Instructions to use VMware/deberta-v3-base-mrqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VMware/deberta-v3-base-mrqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="VMware/deberta-v3-base-mrqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("VMware/deberta-v3-base-mrqa") model = AutoModelForQuestionAnswering.from_pretrained("VMware/deberta-v3-base-mrqa") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model (#1)
Browse files- Adding `safetensors` variant of this model (b8db3bba582b80f074255299a6beabf170aa7ea7)
Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:6256b916b78f3bc5195d99ab5c2bdba8bb23d7d022aa4e649b515b5dca9f4e59
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size 735360944
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