Instructions to use sshleifer/distilbart-xsum-1-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-xsum-1-1 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="sshleifer/distilbart-xsum-1-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-1-1") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-xsum-1-1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 144b62e799816e873905f9faf27217e9ea1aad76a9108f85e22b20199ad479f2
- Size of remote file:
- 332 MB
- SHA256:
- 4aba1bea99e65bcc92d2f200c618e7619fd674b8e8fef780f0674674469a674e
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