Instructions to use eawang/roberta_mc_model_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eawang/roberta_mc_model_8 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("eawang/roberta_mc_model_8") model = AutoModelForMultipleChoice.from_pretrained("eawang/roberta_mc_model_8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from eawang/roberta_mc_model_8: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/eawang/roberta_mc_model_8/resolve/main/training_args.bin
- Command line
-
hf download hf://eawang/roberta_mc_model_8/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eawang/roberta_mc_model_8/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 6f023b24c15c452e160ce51e572b6f4a2f2f506c9082badfec9812de4d11b466
- Size of remote file:
- 3.58 kB
- SHA256:
- 77fe9033322432c6ab5d6590f0b86dd908620be9d04b47c481f653a00e36a38b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.