Summarization
Transformers
PyTorch
English
pegasus
text2text-generation
seq2seq
Eval Results (legacy)
Instructions to use tuner007/pegasus_summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuner007/pegasus_summarizer 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="tuner007/pegasus_summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tuner007/pegasus_summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("tuner007/pegasus_summarizer") - Notebooks
- Google Colab
- Kaggle
Add evaluation results on the default config of xsum
#1
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator π!
Your model has been evaluated on the default config of the xsum dataset by @Neez , using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.