Instructions to use EuroBERT/EuroBERT-610m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EuroBERT/EuroBERT-610m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EuroBERT/EuroBERT-610m", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EuroBERT/EuroBERT-610m", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("EuroBERT/EuroBERT-610m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from EuroBERT/EuroBERT-610m: direct link, hf CLI and curl.
- Browser
- Download file 3.02 GB
-
https://huggingface.co/EuroBERT/EuroBERT-610m/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://EuroBERT/EuroBERT-610m/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EuroBERT/EuroBERT-610m/resolve/main/pytorch_model.bin
3.02 GB
- Xet hash:
- 36dd0400f3b93214e562e961d37325b4455362af8fd628a2ad68cba2cc9ad498
- Size of remote file:
- 3.02 GB
- SHA256:
- a9ec9ab592ee33fee99ffc6d4ca90800866630f1023cbda2f56cec88625601dc
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