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vishwa27
/
rm_longformer_h1r2_FAC

Token Classification
Transformers
PyTorch
longformer
Model card Files Files and versions
xet
Community
1

Instructions to use vishwa27/rm_longformer_h1r2_FAC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use vishwa27/rm_longformer_h1r2_FAC with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="vishwa27/rm_longformer_h1r2_FAC")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("vishwa27/rm_longformer_h1r2_FAC")
    model = AutoModelForTokenClassification.from_pretrained("vishwa27/rm_longformer_h1r2_FAC")
  • Notebooks
  • Google Colab
  • Kaggle
rm_longformer_h1r2_FAC
596 MB
Ctrl+K
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  • 1 contributor
History: 3 commits
vishwa27's picture
vishwa27
Upload tokenizer
bbaa632 verified almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • config.json
    1.18 kB
    Upload LongformerForTokenClassification almost 2 years ago
  • merges.txt
    456 kB
    Upload tokenizer almost 2 years ago
  • pytorch_model.bin
    592 MB
    xet
    Upload LongformerForTokenClassification almost 2 years ago
  • special_tokens_map.json
    280 Bytes
    Upload tokenizer almost 2 years ago
  • tokenizer.json
    2.11 MB
    Upload tokenizer almost 2 years ago
  • tokenizer_config.json
    448 Bytes
    Upload tokenizer almost 2 years ago
  • vocab.json
    798 kB
    Upload tokenizer almost 2 years ago