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
- Xet hash:
- d61920201806f51d9bfdcabd0aa6b250663689402bf261f252996b8c1aba3561
- Size of remote file:
- 735 MB
- SHA256:
- a6106f9014131642fc6ec35e364b2081686ed4a34b04335089bdccb2c1fbba15
路
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