Instructions to use facebook/bart-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/bart-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/bart-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("facebook/bart-base") model = AutoModel.from_pretrained("facebook/bart-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/bart-base: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/facebook/bart-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/bart-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/bart-base/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- b65f15d988de7dde0626e19c131fe7fd002de54c1cf1fdbd344e34c7f84ff2bf
- Size of remote file:
- 558 MB
- SHA256:
- 6e4fdb5e04114696ec4f7cc0cb5fc2a22a826f1c9eea234ca550b5390341a2fd
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