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 pytorch_model.bin 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/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://VMware/deberta-v3-base-mrqa@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/VMware/deberta-v3-base-mrqa/resolve/refs%2Fpr%2F1/pytorch_model.bin
735 MB
- Xet hash:
- d61920201806f51d9bfdcabd0aa6b250663689402bf261f252996b8c1aba3561
- Size of remote file:
- 735 MB
- SHA256:
- a6106f9014131642fc6ec35e364b2081686ed4a34b04335089bdccb2c1fbba15
路
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