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 # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="VMware/deberta-v3-base-mrqa")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from VMware/deberta-v3-base-mrqa: direct link, hf CLI and curl.
- Browser
- Download file 735 MB
-
https://huggingface.co/VMware/deberta-v3-base-mrqa/resolve/main/model.safetensors
- Command line
-
hf download hf://VMware/deberta-v3-base-mrqa/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/VMware/deberta-v3-base-mrqa/resolve/main/model.safetensors
735 MB
- Xet hash:
- a2ebca4ce47ed75981873245ca39149ef0d67fabd2e3b777c0b33cdc0955ca42
- Size of remote file:
- 735 MB
- SHA256:
- 6256b916b78f3bc5195d99ab5c2bdba8bb23d7d022aa4e649b515b5dca9f4e59
路
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