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OTAR3088
/
CeLLaTe-ner-3class-bioformer8l-baseline

Token Classification
Transformers
Safetensors
English
bert
Generated from Trainer
Model card Files Files and versions
xet
Community

Instructions to use OTAR3088/CeLLaTe-ner-3class-bioformer8l-baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use OTAR3088/CeLLaTe-ner-3class-bioformer8l-baseline with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="OTAR3088/CeLLaTe-ner-3class-bioformer8l-baseline")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("OTAR3088/CeLLaTe-ner-3class-bioformer8l-baseline")
    model = AutoModelForTokenClassification.from_pretrained("OTAR3088/CeLLaTe-ner-3class-bioformer8l-baseline", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
CeLLaTe-ner-3class-bioformer8l-baseline
170 MB
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  • 1 contributor
History: 2 commits

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  • .gitattributes
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  • README.md
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  • all_results.json
    545 Bytes
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  • config.json
    966 Bytes
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  • eval_results.json
    545 Bytes
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  • model.safetensors
    169 MB
    xet
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  • special_tokens_map.json
    125 Bytes
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  • tokenizer.json
    764 kB
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  • tokenizer_config.json
    1.3 kB
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  • train_results.json
    234 Bytes
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  • trainer_state.json
    8.08 kB
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  • training_args.bin
    5.75 kB
    xet
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  • vocab.txt
    248 kB
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