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nikhil061307
/
contrastive-learning-bert-added-token-v5

Feature Extraction
Transformers
PyTorch
English
contrastive_clinical
image-feature-extraction
contrastive-learning
clinical-text
medical-nlp
entity-anonymization
triplet-loss
clinical-modernbert
sentence-embeddings
custom_code
Eval Results (legacy)
Model card Files Files and versions
xet
Community
2

Instructions to use nikhil061307/contrastive-learning-bert-added-token-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nikhil061307/contrastive-learning-bert-added-token-v5 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="nikhil061307/contrastive-learning-bert-added-token-v5", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("nikhil061307/contrastive-learning-bert-added-token-v5", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Adding `safetensors` variant of this model

#2 opened 9 months ago by
SFconvertbot

Adding `safetensors` variant of this model

#1 opened 10 months ago by
SFconvertbot
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