Instructions to use nandezgarcia/roberta-base-bne-finetuned-recores-long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nandezgarcia/roberta-base-bne-finetuned-recores-long with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("nandezgarcia/roberta-base-bne-finetuned-recores-long") model = AutoModelForMultipleChoice.from_pretrained("nandezgarcia/roberta-base-bne-finetuned-recores-long", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nandezgarcia/roberta-base-bne-finetuned-recores-long: direct link, hf CLI and curl.
- Browser
- Download file 3.12 kB
-
https://huggingface.co/nandezgarcia/roberta-base-bne-finetuned-recores-long/resolve/main/training_args.bin
- Command line
-
hf download hf://nandezgarcia/roberta-base-bne-finetuned-recores-long/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nandezgarcia/roberta-base-bne-finetuned-recores-long/resolve/main/training_args.bin
3.12 kB
- Xet hash:
- 2835b14c6c78e5dd1099f8820f466a473be27eab1786bda5e23dff1ce9324ab5
- Size of remote file:
- 3.12 kB
- SHA256:
- f24d9738bfda7b895d2495f8d1d0fa9b9d4c06bb7d175ff121acade8f94c301b
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