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Forturne
/
KPMG-NARVIS-summarization

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

Instructions to use Forturne/KPMG-NARVIS-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Forturne/KPMG-NARVIS-summarization with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("Forturne/KPMG-NARVIS-summarization")
    model = AutoModelForSeq2SeqLM.from_pretrained("Forturne/KPMG-NARVIS-summarization")
  • Notebooks
  • Google Colab
  • Kaggle
KPMG-NARVIS-summarization
497 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 8 commits
Forturne's picture
Forturne
Update README.md
ab5b12a over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • README.md
    3.95 kB
    Update README.md over 3 years ago
  • config.json
    1.3 kB
    Upload BartForConditionalGeneration over 3 years ago
  • generation_config.json
    191 Bytes
    Upload BartForConditionalGeneration over 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    496 MB
    xet
    Upload BartForConditionalGeneration over 3 years ago
  • special_tokens_map.json
    122 Bytes
    Upload tokenizer over 3 years ago
  • tokenizer.json
    1.05 MB
    Upload tokenizer over 3 years ago
  • tokenizer_config.json
    365 Bytes
    Upload tokenizer over 3 years ago