Instructions to use BSC-LT/RoBERTalex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/RoBERTalex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BSC-LT/RoBERTalex")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BSC-LT/RoBERTalex") model = AutoModelForMaskedLM.from_pretrained("BSC-LT/RoBERTalex") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1123225d2399087f821c25bd660844198c6ee080487709fa2c9689768a9e099e
- Size of remote file:
- 504 MB
- SHA256:
- 7fd3da1f60a3d1212338f5b79c020681ac303495ba1b9aebf035b70db623fa81
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