--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:360886 - loss:CoSENTLoss base_model: sentence-transformers/all-mpnet-base-v2 widget: - source_sentence: '|Immunosuppressant drug therapy (procedure)| : { |Method (attribute)| = |Administration - action (qualifier value)|, |Direct substance (attribute)| = |Auranofin (substance)| }, { |Has intent (attribute)| = |Therapeutic intent (qualifier value)| }' sentences: - Tofacitinib therapy (procedure) - Mural thrombus of right ventricle following acute myocardial infarction (disorder) - Neonatal botulism (disorder) - source_sentence: '|Injury of finger of left hand (disorder)| + |Traumatic blister of index finger (disorder)| + |Traumatic blister of left hand (disorder)| : { |Finding site (attribute)| = |Skin structure of left index finger (body structure)|, |Associated morphology (attribute)| = |Blister (morphologic abnormality)| }, { |Due to (attribute)| = |Traumatic event (event)| }' sentences: - Cardiovascular system closure (procedure) - Entire skin of lower eyelid and periocular area (body structure) - Avulsion of nail unit of left little finger (disorder) - source_sentence: '|Evaluation finding (finding)| : { |Interprets (attribute)| = |Interferon gamma assay (procedure)|, |Has interpretation (attribute)| = |Positive (qualifier value)| }' sentences: - Gleason pattern (observable entity) - Interferon gamma assay positive (finding) - Intentional melphalan overdose (disorder) - source_sentence: '|Finding of specific antibody level (finding)| : { |Interprets (attribute)| = |Measurement of viral antibody (procedure)| }' sentences: - Lyme detected by immunoblot (finding) - Primary malignant neoplasm of accessory sinus (disorder) - Perfusion of lymphatics with hyperthermia (procedure) - source_sentence: '|Neoplasm of anterior wall of nasopharynx (disorder)| + |Neoplasm of uncertain behavior of nasopharynx (disorder)| : { |Finding site (attribute)| = |Structure of anterior wall of nasopharynx (body structure)|, |Associated morphology (attribute)| = |Neoplasm of uncertain behavior (morphologic abnormality)| }' sentences: - Secondary angle-closure glaucoma - synechial (disorder) - Neoplasm of uncertain behavior of lateral wall of nasopharynx (disorder) - Product containing precisely cefamandole (as cefamandole nafate) 1 gram/1 vial powder for conventional release solution for injection (clinical drug) pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.9048593944190657 name: Pearson Cosine - type: spearman_cosine value: 0.8556279874385214 name: Spearman Cosine --- # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the csv 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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - csv ### 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## 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("yyzheng00/all-mpnet-base-v2_snomed_expression") # Run inference sentences = [ '|Neoplasm of anterior wall of nasopharynx (disorder)| + |Neoplasm of uncertain behavior of nasopharynx (disorder)| : { |Finding site (attribute)| = |Structure of anterior wall of nasopharynx (body