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Yale-LILY
/
reastap-large

Table Question Answering
Transformers
PyTorch
English
bart
text2text-generation
table-fact-checking
table-to-text
Model card Files Files and versions
xet
Community
2

Instructions to use Yale-LILY/reastap-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Yale-LILY/reastap-large with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("table-question-answering", model="Yale-LILY/reastap-large")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("Yale-LILY/reastap-large")
    model = AutoModelForSeq2SeqLM.from_pretrained("Yale-LILY/reastap-large")
  • Notebooks
  • Google Colab
  • Kaggle
reastap-large
1.63 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
yilunzhao's picture
yilunzhao
Update README.md
befb393 almost 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • README.md
    3.42 kB
    Update README.md almost 3 years ago
  • config.json
    1.71 kB
    correct model path almost 3 years ago
  • generation_config.json
    258 Bytes
    Upload folder using huggingface_hub almost 3 years ago
  • merges.txt
    456 kB
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  • pytorch_model.bin
    1.63 GB
    xet
    Upload folder using huggingface_hub almost 3 years ago
  • special_tokens_map.json
    957 Bytes
    Upload folder using huggingface_hub almost 3 years ago
  • tokenizer_config.json
    1.38 kB
    Upload folder using huggingface_hub almost 3 years ago
  • vocab.json
    999 kB
    Upload folder using huggingface_hub almost 3 years ago