Instructions to use google/pegasus-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/pegasus-large 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="google/pegasus-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/pegasus-large") model = AutoModelForSeq2SeqLM.from_pretrained("google/pegasus-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on the default config of xsum
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\ Your model has been evaluated on the default config of the [xsum](https://huggingface.co/datasets/xsum) dataset by @xarymast, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-8dc1621c-12925734).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=xsum).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=xsum).1eaf8f0
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