Instructions to use sshleifer/bart-large-fp32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/bart-large-fp32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sshleifer/bart-large-fp32")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sshleifer/bart-large-fp32") model = AutoModel.from_pretrained("sshleifer/bart-large-fp32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f8564aa1616c1108d13d82a3d9dc7fa2eed289f8aa038af0a2197470e03126a
- Size of remote file:
- 2.04 GB
- SHA256:
- 931cb440da07893989f9bd4a7f6cb7257b8eae104bcafeee69bb066c49ffb01c
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