---
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)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
### 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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 267 training samples
* Columns: anchor, positive, and label
* Approximate statistics based on the first 267 samples:
| | anchor | positive | label |
|:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | - min: 12 tokens
- mean: 94.12 tokens
- max: 256 tokens
| - min: 3 tokens
- mean: 9.77 tokens
- max: 23 tokens
| - min: 1.0
- mean: 1.0
- max: 1.0
|
* Samples:
| anchor | positive | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:-----------------|
| T2DM
Directly indicates the diagnosis of Type 2 Diabetes Mellitus without complications as stated in the Problem/Dx section. | Type 2 Diabetes Mellitus Without Complications | 1.0 |
| Atorvastatin
Atorvastatin is a statin medication prescribed to lower cholesterol levels, directly addressing hypercholesterolemia.
Hyperlipidemia
Hyperlipidemia is a broader term that includes high cholesterol (hypercholesterolemia), which is explicitly mentioned in the assessment.
statin therapy
Statin therapy, including Atorvastatin, is specifically noted as part of the treatment plan for managing high cholesterol.
Hypercholesterolemia
Explicitly listed under assessment as a condition being managed, aligning with the ICD code E78.00. | Pure Hypercholesterolemia, Unspecified | 1.0 |
| Encounter for immunization (Z23)
This phrase directly indicates the ICD code Z23 and its description as the reason for the encounter. | Encounter For Immunization | 1.0 |
* Loss: [ContrastiveLoss](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
Click to expand
- `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