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erbacher
/
t5-base-claim

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

Instructions to use erbacher/t5-base-claim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use erbacher/t5-base-claim with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("erbacher/t5-base-claim")
    model = AutoModelForSeq2SeqLM.from_pretrained("erbacher/t5-base-claim")
  • Notebooks
  • Google Colab
  • Kaggle
t5-base-claim
Ctrl+K
Ctrl+K
  • 1 contributor
History: 38 commits
erbacher's picture
erbacher
Training in progress, step 4000
9526fe1 almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • .gitignore
    13 Bytes
    Training in progress, step 2000 almost 3 years ago
  • config.json
    1.53 kB
    Training in progress, step 2000 almost 3 years ago
  • pytorch_model.bin
    990 MB
    xet
    Training in progress, step 4000 almost 3 years ago
  • special_tokens_map.json
    2.2 kB
    Training in progress, step 2000 almost 3 years ago
  • spiece.model
    792 kB
    xet
    Training in progress, step 2000 almost 3 years ago
  • tokenizer.json
    2.42 MB
    Training in progress, step 2000 almost 3 years ago
  • tokenizer_config.json
    2.35 kB
    Training in progress, step 2000 almost 3 years ago
  • training_args.bin
    4.09 kB
    xet
    Training in progress, step 2000 almost 3 years ago