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l3cube-pune
/
mahahate-multi-roberta

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

Instructions to use l3cube-pune/mahahate-multi-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use l3cube-pune/mahahate-multi-roberta with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="l3cube-pune/mahahate-multi-roberta")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/mahahate-multi-roberta")
    model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/mahahate-multi-roberta")
  • Notebooks
  • Google Colab
  • Kaggle
mahahate-multi-roberta
2.24 GB
Ctrl+K
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  • 2 contributors
History: 8 commits
l3cube-pune's picture
l3cube-pune
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SFconvertbot
Adding `safetensors` variant of this model (#1)
abe958d about 3 years ago
  • .gitattributes
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  • README.md
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  • config.json
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  • model.safetensors
    1.11 GB
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  • pytorch_model.bin
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  • special_tokens_map.json
    239 Bytes
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  • tokenizer.json
    17.1 MB
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  • tokenizer_config.json
    391 Bytes
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