--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:61927 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/all-mpnet-base-v2 widget: - source_sentence: caqh id, CAQH ID sentences: - caqh client number - focus end date - group confidential phone no? - source_sentence: address termination reason, Address Termination Reason sentences: - address impression - address reason for closure - addr. term. date - source_sentence: caqh state, CAQH State sentences: - qcqh state - postal n - provider alt id from - source_sentence: board cert expiration date, Board Cert Expiration Date sentences: - area focus termination end date - replacement identifier source code - certification expiration - source_sentence: board cert agency code, Board Cert Agency Code sentences: - 2nd board cert - comments - mailing address 2 pipeline_tag: sentence-similarity library_name: sentence-transformers --- # term-mapper 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). 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 - **Language:** en - **License:** apache-2.0 ### 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, 'architecture': '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("sentence_transformers_model_id") # Run inference sentences = [ 'board cert agency code, Board Cert Agency Code', '2nd board cert', 'comments', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[ 1.0000, 0.6759, -0.0045], # [ 0.6759, 1.0000, 0.0552], # [-0.0045, 0.0552, 1.0000]]) ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 61,927 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| | accepting patients ind, Accepting Patients IND | primary spec accepting new patients for pcps and ob | | accepting patients ind, Accepting Patients IND | accepting new patients (all practitioner types ongoing outpatient basis) (y n) (no blanks) | | accepting patients ind, Accepting Patients IND | acc ind for pts | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 7,092 evaluation samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:------------------------------------------------------------|:-------------------------------------| | accepting patients ind, Accepting Patients IND | open close panel | | accepting patients ind, Accepting Patients IND | panel status | | accepting patients ind, Accepting Patients IND | commercial panel status | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 2e-05 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: 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`: 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`: 2e-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`: 3 - `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`: True - `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 - `hub_revision`: None - `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 - `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 - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | |:----------:|:--------:|:-------------:|:---------------:| | 0.0258 | 50 | 0.8668 | - | | 0.0517 | 100 | 0.7505 | 0.6548 | | 0.0775 | 150 | 0.6506 | - | | 0.1033 | 200 | 0.4672 | 0.4107 | | 0.1291 | 250 | 0.403 | - | | 0.1550 | 300 | 0.3284 | 0.2954 | | 0.1808 | 350 | 0.3005 | - | | 0.2066 | 400 | 0.2248 | 0.2149 | | 0.2324 | 450 | 0.219 | - | | 0.2583 | 500 | 0.1794 | 0.1685 | | 0.2841 | 550 | 0.1441 | - | | 0.3099 | 600 | 0.1522 | 0.1397 | | 0.3357 | 650 | 0.1322 | - | | 0.3616 | 700 | 0.1254 | 0.1283 | | 0.3874 | 750 | 0.1194 | - | | 0.4132 | 800 | 0.134 | 0.1140 | | 0.4390 | 850 | 0.0932 | - | | 