Instructions to use deepset/gbert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/gbert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="deepset/gbert-large")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("deepset/gbert-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deepset/gbert-large: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/deepset/gbert-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://deepset/gbert-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepset/gbert-large/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- cea5541525f0b2735cc12aeccb4041782972719c8faedfa43a294c1fc844328f
- Size of remote file:
- 1.35 GB
- SHA256:
- 486f35f65525e99baa4e2c801a92fb4986fc9c8c18e3fba2d890fdb3b222088b
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