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panigrah
/
wineberto-ner

Token Classification
Transformers
PyTorch
bert
wine
ner
Model card Files Files and versions
xet
Community
1

Instructions to use panigrah/wineberto-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use panigrah/wineberto-ner with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="panigrah/wineberto-ner")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("panigrah/wineberto-ner")
    model = AutoModelForTokenClassification.from_pretrained("panigrah/wineberto-ner", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
wineberto-ner
437 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
panigrah's picture
panigrah
Update README.md
c13d4ec over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    2.99 kB
    Update README.md over 2 years ago
  • config.json
    1.42 kB
    Added more entities to training set and also randomized wine label. Earlier training set was flawed because wine labels were always located at the beginning of the description of the wine and the model trained itself to tag everything in the begining of the description as the wine label. almost 3 years ago
  • pytorch_model.bin
    436 MB
    xet
    Added more entities to training set and also randomized wine label. Earlier training set was flawed because wine labels were always located at the beginning of the description of the wine and the model trained itself to tag everything in the begining of the description as the wine label. almost 3 years ago
  • special_tokens_map.json
    125 Bytes
    Upload 6 files almost 3 years ago
  • tokenizer.json
    711 kB
    Upload 6 files almost 3 years ago
  • tokenizer_config.json
    314 Bytes
    Upload 6 files almost 3 years ago
  • training_args.bin
    3.96 kB
    xet
    Added more entities to training set and also randomized wine label. Earlier training set was flawed because wine labels were always located at the beginning of the description of the wine and the model trained itself to tag everything in the begining of the description as the wine label. almost 3 years ago
  • vocab.txt
    232 kB
    Upload 6 files almost 3 years ago