0.4649 | 900 | 0.1025 | 0.0957 | | 0.4907 | 950 | 0.1063 | - | | 0.5165 | 1000 | 0.0956 | 0.0945 | | 0.5424 | 1050 | 0.071 | - | | 0.5682 | 1100 | 0.0727 | 0.0836 | | 0.5940 | 1150 | 0.0895 | - | | 0.6198 | 1200 | 0.0786 | 0.0750 | | 0.6457 | 1250 | 0.0923 | - | | 0.6715 | 1300 | 0.0905 | 0.0742 | | 0.6973 | 1350 | 0.0522 | - | | 0.7231 | 1400 | 0.0645 | 0.0693 | | 0.7490 | 1450 | 0.0711 | - | | 0.7748 | 1500 | 0.0655 | 0.0627 | | 0.8006 | 1550 | 0.0532 | - | | 0.8264 | 1600 | 0.0602 | 0.0615 | | 0.8523 | 1650 | 0.0674 | - | | 0.8781 | 1700 | 0.0537 | 0.0564 | | 0.9039 | 1750 | 0.0578 | - | | 0.9298 | 1800 | 0.0643 | 0.0533 | | 0.9556 | 1850 | 0.0655 | - | | 0.9814 | 1900 | 0.0562 | 0.0519 | | 1.0072 | 1950 | 0.0538 | - | | 1.0331 | 2000 | 0.043 | 0.0470 | | 1.0589 | 2050 | 0.035 | - | | 1.0847 | 2100 | 0.0412 | 0.0454 | | 1.1105 | 2150 | 0.0362 | - | | 1.1364 | 2200 | 0.0454 | 0.0449 | | 1.1622 | 2250 | 0.0438 | - | | 1.1880 | 2300 | 0.0453 | 0.0433 | | 1.2138 | 2350 | 0.0298 | - | | 1.2397 | 2400 | 0.0351 | 0.0444 | | 1.2655 | 2450 | 0.0349 | - | | 1.2913 | 2500 | 0.0391 | 0.0431 | | 1.3171 | 2550 | 0.0404 | - | | 1.3430 | 2600 | 0.0371 | 0.0423 | | 1.3688 | 2650 | 0.0382 | - | | 1.3946 | 2700 | 0.0325 | 0.0420 | | 1.4205 | 2750 | 0.0394 | - | | 1.4463 | 2800 | 0.0469 | 0.0421 | | 1.4721 | 2850 | 0.0466 | - | | 1.4979 | 2900 | 0.0374 | 0.0407 | | 1.5238 | 2950 | 0.0321 | - | | 1.5496 | 3000 | 0.022 | 0.0388 | | 1.5754 | 3050 | 0.0229 | - | | 1.6012 | 3100 | 0.0354 | 0.0367 | | 1.6271 | 3150 | 0.0275 | - | | 1.6529 | 3200 | 0.036 | 0.0358 | | 1.6787 | 3250 | 0.0349 | - | | 1.7045 | 3300 | 0.0359 | 0.0337 | | 1.7304 | 3350 | 0.0386 | - | | 1.7562 | 3400 | 0.029 | 0.0341 | | 1.7820 | 3450 | 0.0348 | - | | 1.8079 | 3500 | 0.0241 | 0.0342 | | 1.8337 | 3550 | 0.0281 | - | | 1.8595 | 3600 | 0.0239 | 0.0323 | | 1.8853 | 3650 | 0.0281 | - | | 1.9112 | 3700 | 0.0301 | 0.0323 | | 1.9370 | 3750 | 0.0186 | - | | 1.9628 | 3800 | 0.0246 | 0.0308 | | 1.9886 | 3850 | 0.0315 | - | | 2.0145 | 3900 | 0.0185 | 0.0302 | | 2.0403 | 3950 | 0.0272 | - | | 2.0661 | 4000 | 0.025 | 0.0304 | | 2.0919 | 4050 | 0.0262 | - | | 2.1178 | 4100 | 0.02 | 0.0306 | | 2.1436 | 4150 | 0.0163 | - | | 2.1694 | 4200 | 0.0301 | 0.0294 | | 2.1952 | 4250 | 0.0176 | - | | 2.2211 | 4300 | 0.0206 | 0.0297 | | 2.2469 | 4350 | 0.0121 | - | | 2.2727 | 4400 | 0.0206 | 0.0294 | | 2.2986 | 4450 | 0.018 | - | | 2.3244 | 4500 | 0.0178 | 0.0291 | | 2.3502 | 4550 | 0.0153 | - | | 2.3760 | 4600 | 0.0219 | 0.0288 | | 2.4019 | 4650 | 0.0214 | - | | 2.4277 | 4700 | 0.0212 | 0.0281 | | 2.4535 | 4750 | 0.0183 | - | | 2.4793 | 4800 | 0.0302 | 0.0280 | | 2.5052 | 4850 | 0.0158 | - | | 2.5310 | 4900 | 0.02 | 0.0274 | | 2.5568 | 4950 | 0.0171 | - | | 2.5826 | 5000 | 0.0275 | 0.0269 | | 2.6085 | 5050 | 0.0193 | - | | 2.6343 | 5100 | 0.0158 | 0.0269 | | 2.6601 | 5150 | 0.0179 | - | | 2.6860 | 5200 | 0.0214 | 0.0269 | | 2.7118 | 5250 | 0.0225 | - | | 2.7376 | 5300 | 0.0166 | 0.0264 | | 2.7634 | 5350 | 0.0243 | - | | 2.7893 | 5400 | 0.0154 | 0.0262 | | 2.8151 | 5450 | 0.0245 | - | | 2.8409 | 5500 | 0.0122 | 0.0261 | | 2.8667 | 5550 | 0.0234 | - | | **2.8926** | **5600** | **0.0217** | **0.0259** | | 2.9184 | 5650 | 0.0166 | - | | 2.9442 | 5700 | 0.0165 | 0.0258 | | 2.9700 | 5750 | 0.0126 | - | | 2.9959 | 5800 | 0.0201 | 0.0258 | * The bold row denotes the saved checkpoint.
### Framework Versions - Python: 3.10.18 - Sentence Transformers: 5.0.0 - Transformers: 4.53.3 - PyTorch: 2.7.1+cu126 - Accelerate: 1.9.0 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```