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

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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]])

<!-- ### 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 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

bc5_cdr_me_sh2015_complete

• 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: {'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

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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