structure)|, |Associated morphology (attribute)| = |Neoplasm of uncertain behavior (morphologic abnormality)| }', 'Neoplasm of uncertain behavior of lateral wall of nasopharynx (disorder)', 'Secondary angle-closure glaucoma - synechial (disorder)', ] 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 * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.9049 | | **spearman_cosine** | **0.8556** | ## Training Details ### Training Dataset #### csv * Dataset: csv * Size: 360,886 training samples * Columns: text_a, text_b, and label * Approximate statistics based on the first 1000 samples: | | text_a | text_b | label | |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | text_a | text_b | label | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------|:---------------| | |Risk assessment (procedure)| : { |Method (attribute)| = |Evaluation - action (qualifier value)| }, { |Has focus (attribute)| = |At increased risk of ineffective tissue perfusion (finding)| } | Assessment of risk of ineffective tissue perfusion (procedure) | 1 | | |Chronic inflammatory disorder (disorder)| + |Chronic nervous system disorder (disorder)| + |Meningitis (disorder)| : { |Finding site (attribute)| = |Meninges structure (body structure)|, |Associated morphology (attribute)| = |Chronic inflammatory morphology (morphologic abnormality)| }, { |Clinical course (attribute)| = |Chronic (qualifier value)| } | Chronic meningitis (disorder) | 1 | | |Imaging of head (procedure)| + |Ultrasound procedure on topographic region (procedure)| : { |Method (attribute)| = |Ultrasound imaging - action (qualifier value)|, |Procedure site - Direct (attribute)| = |Head structure (body structure)| } | Imaging of brain (procedure) | 0 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### csv * Dataset: csv * Size: 360,886 evaluation samples * Columns: text_a, text_b, and label * Approximate statistics based on the first 1000 samples: | | text_a | text_b | label | |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | text_a | text_b | label | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------| | |Disorder of fetal abdominal region (disorder)| + |Fetal genitourinary abnormality (disorder)| + |Kidney disease (disorder)| : { |Occurrence (attribute)| = |Fetal period (qualifier value)|, |Finding site (attribute)| = |Kidney structure (body structure)|, |Associated morphology (attribute)| = |Morphologically abnormal structure (morphologic abnormality)|, |Pathological process (attribute)| = |Pathological developmental process (qualifier value)| } | Early urethral obstruction sequence (disorder) | 0 | | |Computed tomography of pelvis for brachytherapy planning (procedure)| + |Computed tomography of prostate for radiotherapy planning (procedure)| : { |Has focus (attribute)| = |Treatment planning for brachytherapy (procedure)| }, { |Method (attribute)| = |Computed tomography imaging - action (qualifier value)|, |Procedure site - Direct (attribute)| = |Prostatic structure (body structure)| } | Computed tomography of prostate with contrast for radiotherapy planning (procedure) | 0 | | |Product containing only hydroxyzine in oral dose