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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:5424
- loss:MultipleNegativesRankingLoss
base_model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext
widget:
- source_sentence: liver injury [SEP] d up all transplant-free survivors of paracetamol-induced
acute liver injury, hospitalized in a Danish national referral centre during 1984-
sentences:
- 'Drug-Induced Liver Injury [SEP] A spectrum of clinical liver diseases ranging
from mild biochemical abnormalities to ACUTE LIVER FAILURE, caused by drugs, drug '
- "Venous Thrombosis [SEP] The formation or presence of a blood clot (THROMBUS)\
\ within a vein.\n "
- "Isoflurophate [SEP] A di-isopropyl-fluorophosphate which is an irreversible cholinesterase\
\ inhibitor used to investigate the NERVOUS SYSTEM.\n "
- source_sentence: renal impairment [SEP] 6, 95% CI 1.57-2.44) in patients with diabetes.
A lower risk of renal impairment was seen in both groups with beta-blocker therapy
(RR 0.70, 95%
sentences:
- Acetylcholine [SEP] A neurotransmitter found at neuromuscular junctions, autonomic
ganglia, parasympathetic effector junctions, a subset of sympathe
- Pilocarpine [SEP] A slowly hydrolyzed muscarinic agonist with no nicotinic effects.
Pilocarpine is used as a miotic and in the treatment of glauco
- 'Renal Insufficiency [SEP] Conditions in which the KIDNEYS perform below the normal
level in the ability to remove wastes, concentrate URINE, and maintain '
- source_sentence: 'grand mal seizures [SEP] MMARY: A 46-year-old African-American
man experienced recurrent grand mal seizures during intravenous infusion of amphotericin
B, then petit mal s'
sentences:
- 'Lithium Carbonate [SEP] A lithium salt, classified as a mood-stabilizing agent.
Lithium ion alters the metabolism of BIOGENIC MONOAMINES in the CENTRAL '
- Epilepsy, Tonic-Clonic [SEP] A generalized seizure disorder characterized by recurrent
major motor seizures. The initial brief tonic phase is marked by trunk
- Neurotoxicity Syndromes [SEP] Neurologic disorders caused by exposure to toxic
substances through ingestion, injection, cutaneous application, or other method
- source_sentence: 'seizure [SEP] OBJECTIVE: To report a case of multiple episodes
of seizure activity in an AIDS patent following amphotericin B infusion. C'
sentences:
- Catalepsy [SEP] A condition characterized by inactivity, decreased responsiveness
to stimuli, and a tendency to maintain an immobile posture. Th
- Seizures [SEP] Clinical or subclinical disturbances of cortical function due to
a sudden, abnormal, excessive, and disorganized discharge of br
- 'ammonium acetate [SEP] '
- source_sentence: insomnia [SEP] pressive symptoms was admitted to a psychiatric
hospital due to insomnia, loss of appetite, exhaustion, and agitation. Medical
treatment
sentences:
- Atrioventricular Block [SEP] Impaired impulse conduction from HEART ATRIA to HEART
VENTRICLES. AV block can mean delayed or completely blocked impulse conduc
- "Sodium [SEP] A member of the alkali group of metals. It has the atomic symbol\
\ Na, atomic number 11, and atomic weight 23.\n "
- Sleep Initiation and Maintenance Disorders [SEP] Disorders characterized by impairment
of the ability to initiate or maintain sleep. This may occur as a primary disorder
or in a
datasets:
- Stevenf232/BC5CDR_MeSH2015_complete
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) on the [bc5_cdr_me_sh2015_complete](https://huggingface.co/datasets/Stevenf232/BC5CDR_MeSH2015_complete) dataset. 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:**
- [bc5_cdr_me_sh2015_complete](https://huggingface.co/datasets/Stevenf232/BC5CDR_MeSH2015_complete)
<!-- - **Language:** Unknown -->
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### 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/SapBERT_MultipleNegativesRankingLoss_BC5CDR_Context")
# Run inference
sentences = [
'insomnia [SEP] pressive symptoms was admitted to a psychiatric hospital due to insomnia, loss of appetite, exhaustion, and agitation. Medical treatment',
'Sleep Initiation and Maintenance Disorders [SEP] Disorders characterized by impairment of the ability to initiate or maintain sleep. This may occur as a primary disorder or in a',
'Atrioventricular Block [SEP] Impaired impulse conduction from HEART ATRIA to HEART VENTRICLES. AV block can mean delayed or completely blocked impulse conduc',
]
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, 0.8093, 0.1453],
