Instructions to use BSC-LT/mRoBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/mRoBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BSC-LT/mRoBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BSC-LT/mRoBERTa") model = AutoModelForMaskedLM.from_pretrained("BSC-LT/mRoBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from BSC-LT/mRoBERTa: direct link, hf CLI and curl.
- Browser
- Download file 1.13 GB
-
https://huggingface.co/BSC-LT/mRoBERTa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BSC-LT/mRoBERTa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BSC-LT/mRoBERTa/resolve/main/pytorch_model.bin
1.13 GB
- Xet hash:
- 8a99fe8ed731a16f19f02cca9afbdf38292b29daf7d1933d6236a7ca71db2b45
- Size of remote file:
- 1.13 GB
- SHA256:
- e20d0734fd085e691495bb99dc5a11693eed22cbf1c841eef4a066efcaa8b473
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