form (medicinal product form)| : |Has manufactured dose form (attribute)| = |Conventional release oral capsule (dose form)|, |Has unit of presentation (attribute)| = |Capsule (unit of presentation)|, |Count of base of active ingredient (attribute)| = #1, { |Has precise active ingredient (attribute)| = |Hydroxyzine pamoate (substance)|, |Has basis of strength substance (attribute)| = |Hydroxyzine pamoate (substance)|, |Has presentation strength numerator value (attribute)| = #100, |Has presentation strength numerator unit (attribute)| = |milligram (qualifier value)|, |Has presentation strength denominator value (attribute)| = #1, |Has presentation strength denominator unit (attribute)| = |Capsule (unit of presentation)| } | Hydroxyzine pamoate 100mg capsule (product) | 1 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 1 - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | |:------:|:-----:|:-------------:|:---------------:|:-----------------------:| | 0.0055 | 100 | 5.2922 | 3.9427 | 0.6159 | | 0.0111 | 200 | 3.2766 | 2.8638 | 0.7437 | | 0.0166 | 300 | 2.8445 | 2.4816 | 0.7833 | | 0.0222 | 400 | 2.5209 | 2.2995 | 0.7974 | | 0.0277 | 500 | 2.5298 | 2.1033 | 0.8072 | | 0.0333 | 600 | 2.0427 | 2.1055 | 0.8114 | | 0.0388 | 700 | 2.1367 | 2.0634 | 0.8121 | | 0.0443 | 800 | 2.2486 | 1.7848 | 0.8210 | | 0.0499 | 900 | 1.921 | 1.9666 | 0.8190 | | 0.0554 | 1000 | 1.9962 | 1.9688 | 0.8180 | | 0.0610 | 1100 | 1.5203 | 2.0695 | 0.8187 | | 0.0665 | 1200 | 2.0616 | 1.7060 | 0.8223 | | 0.0720 | 1300 | 2.0793 | 1.8158 | 0.8254 | | 0.0776 | 1400 | 2.0766 | 1.8549 | 0.8213 | | 0.0831 | 1500 | 1.5608 | 1.8045 | 0.8241 | | 0.0887 | 1600 | 1.7671 | 1.9724 | 0.8196 | | 0.0942 | 1700 | 2.1665 | 2.2623 | 0.8033 | | 0.0998 | 1800 | 1.9596 | 1.8070 | 0.8224 | | 0.1053 | 1900 | 1.5704 | 1.8142 | 0.8265 | | 0.1108 | 2000 | 2.0749 | 2.0596 | 0.8205 | | 0.1164 | 2100 | 1.9445 | 1.7458 | 0.8279 | | 0.1219 | 2200 | 1.6043 | 2.0309 | 0.8242 | | 0.1275 | 2300 | 1.5723 | 1.7440 | 0.8286 | | 0.1330 | 2400 | 1.7905 | 1.5584 | 0.8319 | | 0.1385 | 2500 | 2.0777 | 1.7437 | 0.8254 | | 0.1441 | 2600 | 1.7563 | 1.6852 | 0.8322 | | 0.1496 | 2700 | 1.6565 | 1.8196 | 0.8268 | | 0.1552 | 2800 | 1.5064 | 1.6763 | 0.8302 | | 0.1607 | 2900 | 1.9221 | 1.7317 | 0.8279 | | 0.1663 | 3000 | 1.7803 | 1.8330 | 0.8225 | | 0.1718 | 3100 | 1.3559 | 1.9419 | 0.8278 | | 0.1773 | 3200 | 1.5309 | 1.5263 | 0.8345 | | 0.1829 | 3300 | 1.6429 | 1.7952 | 0.8290 | | 0.1884 | 3400 | 1.4676 | 1.8284 | 0.8270 | | 0.1940 | 3500 | 1.5167 | 1.6084 | 0.8295 | | 0.1995 | 3600 | 1.7605 | 1.6362 | 0.8334 | | 0.2050 | 3700 | 1.6812 | 1.4205 | 0.8348 | | 0.2106 | 3800 | 1.4537 | 1.6432 | 0.8341 | | 0.2161 | 3900 | 1.6718 | 1.2594 | 0.8382 | | 0.2217 | 4000 | 1.3892 | 1.4798 | 0.8351 | | 0.2272 | 4100 | 1.7261 | 1.3948 | 0.8354 | | 0.2328 | 4200 | 1.6611 | 1.4519 | 0.8368 | | 0.2383 | 4300 | 1.3181 | 1.2844 | 0.8389 | | 0.2438 | 4400 | 1.4356 | 1.3015 | 0.8392 | | 0.2494 | 4500 | 1.4077 | 1.3217 | 0.8381 | | 0.2549 | 4600 | 1.2534 | 1.5767 | 0.8340 | | 0.2605 | 4700 | 1.6881 | 1.2737 | 0.8398 | | 0.2660 | 4800 | 1.4572 | 1.2570 | 0.8408 | | 0.2715 | 