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sshleifer
/
bart-large-fp32

Feature Extraction
Transformers
PyTorch
Rust
bart
Model card Files Files and versions
xet
Community
1

Instructions to use sshleifer/bart-large-fp32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use sshleifer/bart-large-fp32 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="sshleifer/bart-large-fp32")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("sshleifer/bart-large-fp32")
    model = AutoModel.from_pretrained("sshleifer/bart-large-fp32")
  • Notebooks
  • Google Colab
  • Kaggle
bart-large-fp32
3.66 GB
Ctrl+K
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History: 7 commits
system's picture
system HF Staff
Update pytorch_model.bin
0a64a99 over 5 years ago
  • .gitattributes
    345 Bytes
    initial commit over 5 years ago
  • config.json
    1.26 kB
    Update config.json over 5 years ago
  • merges.txt
    456 kB
    Update merges.txt over 5 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    1.63 GB
    xet
    Update pytorch_model.bin over 5 years ago
  • rust_model.ot
    2.04 GB
    xet
    Update rust_model.ot over 5 years ago
  • tokenizer_config.json
    26 Bytes
    Update tokenizer_config.json over 5 years ago
  • vocab.json
    899 kB
    Update vocab.json over 5 years ago