Instructions to use deepset/gbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="deepset/gbert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("deepset/gbert-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deepset/gbert-base: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/deepset/gbert-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://deepset/gbert-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepset/gbert-base/resolve/main/pytorch_model.bin
442 MB
- Xet hash:
- 0918eb5ff06e8e562513d1175fd7052bd1d29bb8856137ac29883fad8581c6ee
- Size of remote file:
- 442 MB
- SHA256:
- 227220a5807266f9120d332a562ad169c95411c54ee0f2439955fe5dcea87867
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