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michiyasunaga
/
BioLinkBERT-base

Text Classification
Transformers
PyTorch
English
bert
feature-extraction
exbert
linkbert
biolinkbert
fill-mask
question-answering
token-classification
text-embeddings-inference
Model card Files Files and versions
xet
Community
3

Instructions to use michiyasunaga/BioLinkBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use michiyasunaga/BioLinkBERT-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="michiyasunaga/BioLinkBERT-base")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("michiyasunaga/BioLinkBERT-base")
    model = AutoModel.from_pretrained("michiyasunaga/BioLinkBERT-base")
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
BioLinkBERT-base
434 MB
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  • 1 contributor
History: 1 commit
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  • .gitattributes
    1.18 kB
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  • README.md
    3.82 kB
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  • config.json
    559 Bytes
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  • pytorch_model.bin
    433 MB
    xet
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  • special_tokens_map.json
    112 Bytes
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  • tokenizer.json
    447 kB
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  • tokenizer_config.json
    379 Bytes
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  • vocab.txt
    225 kB
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