reranker-sapbert-ncbi-disease-bce-context

Cambridge-SapBERT-from-PubMedBERT-fulltext trained on NCBI Disease

This is a Cross Encoder model finetuned from cambridgeltl/SapBERT-from-PubMedBERT-fulltext using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

• Model Type: Cross Encoder
• Base model: cambridgeltl/SapBERT-from-PubMedBERT-fulltext <!-- at revision 090663c3ae57bf35ffe4d0d468a2a88d03051a4d -->
• Maximum Sequence Length: 512 tokens
• Number of Output Labels: 1 label

<!-- - **Training Dataset:** Unknown -->

• Language: en

<!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
    ['Original mention: L-N.\nContext: Partial HPRT deficiencies are associated with gouty arthritis, while absence of activity results in Lesch-Nyhan syndrome (L-N).', 'guanine phosphoribosyltransferase deficiencies'],
    ['Original mention: breast cancer.\nContext: Most multiple case families of young onset breast cancer and ovarian cancer are thought to be due to highly penetrant mutations in the predisposing genes BRCA1 and BRCA2.', 'malignancies'],
    ['Original mention: AS.\nContext: We report here the characterization of a transgene insertion (Epstein-Barr virus Latent Membrane Protein 2A, LMP2A) into mouse chromosome 7C, which has resulted in mouse models for PWS and AS dependent on the sex of the transmitting parent.', 'achondroplastic dwarfism'],
    ['Original mention: adenomatous polyposis coli.\nContext: Epidemiologic studies have shown an increased frequency of this tumor type in families affected by adenomatous polyposis coli.', 'apc (adenomatous polyposis coli)'],
    ['Original mention: autosomal recessive disorder.\nContext: Mucopolysaccharidosis IVA (MPS IVA) is an autosomal recessive disorder caused by a deficiency in N-acetylgalactosamine-6-sulfatase (GALNS).', 'susceptibility, disease'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Original mention: L-N.\nContext: Partial HPRT deficiencies are associated with gouty arthritis, while absence of activity results in Lesch-Nyhan syndrome (L-N).',
    [
        'guanine phosphoribosyltransferase deficiencies',
        'malignancies',
        'achondroplastic dwarfism',
        'apc (adenomatous polyposis coli)',
        'susceptibility, disease',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

<!-- ### 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.* -->

Evaluation

Metrics

Cross Encoder Reranking

• Dataset: ncbi-disease-dev
• Evaluated with <code>CrossEncoderRerankingEvaluator</code> with these parameters:

  {
      "at_k": 10,
      "always_rerank_positives": false
  }
  

<!-- ## 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

Unnamed Dataset

• Size: 222,348 training samples
• Columns: <code>query</code>, <code>answer</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	query	answer	label
  type	string	string	int
  details	<ul><li>min: 64 characters</li><li>mean: 207.06 characters</li><li>max: 558 characters</li></ul>	<ul><li>min: 5 characters</li><li>mean: 27.99 characters</li><li>max: 107 characters</li></ul>	<ul><li>0: ~46.10%</li><li>1: ~53.90%</li></ul>

• Samples:

  query	answer	label
  <code>Original mention: L-N.<br>Context: Partial HPRT deficiencies are associated with gouty arthritis, while absence of activity results in Lesch-Nyhan syndrome (L-N).</code>	<code>guanine phosphoribosyltransferase deficiencies</code>	<code>1</code>
  <code>Original mention: breast cancer.<br>Context: Most multiple case families of young onset breast cancer and ovarian cancer are thought to be due to highly penetrant mutations in the predisposing genes BRCA1 and BRCA2.</code>	<code>malignancies</code>	<code>0</code>
  <code>Original mention: AS.<br>Context: We report here the characterization of a transgene insertion (Epstein-Barr virus Latent Membrane Protein 2A, LMP2A) into mouse chromosome 7C, which has resulted in mouse models for PWS and AS dependent on the sex of the transmitting parent.</code>	<code>achondroplastic dwarfism</code>	<code>0</code>

• Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": 0.7792357206344604
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• learning_rate: 2e-05
• warmup_ratio: 0.05
• seed: 12
• bf16: True
• dataloader_num_workers: 4
• load_best_model_at_end: True

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• 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: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.05
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 12
• data_seed: None
• jit_mode_eval: False
• bf16: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 4
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• 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
• adafactor: False
• 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
• use_legacy_prediction_loop: False
• 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_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• 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
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.2.0
• Transformers: 4.57.3
• PyTorch: 2.9.1+cu130
• Accelerate: 1.12.0
• Datasets: 4.4.2
• Tokenizers: 0.22.1

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",
}

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