Instructions to use danlou/roberta-large-finetuned-csqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danlou/roberta-large-finetuned-csqa with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("danlou/roberta-large-finetuned-csqa") model = AutoModelForMultipleChoice.from_pretrained("danlou/roberta-large-finetuned-csqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from danlou/roberta-large-finetuned-csqa: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/danlou/roberta-large-finetuned-csqa/resolve/dadcd61b4ecc12bcdf66d34327dfa4960d575087/pytorch_model.bin
- Command line
-
hf download hf://danlou/roberta-large-finetuned-csqa@dadcd61b4ecc12bcdf66d34327dfa4960d575087/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/danlou/roberta-large-finetuned-csqa/resolve/dadcd61b4ecc12bcdf66d34327dfa4960d575087/pytorch_model.bin
1.42 GB
- Xet hash:
- 431592e131d38913dd2bda35c71e45bc1bc5b8c5e5b008d316e05224f7a76c13
- Size of remote file:
- 1.42 GB
- SHA256:
- 122dedf530fca2c53e39f880bccdbffdfe1dd1bec501f71037af0a475113641c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.