Instructions to use jgriffi/bart_abstract_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jgriffi/bart_abstract_summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jgriffi/bart_abstract_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("jgriffi/bart_abstract_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jgriffi/bart_abstract_summarization: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/jgriffi/bart_abstract_summarization/resolve/main/training_args.bin
- Command line
-
hf download hf://jgriffi/bart_abstract_summarization/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jgriffi/bart_abstract_summarization/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- b9503940cc6b05f6d1457ea94e783ce6ffbe7c870b1212d7aaac7a4b7e090698
- Size of remote file:
- 3.31 kB
- SHA256:
- d9901c47fad989dbf8f7e64527a694a9703c0f2b6652ce2a19f0b8afb5fe240f
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