Instructions to use QuickRead/Reward_training_Pegasus_xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuickRead/Reward_training_Pegasus_xsum with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="QuickRead/Reward_training_Pegasus_xsum")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("QuickRead/Reward_training_Pegasus_xsum") model = AutoModel.from_pretrained("QuickRead/Reward_training_Pegasus_xsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1df8bb0bbe4ceaa3cea75f5e6b124d077420aa3659e984c241c67bec41cce4da
- Size of remote file:
- 2.28 GB
- SHA256:
- 62fe85a7dd361a7c8896f8c332c65482d0b57e19cc143a337032dae9643bfb9b
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