Instructions to use arjunth2001/priv_qna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arjunth2001/priv_qna with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="arjunth2001/priv_qna")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("arjunth2001/priv_qna") model = AutoModelForQuestionAnswering.from_pretrained("arjunth2001/priv_qna", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4af847b3a5876daaf56f1f34e080c41f2a9dc85de75bd3eac192d191270f9dae
- Size of remote file:
- 496 MB
- SHA256:
- c8c3def59de6db237cc574ebd7c452bdef2d544177e2dd07264bff8b1e09c787
路
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