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VRT
/
mT5_summarization

Transformers
PyTorch
mbart
text2text-generation
Model card Files Files and versions
xet
Community
1

Instructions to use VRT/mT5_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use VRT/mT5_summarization with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("VRT/mT5_summarization")
    model = AutoModelForSeq2SeqLM.from_pretrained("VRT/mT5_summarization")
  • Notebooks
  • Google Colab
  • Kaggle
mT5_summarization
4.9 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
d00149a verified over 1 year ago
  • .gitattributes
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  • config.json
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  • model.safetensors
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  • pytorch_model.bin
    2.44 GB
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  • sentencepiece.bpe.model
    5.07 MB
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  • special_tokens_map.json
    495 Bytes
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  • tokenizer.json
    9.08 MB
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  • tokenizer_config.json
    474 Bytes
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  • training_args.bin
    3.12 kB
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