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
- Xet hash:
- 28f0dc685b458c62ac31e3cd46d1deb09e7c771703b48d0bd0b74ee480304264
- Size of remote file:
- 1.35 GB
- SHA256:
- 9bedbb712a8c9de0ee710030ecd47616a52dc533409859c53a2e77069b1bd2f0
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