Instructions to use Vailla-Rohit/bart-base-finetuned-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vailla-Rohit/bart-base-finetuned-samsum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Vailla-Rohit/bart-base-finetuned-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("Vailla-Rohit/bart-base-finetuned-samsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Vailla-Rohit/bart-base-finetuned-samsum: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/Vailla-Rohit/bart-base-finetuned-samsum/resolve/main/training_args.bin
- Command line
-
hf download hf://Vailla-Rohit/bart-base-finetuned-samsum/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Vailla-Rohit/bart-base-finetuned-samsum/resolve/main/training_args.bin
3.77 kB
- Xet hash:
- ef43a6245ff9d7e7df26b5126ca702d0a6acd540d6d50b91f65b380e752756ea
- Size of remote file:
- 3.77 kB
- SHA256:
- 7f302bac09504b99239d94a5defdea11009685d460a8d64f39c1f452ee546d4d
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