| --- |
| 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} |
| } |
| ``` |
|
|
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| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
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