Instructions to use vachevkd/qna-t5base-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vachevkd/qna-t5base-squad with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vachevkd/qna-t5base-squad") model = AutoModelForSeq2SeqLM.from_pretrained("vachevkd/qna-t5base-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65636e45bb3762e8e7bf2270fb6e1cf739f5fab5a8b9cb256ded1c314e35303e
- Size of remote file:
- 892 MB
- SHA256:
- 7cfad7a25b26f1e8970a9d0b8386bcd235636da7f8a0f31308d3a2448199c2d7
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