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:
- bc0ab0312e669185d5122a7ed93bb5231554cc86f45557b1682f63aaba87a637
- Size of remote file:
- 1.63 GB
- SHA256:
- c94f6c2c49654c7e9cc1fe073dbb727baaec43475ea94905acc262e4d6e737e3
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