| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:5424 |
| - loss:ContrastiveLoss |
| 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 --> |
| <!-- - **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/SapBERT_ContrastiveLoss_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, 0.9985, 0.9974], |
| # [0.9985, 1.0000, 0.9981], |
| # [0.9974, 0.9981, 1.0000]]) |
| ``` |
|
|
| <!-- |
| ### 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 |
|
|
| #### 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>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 |
| } |
| ``` |
|
|
| ### 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>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 |
|
|
| - `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 | 0.1273 | 0.0616 | |
| | 0.2353 | 20 | 0.0290 | 0.0021 | |
| | 0.3529 | 30 | 0.0024 | 0.0001 | |
| | 0.4706 | 40 | 0.0010 | 0.0000 | |
| | 0.5882 | 50 | 0.0009 | 0.0000 | |
| | 0.7059 | 60 | 0.0008 | 0.0000 | |
| | 0.8235 | 70 | 0.0007 | 0.0000 | |
| | 0.9412 | 80 | 0.0007 | 0.0000 | |
| | 1.0588 | 90 | 0.0007 | 0.0000 | |
| | 1.1765 | 100 | 0.0006 | 0.0000 | |
| | 1.2941 | 110 | 0.0006 | 0.0000 | |
| | 1.4118 | 120 | 0.0006 | 0.0000 | |
| | 1.5294 | 130 | 0.0005 | 0.0000 | |
| | 1.6471 | 140 | 0.0006 | 0.0000 | |
| | 1.7647 | 150 | 0.0005 | 0.0000 | |
| | 1.8824 | 160 | 0.0005 | 0.0000 | |
| | 2.0 | 170 | 0.0005 | 0.0000 | |
| | 2.1176 | 180 | 0.0005 | 0.0000 | |
| | 2.2353 | 190 | 0.0005 | 0.0000 | |
| | 2.3529 | 200 | 0.0005 | 0.0000 | |
|
|
|
|
| ### 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", |
| } |
| ``` |
|
|
| #### 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} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
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| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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