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.json from BSC-LT/MrBERT: direct link, hf CLI and curl.
- Browser
- Download file 19.1 MB
-
https://huggingface.co/BSC-LT/MrBERT/resolve/main/tokenizer.json
- Command line
-
hf download hf://BSC-LT/MrBERT/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BSC-LT/MrBERT/resolve/main/tokenizer.json
19.1 MB
- Xet hash:
- 6c8eaca4acf271605a0a45e3841e84bd979946ae07d4dea983e470c61dd03952
- Size of remote file:
- 19.1 MB
- SHA256:
- 5de759bbb22a74d2cdd9ea819fa46faa89612fff8d2fcce93a926961d2a233a9
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