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lidiya
/
bart-base-samsum

Summarization
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
seq2seq
Eval Results (legacy)
Model card Files Files and versions
xet
Community
4

Instructions to use lidiya/bart-base-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use lidiya/bart-base-samsum 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="lidiya/bart-base-samsum")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("lidiya/bart-base-samsum")
    model = AutoModelForSeq2SeqLM.from_pretrained("lidiya/bart-base-samsum")
  • Notebooks
  • Google Colab
  • Kaggle
bart-base-samsum
561 MB
Ctrl+K
Ctrl+K
  • 4 contributors
History: 7 commits
lewtun's picture
lewtun HF Staff
Fix typo in ROUGE metrics
c3da2c2 almost 4 years ago
  • .gitattributes
    690 Bytes
    initial commit almost 5 years ago
  • README.md
    2.9 kB
    Fix typo in ROUGE metrics almost 4 years ago
  • config.json
    1.68 kB
    Initial commit almost 5 years ago
  • merges.txt
    456 kB
    Initial commit almost 5 years ago
  • pytorch_model.bin
    558 MB
    xet
    Initial commit almost 5 years ago
  • special_tokens_map.json
    239 Bytes
    Initial commit almost 5 years ago
  • tokenizer.json
    1.36 MB
    Initial commit almost 5 years ago
  • tokenizer_config.json
    295 Bytes
    Initial commit almost 5 years ago
  • vocab.json
    798 kB
    Initial commit almost 5 years ago