Stevenf232/SapBERT_ContrastiveLoss_BC5CDR_Context

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

This is a sentence-transformers model finetuned from cambridgeltl/SapBERT-from-PubMedBERT-fulltext on the bc5_cdr_me_sh2015_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 <!-- at revision 090663c3ae57bf35ffe4d0d468a2a88d03051a4d -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity
• —Training Dataset:
• —bc5_cdr_me_sh2015_complete <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]])

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Training Details

Training Dataset

bc5cdrmesh2015complete

• —Dataset: bc5_cdr_me_sh2015_complete at f40f655
• —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> with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }

Evaluation Dataset

bc5cdrmesh2015complete

• —Dataset: bc5_cdr_me_sh2015_complete at f40f655
• —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> with these parameters:

  {
      "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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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

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

@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

@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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