4900 | 1.2339 | 1.1919 | 0.8423 | | 0.2771 | 5000 | 1.2871 | 1.3166 | 0.8398 | | 0.2826 | 5100 | 1.3532 | 1.4045 | 0.8360 | | 0.2882 | 5200 | 1.2731 | 1.4843 | 0.8384 | | 0.2937 | 5300 | 1.3776 | 1.1347 | 0.8423 | | 0.2993 | 5400 | 1.2179 | 1.5040 | 0.8373 | | 0.3048 | 5500 | 1.41 | 1.2401 | 0.8418 | | 0.3103 | 5600 | 1.3901 | 1.1494 | 0.8416 | | 0.3159 | 5700 | 1.4007 | 1.2487 | 0.8414 | | 0.3214 | 5800 | 1.3444 | 1.4062 | 0.8397 | | 0.3270 | 5900 | 1.3671 | 1.3194 | 0.8410 | | 0.3325 | 6000 | 1.2401 | 1.2642 | 0.8411 | | 0.3380 | 6100 | 1.4102 | 1.3317 | 0.8392 | | 0.3436 | 6200 | 1.1672 | 1.0846 | 0.8438 | | 0.3491 | 6300 | 1.3595 | 1.2747 | 0.8387 | | 0.3547 | 6400 | 1.0956 | 1.4071 | 0.8392 | | 0.3602 | 6500 | 1.539 | 1.2683 | 0.8413 | | 0.3658 | 6600 | 1.3078 | 1.2173 | 0.8430 | | 0.3713 | 6700 | 1.3562 | 1.0733 | 0.8447 | | 0.3768 | 6800 | 1.3009 | 1.3561 | 0.8408 | | 0.3824 | 6900 | 1.4319 | 1.1958 | 0.8432 | | 0.3879 | 7000 | 1.0702 | 1.1325 | 0.8437 | | 0.3935 | 7100 | 1.2339 | 0.9852 | 0.8465 | | 0.3990 | 7200 | 0.8772 | 1.2658 | 0.8419 | | 0.4045 | 7300 | 1.3411 | 1.1585 | 0.8438 | | 0.4101 | 7400 | 1.1518 | 1.1572 | 0.8439 | | 0.4156 | 7500 | 1.0287 | 0.9960 | 0.8456 | | 0.4212 | 7600 | 1.2913 | 1.1595 | 0.8437 | | 0.4267 | 7700 | 1.1006 | 1.1575 | 0.8437 | | 0.4323 | 7800 | 1.3463 | 1.0478 | 0.8459 | | 0.4378 | 7900 | 1.0428 | 1.0495 | 0.8461 | | 0.4433 | 8000 | 1.0657 | 1.0442 | 0.8465 | | 0.4489 | 8100 | 1.1002 | 1.0223 | 0.8475 | | 0.4544 | 8200 | 1.1596 | 1.0066 | 0.8474 | | 0.4600 | 8300 | 1.3218 | 1.0403 | 0.8460 | | 0.4655 | 8400 | 1.1482 | 1.1177 | 0.8457 | | 0.4710 | 8500 | 1.0033 | 1.1743 | 0.8448 | | 0.4766 | 8600 | 1.0772 | 1.1071 | 0.8464 | | 0.4821 | 8700 | 0.775 | 1.2731 | 0.8438 | | 0.4877 | 8800 | 0.8859 | 0.9293 | 0.8491 | | 0.4932 | 8900 | 0.7837 | 1.0760 | 0.8462 | | 0.4988 | 9000 | 0.7768 | 1.0135 | 0.8470 | | 0.5043 | 9100 | 1.0103 | 0.9691 | 0.8477 | | 0.5098 | 9200 | 1.0219 | 1.2059 | 0.8441 | | 0.5154 | 9300 | 0.9093 | 1.0895 | 0.8461 | | 0.5209 | 9400 | 1.0176 | 0.9229 | 0.8489 | | 0.5265 | 9500 | 1.3811 | 0.9470 | 0.8483 | | 0.5320 | 9600 | 0.8338 | 1.0048 | 0.8477 | | 0.5375 | 9700 | 0.7105 | 1.0591 | 0.8464 | | 0.5431 | 9800 | 1.0313 | 0.9789 | 0.8482 | | 0.5486 | 9900 | 1.0308 | 0.8741 | 0.8499 | | 0.5542 | 10000 | 0.7353 | 0.9419 | 0.8482 | | 0.5597 | 10100 | 0.7683 | 1.0695 | 0.8473 | | 0.5653 | 10200 | 1.1728 | 0.9705 | 0.8494 | | 0.5708 | 10300 | 0.8578 | 0.9633 | 0.8493 | | 0.5763 | 10400 | 1.0095 | 0.7799 | 0.8514 | | 0.5819 | 10500 | 1.0157 | 1.0333 | 0.8485 | | 0.5874 | 10600 | 0.8164 | 0.8596 | 0.8509 | | 0.5930 | 10700 | 0.9278 | 0.8256 | 0.8516 | | 0.5985 | 10800 | 0.5919 | 1.0104 | 0.8493 | | 0.6040 | 10900 | 0.6931 | 0.9957 | 0.8492 | | 0.6096 | 11000 | 1.1545 | 0.9758 | 0.8494 | | 0.6151 | 11100 | 1.1061 | 1.0360 | 0.8493 | | 0.6207 | 11200 | 0.7954 | 0.9362 | 0.8509 | | 0.6262 | 11300 | 0.6365 | 0.9504 | 0.8511 | | 0.6318 | 11400 | 0.992 | 0.8553 | 0.8521 | | 0.6373 | 11500 | 0.6971 | 0.8763 | 0.8520 | | 0.6428 | 11600 | 0.8162 | 0.9527 | 0.8504 | | 0.6484 | 11700 | 0.8973 | 0.8722 | 0.8519 | | 0.6539 | 11800 | 0.7652 | 0.9417 | 0.8510 | | 0.6595 | 11900 | 0.7305 | 0.8955 | 0.8519 | | 0.6650 | 12000 | 0.8555 | 0.9007 | 0.8510 | | 0.6705 | 12100 | 0.7165 | 0.7924 | 0.8530 | | 0.6761 | 12200 | 0.7939 | 0.8607 | 0.8516 | | 0.6816 | 12300 | 0.9873 | 0.7780 | 0.8533 | | 0.6872 | 12400 | 0.7197 | 0.9380 | 0.8508 | | 0.6927 | 12500 | 1.076 | 0.8041 | 0.8531 | | 0.6983 | 12600 | 0.6853 | 0.8800 | 0.8517 | | 0.7038 | 12700 | 0.9403 | 0.8181 | 0.8527 | | 0.7093 | 12800 | 0.8598 | 0.7641 | 0.8536 | | 0.7149 | 12900 | 0.628 | 0.7479 | 0.8540 | | 0.7204 | 13000 | 1.0517 | 0.7611 | 0.8536 | | 0.7260 | 13100 | 0.5099 | 0.8426 | 0.8521 | | 0.7315 | 13200 | 0.751 | 0.8133 | 0.8526 | | 0.7370 | 13300 | 0.572 | 0.8344 | 0.8524 | | 0.7426 | 13400 | 0.8213 | 0.7869 | 0.8528 | | 0.7481 | 13500 | 0.6046 | 0.7810 | 0.8528 | | 0.7537 | 13600 | 0.7211 | 0.7502 | 0.8537 | | 0.7592 | 13700 | 0.7443 | 0.7398 | 0.8538 | | 0.7648 | 13800 | 0.6644 | 0.8257 | 0.8529 | | 0.7703 | 13900 | 0.8948 | 0.7271 | 0.8536 | | 0.7758 | 14000 | 0.6886 | 0.7607 | 0.8531 | | 0.7814 | 14100 | 0.8322 | 0.7143 | 0.8540 | | 0.7869 | 14200 | 0.6965 | 0.7270 | 0.8540 | | 0.7925 | 14300 | 0.6478 | 0.7368 | 0.8541 | | 0.7980 | 14400 | 0.6877 | 0.7690 | 0.8532 | | 0.8035 | 14500 | 0.6289 | 0.7316 | 0.8538 | | 0.8091 | 14600 | 0.9058 | 0.6514 | 0.8548 | | 0.8146 | 14700 | 0.5971 | 0.6980 | 0.8542 | | 0.8202 | 14800 | 0.5774 | 0.7124 | 0.8539 | | 0.8257 | 14900 | 0.6134 | 0.7480 | 0.8534 | | 0.8313 | 15000 | 0.6962 | 0.6284 | 0.8551 | | 0.8368 | 15100 | 0.5934 | 0.7099 | 0.8540 | | 0.8423 | 15200 | 0.7791 | 0.6925 | 0.8542 | | 0.8479 | 15300 | 0.5418 | 0.6774 | 0.8544 | | 0.8534 | 15400 | 0.7526 | 0.6380 | 0.8552 | | 0.8590 | 15500 | 0.694 | 0.6967 | 0.8543 | | 0.8645 | 15600 | 0.5813 | 0.6864 | 0.8543 | | 0.8700 | 15700 | 0.726 | 0.6325 | 0.8552 | | 0.8756 | 15800 | 0.5094 | 0.6491 | 0.8549 | | 0.8811 | 15900 | 0.5728 | 0.6549 | 0.8549 | | 0.8867 | 16000 | 0.5272 | 0.6723 | 0.8548 | | 0.8922 | 16100 | 0.6896 | 0.6786 | 0.8546 | | 0.8978 | 16200 | 0.5666 | 0.6629 | 0.8550 | | 0.9033 | 16300 | 0.7312 | 0.6801 | 0.8549 | | 0.9088 | 16400 | 0.6451 | 0.6779 | 0.8549 | | 0.9144 | 16500 | 0.6572 | 0.6374 | 0.8556 | | 0.9199 | 16600 | 0.5052 | 0.6672 | 0.8551 | | 0.9255 | 16700 | 0.5395 | 0.6686 | 0.8550 | | 0.9310 | 16800 | 0.4715 | 0.6840 | 0.8547 | | 0.9365 | 16900 | 0.7149 | 0.6576 | 0.8552 | | 0.9421 | 17000 | 0.5066 | 0.6533 | 0.8553 | | 0.9476 | 17100 | 0.6382 | 0.6509 | 0.8552 | | 0.9532 | 17200 | 0.5585 | 0.6729 | 0.8550 | | 0.9587 | 17300 | 0.5953 | 0.6505 | 0.8554 | | 0.9643 | 17400 | 0.3545 | 0.6487 | 0.8555 | | 0.9698 | 17500 | 0.8031 | 0.6451 | 0.8555 | | 0.9753 | 17600 | 0.8531 | 0.6366 | 0.8557 | | 0.9809 | 17700 | 0.7154 | 0.6365 | 0.8557 | | 0.9864 | 17800 | 0.3339 | 0.6339 | 0.8557 | | 0.9920 | 17900 | 0.5858 | 0.6410 | 0.8556 | | 0.9975 | 18000 | 0.7509 | 0.6400 | 0.8556 |
### Framework Versions - Python: 3.11.1 - Sentence Transformers: 3.3.1 - Transformers: 4.47.0 - PyTorch: 2.1.1+cu121 - Accelerate: 1.2.0 - Datasets: 2.18.0 - 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", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```