Summarization
Transformers
PyTorch
TensorFlow
JAX
English
pegasus
text2text-generation
Eval Results (legacy)
Instructions to use google/pegasus-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/pegasus-xsum 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-xsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("google/pegasus-xsum") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d5657b36935306d0a4220c7402c8a8c858a4da85f114a8aab8c5318ecb8fb39
- Size of remote file:
- 2.27 GB
- SHA256:
- e114926c773dd59cd5fd043292a6ff280d2260b405c0aa4081e74866707c105c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.