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enod
/
esg-bert

Text Classification
Transformers
PyTorch
bert
Model card Files Files and versions
xet
Community
1

Instructions to use enod/esg-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use enod/esg-bert with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="enod/esg-bert")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("enod/esg-bert")
    model = AutoModelForSequenceClassification.from_pretrained("enod/esg-bert")
  • Notebooks
  • Google Colab
  • Kaggle
esg-bert
438 MB
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  • 1 contributor
History: 3 commits
enod's picture
enod
add model
9e022f7 over 4 years ago
  • .gitattributes
    1.18 kB
    initial commit over 4 years ago
  • config.json
    1.77 kB
    add model over 4 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

    • "torch.FloatStorage",
    • "torch.LongStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict"

    What is a pickle import?

    438 MB
    xet
    add model over 4 years ago
  • special_tokens_map.json
    112 Bytes
    add tokenizer over 4 years ago
  • tokenizer_config.json
    424 Bytes
    add tokenizer over 4 years ago
  • vocab.txt
    232 kB
    add tokenizer over 4 years ago