Instructions to use eawang/roberta_mc_model_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eawang/roberta_mc_model_3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("eawang/roberta_mc_model_3") model = AutoModelForMultipleChoice.from_pretrained("eawang/roberta_mc_model_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from eawang/roberta_mc_model_3: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/eawang/roberta_mc_model_3/resolve/main/training_args.bin
- Command line
-
hf download hf://eawang/roberta_mc_model_3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/eawang/roberta_mc_model_3/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- cc5115ae2787666b03d9a39a26a6c7d0a9a27568a1837d484cc11b209833a893
- Size of remote file:
- 3.58 kB
- SHA256:
- eb7e9f018c738aea805a5c68bb085d6f224294eb30b63494af867bab4adc810a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.