Instructions to use baikalai/dbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baikalai/dbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="baikalai/dbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("baikalai/dbert") model = AutoModelForMaskedLM.from_pretrained("baikalai/dbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from baikalai/dbert: direct link, hf CLI and curl.
- Browser
- Download file 454 MB
-
https://huggingface.co/baikalai/dbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://baikalai/dbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/baikalai/dbert/resolve/main/pytorch_model.bin
454 MB
- Xet hash:
- 3f6addef08d0e9ebf84fa68400e32e1dc9237162c912561a6ac3576a34be5bb2
- Size of remote file:
- 454 MB
- SHA256:
- ef853d4b03ed00667b502ccc67267ced33a6997fb704f554c4c82097157acffa
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