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
Download flax_model.msgpack from readerbench/RoBERT-large: direct link, hf CLI and curl.
- Browser
- Download file 1.37 GB
-
https://huggingface.co/readerbench/RoBERT-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://readerbench/RoBERT-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/readerbench/RoBERT-large/resolve/main/flax_model.msgpack
1.37 GB
- Xet hash:
- ab12802a6b188da457a8a70e15c847b818724ce01b7de0f8277d8a4f813669a0
- Size of remote file:
- 1.37 GB
- SHA256:
- d7b0858b4941868dab8b2f76874d61a3c0b06072d80cd7537d97e4f4c1daa356
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