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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:132037
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: back pain. I'm not sure what to make of it.
sentences:
- 'Observed: back pain — musculoskeletal system'
- Patient is frustrated with the medical system
- 'OPQRST — Severity: fever rated 7/10 by patient'
- source_sentence: I'm absolutely terrified. I've had eye redness for a few days and
I keep imagining the worst.
sentences:
- 'Observed: palpitations and irregular heart rate — cardiovascular — physician
confirmed: Atrial fibrillation'
- 'Patient presented: blood in urine — neurological/cognitive system'
- 'Chief complaint: patient expressed concern, reason not yet specified — calm presentation'
- source_sentence: I take a pill called clopidogrel — not sure of the dose
sentences:
- Patient is drug-free by choice — lifestyle management only
- 'Observed: fatigue and shortness of breath — haematological — physician confirmed:
Anaemia'
- 'Current medication: clopidogrel 75mg daily — antiplatelet — for post-MI'
- source_sentence: I'm on gabapentin for my neuropathic pain
sentences:
- Patient takes no regular medications
- 'Current medication: gabapentin 300mg three times daily — anticonvulsant — for
neuropathic pain'
- 'Observed: palpitations — cardiovascular system'
- source_sentence: my breathing is fast and I feel disoriented
sentences:
- Patient is anxious and hyperventilating due to health anxiety
- 'OPQRST — Onset: abdominal pain started suddenly without warning'
- 'Red flag: tachypnoea + confusion — qSOFA >= 2 — possible sepsis — IMMEDIATE ESCALATION'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
results:
- task:
type: triplet
name: Triplet
dataset:
name: rapha val
type: rapha-val
metrics:
- type: cosine_accuracy
value: 1.0
name: Cosine Accuracy
---
# SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
(1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'cls', '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 = [
'my breathing is fast and I feel disoriented',
'Red flag: tachypnoea + confusion — qSOFA >= 2 — possible sepsis — IMMEDIATE ESCALATION',
'Patient is anxious and hyperventilating due to health anxiety',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8755, 0.1384],
# [0.8755, 1.0000, 0.0772],
# [0.1384, 0.0772, 1.0000]])
```
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## Evaluation
### Metrics
#### Triplet
* Dataset: `rapha-val`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:--------|
| **cosine_accuracy** | **1.0** |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 132,037 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 100 samples:
| | anchor | positive | negative |
|:---------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| modality | text | text | text |
| details | <ul><li>min: 5 tokens</li><li>mean: 16.2 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.9 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.62 tokens</li><li>max: 19 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------|
| <code>I'm trying to stay calm but I have tremors and I genuinely feel like something is very wrong.</code> | <code>Observed: tremors — neurological system</code> | <code>Patient is defensive, resistant to clinical interview</code> |
| <code>neck pain so bad I can't look down and light sensitivity too</code> | <code>Red flag: neck stiffness + photophobia + fever — possible meningitis — IMMEDIATE ESCALATION</code> | <code>Patient reports muscle tension in neck from bad posture</code> |
| <code>neck pain so bad I can't look down and light sensitivity too</code> | <code>Red flag: neck stiffness + photophobia + fever — possible meningitis — IMMEDIATE ESCALATION</code> | <code>Patient reports muscle tension in neck from bad posture</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",
"gather_across_devices": false,
"directions": [
"query_to_doc"
],
"partition_mode": "joint",
"hardness_mode": null,
"hardness_strength": 0.0
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 15,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 100 samples:
| | anchor | positive | negative |
|:---------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| modality | text | text | text |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.39 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 18.62 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.74 tokens</li><li>max: 23 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
| <code>I'm on prednisolone 5mg daily</code> | <code>Current medication: prednisolone 5mg daily — corticosteroid — for inflammatory condition</code> | <code>Patient is not currently prescribed anything</code> |
| <code>Diagnosis recorded: Community-acquired pneumonia — ICD-10 code J18.9</code> | <code>Observed: cough, fever, shortness of breath — respiratory/infectious — physician confirmed: Community-acquired pneumonia</code> | <code>Observed: cough — upper respiratory tract infection — self-limiting, no antibiotics</code> |
| <code>breast lump.</code> | <code>Session start: patient presented with unspecified concern — Minimal communication style</code> | <code>Patient presented: numbness in limbs — respiratory system</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",
"gather_across_devices": false,
"directions": [
"query_to_doc"
],
"partition_mode": "joint",
"hardness_mode": null,
"hardness_strength": 0.0
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 256
- `learning_rate`: 2e-05
- `lr_scheduler_type`: cosine
- `warmup_steps`: 0.1
- `bf16`: True
- `per_device_eval_batch_size`: 256
- `load_best_model_at_end`: True
- `dataloader_drop_last`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `per_device_train_batch_size`: 256
- `num_train_epochs`: 3
- `max_steps`: -1
- `learning_rate`: 2e-05
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.1
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 1
- `average_tokens_across_devices`: True
- `max_grad_norm`: 1.0
- `label_smoothing_factor`: 0.0
- `bf16`: True
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 256
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_static_graph`: None
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: None
- `fsdp_config`: None
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | rapha-val_cosine_accuracy |
|:----------:|:-------:|:-------------:|:---------------:|:-------------------------:|
| 0.0971 | 50 | 3.5863 | - | - |
| 0.1942 | 100 | 2.3320 | - | - |
| 0.2913 | 150 | 1.7194 | - | - |
| 0.3883 | 200 | 1.5596 | - | - |
| 0.4854 | 250 | 1.4893 | - | - |
| 0.5825 | 300 | 1.4520 | - | - |
| 0.6796 | 350 | 1.4428 | - | - |
| 0.7767 | 400 | 1.4143 | - | - |
| 0.8738 | 450 | 1.4227 | - | - |
| **0.9709** | **500** | **1.3991** | **1.4893** | **1.0** |
| 1.0680 | 550 | 1.3811 | - | - |
| 1.1650 | 600 | 1.3818 | - | - |
| 1.2621 | 650 | 1.3876 | - | - |
| 1.3592 | 700 | 1.3830 | - | - |
| 1.4563 | 750 | 1.3690 | - | - |
| 1.5534 | 800 | 1.3927 | - | - |
| 1.6505 | 850 | 1.3683 | - | - |
| 1.7476 | 900 | 1.3772 | - | - |
| 1.8447 | 950 | 1.3812 | - | - |
| 1.9417 | 1000 | 1.4034 | 1.4836 | 1.0 |
| 2.0388 | 1050 | 1.3812 | - | - |
| 2.1359 | 1100 | 1.3562 | - | - |
| 2.2330 | 1150 | 1.3676 | - | - |
| 2.3301 | 1200 | 1.3813 | - | - |
| 2.4272 | 1250 | 1.3849 | - | - |
| 2.5243 | 1300 | 1.3729 | - | - |
| 2.6214 | 1350 | 1.3770 | - | - |
| 2.7184 | 1400 | 1.3665 | - | - |
| 2.8155 | 1450 | 1.3469 | - | - |
| 2.9126 | 1500 | 1.3790 | 1.4779 | 1.0 |
| 3.0 | 1545 | - | 1.4779 | 1.0 |
* The bold row denotes the saved checkpoint.
### Training Time
- **Training**: 6.3 minutes
### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.6.0
- Transformers: 5.12.1
- PyTorch: 2.11.0+cu128
- Accelerate: 1.14.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2
## 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{oord2019representationlearningcontrastivepredictive,
title={Representation Learning with Contrastive Predictive Coding},
author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
year={2019},
eprint={1807.03748},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/1807.03748},
}
```
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