README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:27120
- loss:ContrastiveLoss
base_model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext
widget:
- source_sentence: 'anencephaly [SEP] Sequential observations of exencephaly and subsequent
    morphological changes by mouse exo utero development system: analysis of t'
  sentences:
  - Hemostatic Disorders [SEP] Pathological processes involving the integrity of blood
    circulation. Hemostasis depends on the integrity of BLOOD VESSELS, blood
  - Pentylenetetrazole [SEP] A pharmaceutical agent that displays activity as a central
    nervous system and respiratory stimulant. It is considered a non-comp
  - Epilepsy [SEP] A disorder characterized by recurrent episodes of paroxysmal brain
    dysfunction due to a sudden, disorderly, and excessive neuron
- source_sentence: nifedipine [SEP] The effect of nifedipine on renal function in
    liver transplant recipients who were receiving tacrolimus was evaluated between
    Ja
  sentences:
  - 'Nifedipine [SEP] A potent vasodilator agent with calcium antagonistic action.
    It is a useful anti-anginal agent that also lowers blood pressure.

    '
  - Hypotension [SEP] Abnormally low BLOOD PRESSURE that can result in inadequate
    blood flow to the brain and other vital organs. Common symptom is DI
  - Granulomatosis with Polyangiitis [SEP] A multisystemic disease of a complex genetic
    background. It is characterized by inflammation of the blood vessels (VASCULITIS)
    l
- source_sentence: toxicity [SEP] Effects of calcium channel blockers on bupivacaine-induced
    toxicity.
  sentences:
  - 'Methamphetamine [SEP] A central nervous system stimulant and sympathomimetic
    with actions and uses similar to DEXTROAMPHETAMINE. The smokable form is '
  - Dizocilpine Maleate [SEP] A potent noncompetitive antagonist of the NMDA receptor
    (RECEPTORS, N-METHYL-D-ASPARTATE) used mainly as a research tool. The dr
  - Hallucinations [SEP] Subjectively experienced sensations in the absence of an
    appropriate stimulus, but which are regarded by the individual as real.
- source_sentence: Ca [SEP] Interactive effects of variations in [Na]o and [Ca]o on
    rat atrial spontaneous frequency.
  sentences:
  - Brain Edema [SEP] Increased intracellular or extracellular fluid in brain tissue.
    Cytotoxic brain edema (swelling due to increased intracellular f
  - "Capsaicin [SEP] An alkylamide found in CAPSICUM that acts at TRPV CATION CHANNELS.\n\
    \    "
  - "Thrombocytopenia [SEP] A subnormal level of BLOOD PLATELETS.\n    "
- source_sentence: acromegaly [SEP] This article reports the changes in gallbladder
    function examined by ultrasonography in 20 Chinese patients with active acromega
  sentences:
  - "Indocyanine Green [SEP] A tricarbocyanine dye that is used diagnostically in\
    \ liver function tests and to determine blood volume and cardiac output.\n    "
  - Nitroglycerin [SEP] A volatile vasodilator which relieves ANGINA PECTORIS by stimulating
    GUANYLATE CYCLASE and lowering cytosolic calcium. It is als
  - Nausea [SEP] An unpleasant sensation in the stomach usually accompanied by the
    urge to vomit. Common causes are early pregnancy, sea and moti
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on cambridgeltl/SapBERT-from-PubMedBERT-fulltext

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext). 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:** [cambridgeltl/SapBERT-from-PubMedBERT-fulltext](https://huggingface.co/cambridgeltl/SapBERT-from-PubMedBERT-fulltext) <!-- at revision 090663c3ae57bf35ffe4d0d468a2a88d03051a4d -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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/huggingface/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': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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("Stevenf232/context_fine-tuned-SapBERT")
# Run inference
sentences = [
    'acromegaly [SEP] This article reports the changes in gallbladder function examined by ultrasonography in 20 Chinese patients with active acromega',
    'Nitroglycerin [SEP] A volatile vasodilator which relieves ANGINA PECTORIS by stimulating GUANYLATE CYCLASE and lowering cytosolic calcium. It is als',
    'Indocyanine Green [SEP] A tricarbocyanine dye that is used diagnostically in liver function tests and to determine blood volume and cardiac output.\n    ',
]
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, 0.2218, 0.3974],
#         [0.2218, 1.0000, 0.5304],
#         [0.3974, 0.5304, 1.0000]])
```

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

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 27,120 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 9 tokens</li><li>mean: 27.48 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.74 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.21</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                                                                                                                                              | sentence_1                                                                                                                                                                | label            |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
  | <code>toxic to the central nervous system [SEP] Treatment for scabies is usually initiated by general practitioners; most consider lindane (gamma benzene hexachloride) the trea</code> | <code>Thyrotoxicosis [SEP] A hypermetabolic syndrome caused by excess THYROID HORMONES which may come from endogenous or exogenous sources. The endogenous </code>        | <code>0.0</code> |
  | <code>cancer [SEP] Doxorubicin is an effective anticancer chemotherapeutic agent known to cause acute and chronic cardiomyopathy. To develop a more</code>                              | <code>Nystagmus, Pathologic [SEP] Involuntary movements of the eye that are divided into two types, jerk and pendular. Jerk nystagmus has a slow phase in one dire</code> | <code>0.0</code> |
  | <code>doxorubicin [SEP] Doxorubicin is an effective anticancer chemotherapeutic agent known to cause acute and chronic cardiomyopathy. To develop a more</code>                         | <code>Cisplatin [SEP] An inorganic and water-soluble platinum complex. After undergoing hydrolysis, it reacts with DNA to produce both intra and inter</code>             | <code>0.0</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
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: None
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: True
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `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_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `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`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.2950 | 500  | 0.0106        |
| 0.5900 | 1000 | 0.0065        |
| 0.8850 | 1500 | 0.0058        |
| 1.1799 | 2000 | 0.0045        |
| 1.4749 | 2500 | 0.0038        |
| 1.7699 | 3000 | 0.0036        |
| 2.0649 | 3500 | 0.0036        |
| 2.3599 | 4000 | 0.0027        |
| 2.6549 | 4500 | 0.0027        |
| 2.9499 | 5000 | 0.0027        |


### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.3
- Transformers: 5.0.0
- PyTorch: 2.9.0+cu128
- Accelerate: 1.12.0
- 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",
}
```

#### 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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