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NLPScholars
/
Roberta-Earning-Call-Transcript-Classification

Text Classification
Transformers
PyTorch
roberta
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use NLPScholars/Roberta-Earning-Call-Transcript-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use NLPScholars/Roberta-Earning-Call-Transcript-Classification with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="NLPScholars/Roberta-Earning-Call-Transcript-Classification")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("NLPScholars/Roberta-Earning-Call-Transcript-Classification")
    model = AutoModelForSequenceClassification.from_pretrained("NLPScholars/Roberta-Earning-Call-Transcript-Classification")
  • Notebooks
  • Google Colab
  • Kaggle
Roberta-Earning-Call-Transcript-Classification
501 MB
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  • 1 contributor
History: 32 commits
sumitranjan's picture
sumitranjan
Update README.md
dbe357e over 3 years ago
  • .gitattributes
    1.34 kB
    initial commit almost 4 years ago
  • README.md
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  • config.json
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  • merges.txt
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  • pytorch_model.bin

    Detected Pickle imports (4)

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

    What is a pickle import?

    499 MB
    xet
    Upload pytorch_model.bin almost 4 years ago
  • special_tokens_map.json
    239 Bytes
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  • tokenizer.json
    1.36 MB
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  • tokenizer_config.json
    326 Bytes
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  • vocab.json
    798 kB
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