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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BSC-LT/MrBERT") model = AutoModelForMaskedLM.from_pretrained("BSC-LT/MrBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from BSC-LT/MrBERT: direct link, hf CLI and curl.
- Browser
- Download file 4.81 MB
-
https://huggingface.co/BSC-LT/MrBERT/resolve/main/tokenizer.model
- Command line
-
hf download hf://BSC-LT/MrBERT/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/BSC-LT/MrBERT/resolve/main/tokenizer.model
4.81 MB
- Xet hash:
- bd57378407d0dbda5c050836ffa43024f87bf5b1bc993c3ff5a734fef7e68ed9
- Size of remote file:
- 4.81 MB
- SHA256:
- 8ddbda5816a0138ffd754cbbfafceba9628342cdd91df4bea6ee86f0fb44eae9
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