Instructions to use BSC-LT/MrBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/MrBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BSC-LT/MrBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BSC-LT/MrBERT") model = AutoModelForMaskedLM.from_pretrained("BSC-LT/MrBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd57378407d0dbda5c050836ffa43024f87bf5b1bc993c3ff5a734fef7e68ed9
- Size of remote file:
- 4.81 MB
- SHA256:
- 8ddbda5816a0138ffd754cbbfafceba9628342cdd91df4bea6ee86f0fb44eae9
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