Instructions to use jhu-clsp/mmBERT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhu-clsp/mmBERT-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jhu-clsp/mmBERT-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmBERT-small") model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/mmBERT-small", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jhu-clsp/mmBERT-small: direct link, hf CLI and curl.
- Browser
- Download file 564 MB
-
https://huggingface.co/jhu-clsp/mmBERT-small/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jhu-clsp/mmBERT-small/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jhu-clsp/mmBERT-small/resolve/main/pytorch_model.bin
564 MB
- Xet hash:
- d3f44d1b94ecc24269a1cb069149fc0acaaf8298dce949e2d593f6c05367f148
- Size of remote file:
- 564 MB
- SHA256:
- be7c9ce2af947fb5bb17a3bc919552f898c5ef4164bf0a7190cf25a0f98f85b6
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