Instructions to use readerbench/RoBERT-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use readerbench/RoBERT-large with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("readerbench/RoBERT-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 453fe7e27ebdb12f4e307e17375016a9a2a08103bf6c4abbc1668e56b565afcd
- Size of remote file:
- 1.37 GB
- SHA256:
- 498bc6dfffb91c89024dc5de321b4a68a83d605a8c3ab6b589d8662526dec12e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.