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", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c022c8730613b3bdba3a400440bd2778033832bc14b575077dc370ab96f802d
- Size of remote file:
- 1.48 GB
- SHA256:
- 3f8af0d8ba799382ab50b6359d3b0ffa4d59751da21b2bdbf544e296379a8f46
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