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ThirdEyeData
/
Consumer-Complaint-Categorization

Text Classification
Transformers
PyTorch
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community
2

Instructions to use ThirdEyeData/Consumer-Complaint-Categorization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ThirdEyeData/Consumer-Complaint-Categorization with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="ThirdEyeData/Consumer-Complaint-Categorization")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("ThirdEyeData/Consumer-Complaint-Categorization")
    model = AutoModelForSequenceClassification.from_pretrained("ThirdEyeData/Consumer-Complaint-Categorization", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Consumer-Complaint-Categorization
439 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 8 commits
saritha5's picture
saritha5
Upload pytorch_model.bin
14fdadf over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • config.json
    1.09 kB
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  • pytorch_model.bin
    438 MB
    xet
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  • special_tokens_map.json
    125 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    348 Bytes
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  • training_args.bin
    3.39 kB
    xet
    Upload training_args.bin over 3 years ago
  • vocab.txt
    232 kB
    Upload vocab.txt over 3 years ago