Modelpublic

Stevenf232/context_fine-tuned-SapBERT1

sourceHugging Faceupdated 7mo agoView on Hugging Face

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Model Card

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

This is a sentence-transformers model finetuned from cambridgeltl/SapBERT-from-PubMedBERT-fulltext. 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
—Maximum Sequence Length: 512 tokens
—Output Dimensionality: 768 dimensions
—Similarity Function: Cosine Similarity

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:

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/context_fine-tuned-SapBERT1")
# Run inference
sentences = [
    'Death [SEP] Death from chemotherapy in gestational trophoblastic disease.',
    'Neurotoxicity Syndromes [SEP] Neurologic disorders caused by exposure to toxic substances through ingestion, injection, cutaneous application, or other method',
    'Coma [SEP] A profound state of unconsciousness associated with depressed cerebral activity from which the individual cannot be aroused. Com',
]
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, 0.5542, 0.6546],
#         [0.5542, 1.0000, 0.4659],
#         [0.6546, 0.4659, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 27,120 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
—Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | label | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 9 tokens</li><li>mean: 29.3 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 24.34 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.19</li><li>max: 1.0</li></ul> |
—Samples: | sentence0 | sentence1 | label | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | <code>prolactinomas [SEP] l prolactin greater than 20 ng./ml. in 1.86% of 1,821 patients, prolactinomas in 7, 0.38%). Bromocriptine was definitely effective in cases w</code> | <code>Nicotine [SEP] Nicotine is highly toxic alkaloid. It is the prototypical agonist at nicotinic cholinergic receptors where it dramatically stimu</code> | <code>0.0</code> | | <code>acetazolamide [SEP] reatment for periodic paralysis and myotonia. Three patients on acetazolamide (15%) developed renal calculi. Extracorporeal lithotripsy succe</code> | <code>Neutropenia [SEP] A decrease in the number of NEUTROPHILS found in the blood.<br> </code> | <code>0.0</code> | | <code>methylergonovine [SEP] Effect of direct intracoronary administration of methylergonovine in patients with and without variant angina.</code> | <code>Methylergonovine [SEP] A homolog of ERGONOVINE containing one more CH2 group. (Merck Index, 11th ed)<br> </code> | <code>1.0</code> |
—Loss: <code>ContrastiveLoss</code> with these parameters:

json

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

Training Hyperparameters

Non-Default Hyperparameters

—per_device_train_batch_size: 16
—per_device_eval_batch_size: 16
—fp16: True
—multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

—do_predict: False
—eval_strategy: no
—prediction_loss_only: True
—per_device_train_batch_size: 16
—per_device_eval_batch_size: 16
—gradient_accumulation_steps: 1
—eval_accumulation_steps: None
—torch_empty_cache_steps: None
—learning_rate: 5e-05
—weight_decay: 0.0
—adam_beta1: 0.9
—adam_beta2: 0.999
—adam_epsilon: 1e-08
—max_grad_norm: 1
—num_train_epochs: 3
—max_steps: -1
—lr_scheduler_type: linear
—lr_scheduler_kwargs: None
—warmup_ratio: None
—warmup_steps: 0
—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: round_robin
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Logs

Epoch	Step	Training Loss
0.2950	500	0.0105
0.5900	1000	0.0066
0.8850	1500	0.0054
1.1799	2000	0.0043
1.4749	2500	0.0036
1.7699	3000	0.0034
2.0649	3500	0.0032
2.3599	4000	0.0024
2.6549	4500	0.0025
2.9499	5000	0.0024

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}
}

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