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nbroad
/
ESG-BERT

Text Classification
Transformers
PyTorch
Safetensors
English
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community
15

Instructions to use nbroad/ESG-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nbroad/ESG-BERT with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="nbroad/ESG-BERT")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("nbroad/ESG-BERT")
    model = AutoModelForSequenceClassification.from_pretrained("nbroad/ESG-BERT")
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
ESG-BERT
439 MB
Ctrl+K
Ctrl+K
  • 5 contributors
History: 5 commits
nazneen's picture
nazneen
model documentation
1ae536b over 3 years ago
  • .gitattributes
    1.18 kB
    initial commit over 4 years ago
  • README.md
    10.6 kB
    model documentation over 3 years ago
  • config.json
    2.67 kB
    commit files to HF hub 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
    commit files to HF hub over 4 years ago
  • special_tokens_map.json
    112 Bytes
    commit files to HF hub over 4 years ago
  • tokenizer.json
    466 kB
    commit files to HF hub over 4 years ago
  • tokenizer_config.json
    376 Bytes
    commit files to HF hub over 4 years ago
  • vocab.txt
    232 kB
    commit files to HF hub over 4 years ago