Instructions to use QQhahaha/QuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QQhahaha/QuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="QQhahaha/QuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("QQhahaha/QuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("QQhahaha/QuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f53401d275a650930f42e48c87b9a6107c6ee342273134f4391f9ab2a556aa52
- Size of remote file:
- 1.3 GB
- SHA256:
- 1e7214f9f1426ccb8ba65d2419d987c310a8645beeb6abbf2069fafa89873cfb
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