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Xmm
/
bart-large-entity

Summarization
Transformers
PyTorch
English
bart
text2text-generation
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use Xmm/bart-large-entity with Transformers:

    # Use a pipeline as a high-level helper
    # Warning: Pipeline type "summarization" is no longer supported in transformers v5.
    # You must load the model directly (see below) or downgrade to v4.x with:
    # 'pip install "transformers<5.0.0'
    from transformers import pipeline
    
    pipe = pipeline("summarization", model="Xmm/bart-large-entity")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("Xmm/bart-large-entity")
    model = AutoModelForSeq2SeqLM.from_pretrained("Xmm/bart-large-entity", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
bart-large-entity
4.88 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
Xmm's picture
Xmm
Upload 14 files
f81cb74 about 3 years ago
  • .gitattributes
    1.52 kB
    initial commit about 3 years ago
  • README.md
    6 kB
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  • config.json
    1.65 kB
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  • generation_config.json
    380 Bytes
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  • generation_config_for_summarization.json
    363 Bytes
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  • merges.txt
    456 kB
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  • optimizer.pt
    3.25 GB
    xet
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  • pytorch_model.bin
    1.63 GB
    xet
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  • rng_state.pth
    14.5 kB
    xet
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  • scaler.pt
    557 Bytes
    xet
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  • scheduler.pt
    627 Bytes
    xet
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  • special_tokens_map.json
    280 Bytes
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  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    344 Bytes
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  • trainer_state.json
    43.3 kB
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  • training_args.bin
    3.9 kB
    xet
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  • vocab.json
    798 kB
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