Instructions to use ucla-nb-project/bart-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ucla-nb-project/bart-finetuned with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ucla-nb-project/bart-finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("ucla-nb-project/bart-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ucla-nb-project/bart-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/ucla-nb-project/bart-finetuned/resolve/main/training_args.bin
- Command line
-
hf download hf://ucla-nb-project/bart-finetuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ucla-nb-project/bart-finetuned/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- 52f5c1406ce3cb85ba3a0347890f097cac73b09ec4cb254f333383d8101ed4d4
- Size of remote file:
- 4.86 kB
- SHA256:
- 7f983fc746b5cb7439b842ceef6975b272e02cf836516fc6cbf211fe4e4dcb9d
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