Instructions to use microsoft/deberta-v2-xlarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-v2-xlarge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="microsoft/deberta-v2-xlarge")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/deberta-v2-xlarge", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.bacc8cb
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Add task to enable widget (#1)
- Add task to enable widget (280cd0cd4ebc941b4edd6b6836da4729a73bb5a0) Co-authored-by: Lewis Tunstall <lewtun@users.noreply.huggingface.co>1d13496
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Adding TF checkpoint
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Update README.md
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Update README.md
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Update README.md
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Add DeBERTa v2 XLarge(900M) model
Pengcheng He2ad1845 -
initial commit
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