Instructions to use microsoft/deberta-v3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-v3-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="microsoft/deberta-v3-base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/deberta-v3-base", dtype="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Fix typo in README.md
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README.md
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@@ -16,7 +16,7 @@ In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the effic
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Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
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The DeBERTa V3 base model comes with 12 layers and a hidden size of 768. It has only 86M backbone parameters with a vocabulary containing 128K tokens which introduces 98M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2.
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#### Fine-tuning on NLU tasks
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Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
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The DeBERTa V3 base model comes with 12 layers and a hidden size of 768. It has only 86M backbone parameters with a vocabulary containing 128K tokens which introduces 98M parameters in the Embedding layer. This model was trained using the same 160GB data as DeBERTa V2.
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#### Fine-tuning on NLU tasks
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