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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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python -m torch.distributed.launch --nproc_per_node=${num_gpus} \
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run_glue.py \
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--model_name_or_path microsoft/deberta-v3-
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--task_name $TASK_NAME \
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--do_train \
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--do_eval \
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python -m torch.distributed.launch --nproc_per_node=${num_gpus} \
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run_glue.py \
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--model_name_or_path microsoft/deberta-v3-base \
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--task_name $TASK_NAME \
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--do_train \
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--do_eval \
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