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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:267
+- loss:ContrastiveLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: 'hypertension
+
+    The patient''s primary diagnosis is hypertension, as stated in the visit note.
+
+    BP medications
+
+    The patient is on BP medications which are used to treat hypertension.
+
+    BP management
+
+    The visit note mentions follow-up on BP management, indicating ongoing treatment
+    for hypertension.
+
+    HTN
+
+    HTN is the abbreviation for hypertension, which is the patient''s diagnosed condition.
+
+    BP was measured at 138/90
+
+    This blood pressure reading supports the diagnosis of hypertension as it is elevated.
+
+    monthly bp at home have been around that number or higher
+
+    Consistently high blood pressure readings confirm the presence of hypertension.
+
+    most likely diagnosis for this patient is hypertension
+
+    The visit note explicitly states that hypertension is the most likely diagnosis.'
+  sentences:
+  - Anemia, Unspecified
+  - Essential (Primary) Hypertension
+  - Dehydration
+- source_sentence: 'BMI ABOVE NORMAL PARAM F/U DOCUMENTED
+
+    This phrase indicates that the patient''s BMI is above normal parameters and requires
+    follow-up, which is a key indicator for obesity classification.
+
+    34.11
+
+    The specific BMI value of 34.11 falls within the range for Class 1 obesity (30.0-34.9),
+    providing numerical confirmation of the diagnosis.
+
+    Class 1 obesity
+
+    This is the explicit statement of the patient''s condition, directly aligning
+    with the ICD code E66.811 for Class 1 obesity.'
+  sentences:
+  - Obesity, Class 1
+  - Hypothyroidism, Unspecified
+  - Overweight
+- source_sentence: 'anxious and uses food for comfort
+
+    This phrase indicates the presence of anxiety symptoms, specifically using food
+    as a coping mechanism, which aligns with an unspecified anxiety disorder.'
+  sentences:
+  - Essential (Primary) Hypertension
+  - Essential (Primary) Hypertension
+  - Anxiety Disorder, Unspecified
+- source_sentence: 'compression stockings
+
+    Compression stockings are a treatment for venous insufficiency, which can cause
+    localized edema.
+
+    venous insufficiency
+
+    Venous insufficiency is a condition that leads to leg edema, which is a type of
+    localized edema.
+
+    Leg edema
+
+    Leg edema is a direct symptom of localized edema.
+
+    edema
+
+    Edema refers to swelling caused by fluid retention, which aligns with the ICD
+    code R60.0 for Localized Edema.'
+  sentences:
+  - Nasal Congestion
+  - Localized Edema
+  - Essential (Primary) Hypertension
+- source_sentence: 'Had lithotripsy and passed an 8x5 mm stone on L.
+
+    This phrase indicates a history of urinary calculi as evidenced by the treatment
+    (lithotripsy) for kidney stones.'
+  sentences:
+  - Pure Hypercholesterolemia, Unspecified
+  - Personal History Of Urinary Calculi
+  - Menopausal And Female Climacteric States
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, '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 = [
+    'Had lithotripsy and passed an 8x5 mm stone on L.\nThis phrase indicates a history of urinary calculi as evidenced by the treatment (lithotripsy) for kidney stones.',
+    'Personal History Of Urinary Calculi',
+    'Pure Hypercholesterolemia, Unspecified',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# 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.*
+-->
+
+<!--
+## 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
+
+#### Unnamed Dataset
+
+* Size: 267 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>label</code>
+* Approximate statistics based on the first 267 samples:
+  |         | anchor                                                                              | positive                                                                         | label                                                         |
+  |:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                           | float                                                         |
+  | details | <ul><li>min: 12 tokens</li><li>mean: 94.12 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.77 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | positive                                                    | label            |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:-----------------|
+  | <code>T2DM<br>Directly indicates the diagnosis of Type 2 Diabetes Mellitus without complications as stated in the Problem/Dx section.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                    | <code>Type 2 Diabetes Mellitus Without Complications</code> | <code>1.0</code> |
+  | <code>Atorvastatin<br>Atorvastatin is a statin medication prescribed to lower cholesterol levels, directly addressing hypercholesterolemia.<br>Hyperlipidemia<br>Hyperlipidemia is a broader term that includes high cholesterol (hypercholesterolemia), which is explicitly mentioned in the assessment.<br>statin therapy<br>Statin therapy, including Atorvastatin, is specifically noted as part of the treatment plan for managing high cholesterol.<br>Hypercholesterolemia<br>Explicitly listed under assessment as a condition being managed, aligning with the ICD code E78.00.</code> | <code>Pure Hypercholesterolemia, Unspecified</code>         | <code>1.0</code> |
+  | <code>Encounter for immunization (Z23)<br>This phrase directly indicates the ICD code Z23 and its description as the reason for the encounter.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>Encounter For Immunization</code>                     | <code>1.0</code> |
+* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
+  ```json
+  {
+      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
+      "margin": 0.5,
+      "size_average": true
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 1
+- `warmup_ratio`: 0.1
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `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`: 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`: 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`: False
+- `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}
+- `tp_size`: 0
+- `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
+- `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
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0588 | 1    | 0.1007        |
+| 0.1176 | 2    | 0.1131        |
+| 0.1765 | 3    | 0.099         |
+| 0.2353 | 4    | 0.0867        |
+| 0.2941 | 5    | 0.0682        |
+| 0.3529 | 6    | 0.1019        |
+| 0.4118 | 7    | 0.0618        |
+| 0.4706 | 8    | 0.0623        |
+| 0.5294 | 9    | 0.0564        |
+| 0.5882 | 10   | 0.0521        |
+| 0.6471 | 11   | 0.0545        |
+| 0.7059 | 12   | 0.0335        |
+| 0.7647 | 13   | 0.0593        |
+| 0.8235 | 14   | 0.0381        |
+| 0.8824 | 15   | 0.0308        |
+| 0.9412 | 16   | 0.0487        |
+| 1.0    | 17   | 0.0398        |
+
+
+### Framework Versions
+- Python: 3.11.12
+- Sentence Transformers: 3.4.1
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## 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",
+}
+```
+
+#### ContrastiveLoss
+```bibtex
+@inproceedings{hadsell2006dimensionality,
+    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
+    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
+    title={Dimensionality Reduction by Learning an Invariant Mapping},
+    year={2006},
+    volume={2},
+    number={},
+    pages={1735-1742},
+    doi={10.1109/CVPR.2006.100}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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modules.json

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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
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+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

optimizer.pt

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rng_state.pth

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sentence_bert_config.json

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+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
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+    "lstrip": false,
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+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "do_lower_case": true,
+  "extra_special_tokens": {},
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+  "model_max_length": 256,
+  "never_split": null,
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+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

trainer_state.json

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+  "best_metric": null,
+  "best_model_checkpoint": null,
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