# [0.8093, 1.0000, 0.1948],
# [0.1453, 0.1948, 1.0000]])
```
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## Training Details
### Training Dataset
#### bc5_cdr_me_sh2015_complete
* Dataset: [bc5_cdr_me_sh2015_complete](https://huggingface.co/datasets/Stevenf232/BC5CDR_MeSH2015_complete) at [f40f655](https://huggingface.co/datasets/Stevenf232/BC5CDR_MeSH2015_complete/tree/f40f655ae0d844cb1bd1db8b25819616af991cb0)
* Size: 5,424 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | <ul><li>min: 9 tokens</li><li>mean: 29.07 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 25.04 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Naloxone [SEP] Naloxone reverses the antihypertensive effect of clonidine.</code> | <code>Naloxone [SEP] A specific opiate antagonist that has no agonist activity. It is a competitive antagonist at mu, delta, and kappa opioid recepto</code> | <code>1</code> |
| <code>clonidine [SEP] Naloxone reverses the antihypertensive effect of clonidine.</code> | <code>Clonidine [SEP] An imidazoline sympatholytic agent that stimulates ALPHA-2 ADRENERGIC RECEPTORS and central IMIDAZOLINE RECEPTORS. It is commonl</code> | <code>1</code> |
| <code>hypertensive [SEP] In unanesthetized, spontaneously hypertensive rats the decrease in blood pressure and heart rate produced by </code> | <code>Hypertension [SEP] Persistently high systemic arterial BLOOD PRESSURE. Based on multiple readings (BLOOD PRESSURE DETERMINATION), hypertension is c</code> | <code>1</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
}
```
### Evaluation Dataset
#### bc5_cdr_me_sh2015_complete
* Dataset: [bc5_cdr_me_sh2015_complete](https://huggingface.co/datasets/Stevenf232/BC5CDR_MeSH2015_complete) at [f40f655](https://huggingface.co/datasets/Stevenf232/BC5CDR_MeSH2015_complete/tree/f40f655ae0d844cb1bd1db8b25819616af991cb0)
* Size: 5,445 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | <ul><li>min: 11 tokens</li><li>mean: 30.69 tokens</li><li>max: 166 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.66 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Tricuspid valve regurgitation [SEP] Tricuspid valve regurgitation and lithium carbonate toxicity in a newborn infant.</code> | <code>Tricuspid Valve Insufficiency [SEP] Backflow of blood from the RIGHT VENTRICLE into the RIGHT ATRIUM due to imperfect closure of the TRICUSPID VALVE.<br> </code> | <code>1</code> |
| <code>lithium carbonate [SEP] Tricuspid valve regurgitation and lithium carbonate toxicity in a newborn infant.</code> | <code>Lithium Carbonate [SEP] A lithium salt, classified as a mood-stabilizing agent. Lithium ion alters the metabolism of BIOGENIC MONOAMINES in the CENTRAL </code> | <code>1</code> |
| <code>toxicity [SEP] Tricuspid valve regurgitation and lithium carbonate toxicity in a newborn infant.</code> | <code>Drug-Related Side Effects and Adverse Reactions [SEP] Disorders that result from the intended use of PHARMACEUTICAL PREPARATIONS. Included in this heading are a broad variety of chem</code> | <code>1</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
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `max_steps`: 200
- `warmup_ratio`: 0.1
- `warmup_steps`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `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`: 3
- `max_steps`: 200
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: 0.1
- `warmup_steps`: 0.1
- `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`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.1176 | 10 | 2.6695 | 2.4324 |
| 0.2353 | 20 | 2.2030 | 1.8628 |
| 0.3529 | 30 | 1.6394 | 1.5455 |
| 0.4706 | 40 | 1.5937 | 1.3570 |
| 0.5882 | 50 | 1.3294 | 1.2489 |
| 0.7059 | 60 | 1.2576 | 1.1594 |
| 0.8235 | 70 | 1.0213 | 1.1042 |
| 0.9412 | 80 | 1.0295 | 1.0672 |
| 1.0588 | 90 | 0.8890 | 1.0293 |
| 1.1765 | 100 | 0.9259 | 1.0030 |
| 1.2941 | 110 | 0.8096 | 0.9743 |
| 1.4118 | 120 | 0.7438 | 0.9587 |
| 1.5294 | 130 | 0.7797 | 0.9442 |
| 1.6471 | 140 | 0.7999 | 0.9265 |
| 1.7647 | 150 | 0.7323 | 0.9142 |
| 1.8824 | 160 | 0.7510 | 0.9070 |
| 2.0 | 170 | 0.7297 | 0.9032 |
| 2.1176 | 180 | 0.6434 | 0.8985 |
| 2.2353 | 190 | 0.5984 | 0.8967 |
| 2.3529 | 200 | 0.6603 | 0.8959 |
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.3
- Transformers: 5.0.0
- PyTorch: 2.10.0+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",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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