Summarization
Transformers
PyTorch
TensorBoard
English
bart
text2text-generation
azureml
azure
codecarbon
Eval Results (legacy)
Instructions to use linydub/bart-large-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linydub/bart-large-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="linydub/bart-large-samsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("linydub/bart-large-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("linydub/bart-large-samsum") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18416f01c238eed85c9c1e836d2cc1fa531ecce28d8e2ce2edb9c5643b6f46a2
- Size of remote file:
- 2.86 kB
- SHA256:
- 5a2d6c9f4f01c97b5e055b931c3261dc5b105b1938180836634f0b17bc571c2f
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