Instructions to use VMware/deberta-v3-large-mrqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VMware/deberta-v3-large-mrqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="VMware/deberta-v3-large-mrqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("VMware/deberta-v3-large-mrqa") model = AutoModelForQuestionAnswering.from_pretrained("VMware/deberta-v3-large-mrqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a36545747e7fe462aac76efbf67b1f213d2753181641e594170b4e95303369f
- Size of remote file:
- 1.74 GB
- SHA256:
- 9d279794ae4275e05c7060afe052aa0e0b28d9d9cac24f1cfddbe7cb313f94a9
路
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