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jrc-ai
/
PreDA-base

Transformers
PyTorch
Safetensors
English
t5
text2text-generation
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use jrc-ai/PreDA-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use jrc-ai/PreDA-base with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("jrc-ai/PreDA-base")
    model = AutoModelForSeq2SeqLM.from_pretrained("jrc-ai/PreDA-base", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
PreDA-base
1.79 GB
Ctrl+K
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  • 1 contributor
History: 32 commits
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lorenzoscottb
Update README.md
cc71021 verified about 2 months ago
  • .gitattributes
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    Upload preda_architecture_digram.png almost 2 years ago
  • README.md
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  • config.json
    1.51 kB
    Training in progress, epoch 1 almost 2 years ago
  • generation_config.json
    112 Bytes
    lorenzoscottb/PreDA-t5-base almost 2 years ago
  • model.safetensors
    892 MB
    xet
    lorenzoscottb/PreDA-t5-base almost 2 years ago
  • preda_architecture_digram.png
    1.28 MB
    xet
    Upload preda_architecture_digram.png almost 2 years ago
  • pytorch_model.bin
    892 MB
    xet
    Training in progress, epoch 20 almost 2 years ago
  • special_tokens_map.json
    2.2 kB
    Training in progress, epoch 1 almost 2 years ago
  • tokenizer.json
    2.42 MB
    lorenzoscottb/PreDA-t5-base almost 2 years ago
  • tokenizer_config.json
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    Training in progress, epoch 1 almost 2 years ago
  • training_args.bin
    5.3 kB
    xet
    Training in progress, epoch 1 almost 2 years ago