Instructions to use summaria/qa-t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use summaria/qa-t5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("summaria/qa-t5") model = AutoModelForSeq2SeqLM.from_pretrained("summaria/qa-t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1271baa1f9767608f4657b84174c795c321c627811945bf0c49607163b3c2d17
- Size of remote file:
- 892 MB
- SHA256:
- 36ff0d6e3c8423c8eab8f83be58cd9e0f416d17086db9d6cd39f599ea6efa963
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