Summarization
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
seq2seq
Eval Results (legacy)
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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lidiya/bart-base-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("lidiya/bart-base-samsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#4)
- Adding `safetensors` variant of this model (c864e8256aa1c6dae9d297d072548558b6f075f3) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>6d9e0d7
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Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (#3)
- Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (04638e9b48a06f66cdc9c63e1cfc914487d49f57) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>2b0c321
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Fix typo in ROUGE metrics (#2)
- Fix typo in ROUGE metrics (c3da2c23e4fa7af91bd721efb83052bf8367ae8e) Co-authored-by: Lewis Tunstall <lewtun@users.noreply.huggingface.co>eeb1911
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Add evaluation results on samsum dataset (#1)
- Add evaluation results on samsum dataset (d9180b549d58db8d3c078617c8c1f689a1d561b2) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>8a514eb
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Update README
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Update README
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Add README
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