retriever_module / README.md

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	---
	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) <!-- at revision e8c3b32edf5434bc2275fc9bab85f82640a19130 -->
	- Maximum Sequence Length: 384 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	- 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]])
	```

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	</details>
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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 61,927 training samples
	* Columns: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 9 tokens</li><li>mean: 10.39 tokens</li><li>max: 11 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 6.42 tokens</li><li>max: 25 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------\|
	\| <code>accepting patients ind, Accepting Patients IND</code> \| <code>primary spec accepting new patients for pcps and ob</code> \|
	\| <code>accepting patients ind, Accepting Patients IND</code> \| <code>accepting new patients (all practitioner types ongoing outpatient basis) (y n) (no blanks)</code> \|
	\| <code>accepting patients ind, Accepting Patients IND</code> \| <code>acc ind for pts</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](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: <code>anchor</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 11.39 tokens</li><li>max: 19 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 6.96 tokens</li><li>max: 23 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \|
	\|:------------------------------------------------------------\|:-------------------------------------\|
	\| <code>accepting patients ind, Accepting Patients IND</code> \| <code>open close panel</code> \|
	\| <code>accepting patients ind, Accepting Patients IND</code> \| <code>panel status</code> \|
	\| <code>accepting patients ind, Accepting Patients IND</code> \| <code>commercial panel status</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](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
	<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`: 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`: {}

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| 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.
	</details>

	### 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}
	}
	```

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