | 4.0195 | 26750 | 0.0029 | 0.0042 | 0.9039 | - | |
| | 4.0571 | 27000 | 0.0025 | 0.0040 | 0.9047 | - | |
| | 4.0947 | 27250 | 0.0027 | 0.0041 | 0.9033 | - | |
| | 4.1322 | 27500 | 0.0027 | 0.0041 | 0.9034 | - | |
| | 4.1698 | 27750 | 0.0025 | 0.0040 | 0.9040 | - | |
| | 4.2074 | 28000 | 0.0033 | 0.0041 | 0.9079 | - | |
| | 4.2449 | 28250 | 0.0027 | 0.0040 | 0.9078 | - | |
| | 4.2825 | 28500 | 0.0024 | 0.0040 | 0.9059 | - | |
| | 4.3201 | 28750 | 0.0026 | 0.0040 | 0.9084 | - | |
| | 4.3576 | 29000 | 0.0021 | 0.0039 | 0.9101 | - | |
| | 4.3952 | 29250 | 0.0024 | 0.0040 | 0.9081 | - | |
| | 4.4328 | 29500 | 0.0024 | 0.0039 | 0.9128 | - | |
| | 4.4703 | 29750 | 0.0027 | 0.0039 | 0.9067 | - | |
| | 4.5079 | 30000 | 0.003 | 0.0038 | 0.9120 | - | |
| | 4.5455 | 30250 | 0.0024 | 0.0037 | 0.9140 | - | |
| | 4.5830 | 30500 | 0.0025 | 0.0037 | 0.9116 | - | |
| | 4.6206 | 30750 | 0.0023 | 0.0037 | 0.9124 | - | |
| | 4.6582 | 31000 | 0.0026 | 0.0036 | 0.9161 | - | |
| | 4.6957 | 31250 | 0.0021 | 0.0036 | 0.9155 | - | |
| | 4.7333 | 31500 | 0.0025 | 0.0035 | 0.9147 | - | |
| | 4.7708 | 31750 | 0.0023 | 0.0035 | 0.9171 | - | |
| | 4.8084 | 32000 | 0.0024 | 0.0035 | 0.9153 | - | |
| | 4.8460 | 32250 | 0.002 | 0.0035 | 0.9153 | - | |
| | 4.8835 | 32500 | 0.0025 | 0.0034 | 0.9173 | - | |
| | 4.9211 | 32750 | 0.0018 | 0.0035 | 0.9180 | - | |
| | 4.9587 | 33000 | 0.0021 | 0.0035 | 0.9201 | - | |
| | 4.9962 | 33250 | 0.0019 | 0.0035 | 0.9205 | - | |
| | 5.0338 | 33500 | 0.0016 | 0.0034 | 0.9223 | - | |
| | 5.0714 | 33750 | 0.0016 | 0.0034 | 0.9217 | - | |
| | 5.1089 | 34000 | 0.0015 | 0.0033 | 0.9208 | - | |
| | 5.1465 | 34250 | 0.002 | 0.0034 | 0.9234 | - | |
| | 5.1841 | 34500 | 0.0017 | 0.0033 | 0.9212 | - | |
| | 5.2216 | 34750 | 0.002 | 0.0033 | 0.9212 | - | |
| | 5.2592 | 35000 | 0.0015 | 0.0032 | 0.9241 | - | |
| | 5.2968 | 35250 | 0.002 | 0.0031 | 0.9232 | - | |
| | 5.3343 | 35500 | 0.0017 | 0.0031 | 0.9251 | - | |
| | 5.3719 | 35750 | 0.0015 | 0.0031 | 0.9256 | - | |
| | 5.4095 | 36000 | 0.0018 | 0.0031 | 0.9246 | - | |
| | 5.4470 | 36250 | 0.0015 | 0.0030 | 0.9257 | - | |
| | 5.4846 | 36500 | 0.0017 | 0.0030 | 0.9261 | - | |
| | 5.5222 | 36750 | 0.0018 | 0.0030 | 0.9251 | - | |
| | 5.5597 | 37000 | 0.0016 | 0.0030 | 0.9270 | - | |
| | 5.5973 | 37250 | 0.0016 | 0.0029 | 0.9275 | - | |
| | 5.6349 | 37500 | 0.0017 | 0.0029 | 0.9283 | - | |
| | 5.6724 | 37750 | 0.0015 | 0.0029 | 0.9277 | - | |
| | 5.7100 | 38000 | 0.0017 | 0.0029 | 0.9286 | - | |
| | 5.7476 | 38250 | 0.0015 | 0.0029 | 0.9284 | - | |
| | 5.7851 | 38500 | 0.0015 | 0.0029 | 0.9286 | - | |
| | 5.8227 | 38750 | 0.0014 | 0.0029 | 0.9287 | - | |
| | 5.8603 | 39000 | 0.0015 | 0.0028 | 0.9290 | - | |
| | 5.8978 | 39250 | 0.0014 | 0.0028 | 0.9291 | - | |
| | 5.9354 | 39500 | 0.0014 | 0.0028 | 0.9293 | - | |
| | 5.9730 | 39750 | 0.0015 | 0.0028 | 0.9293 | - | |
| | 6.0 | 39930 | - | - | - | 0.9283 | |
|
|
| </details> |
|
|
| ### 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", |
| } |
| ``` |
|
|
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