Instructions to use stevemobs/deberta-base-finetuned-aqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stevemobs/deberta-base-finetuned-aqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="stevemobs/deberta-base-finetuned-aqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("stevemobs/deberta-base-finetuned-aqa") model = AutoModelForQuestionAnswering.from_pretrained("stevemobs/deberta-base-finetuned-aqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e890c5854d6a5fc52bbb8d7d78d00f4259158f8b862eab31419a68572002b13b
- Size of remote file:
- 554 MB
- SHA256:
- 893e3c535b829690f16ceafb40339903db7cf75e384939fd08e1a4debf96e1e4
路
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