Instructions to use facebook/bart-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/bart-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/bart-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large") model = AutoModel.from_pretrained("facebook/bart-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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typo: encoder-encoder -> encoder-decoder
BART is an encoder-decoder, not an encoder-encoder. > It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. (https://arxiv.org/abs/1910.13461)58509d6
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Read announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755 Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/facebook/bart-large/README.mdb51e601
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