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:
- 2ca1349c37d1f2c322cdb4e1d15d1d4700b5b74d945174b2b2748b4a35420f86
- Size of remote file:
- 2.14 MB
- SHA256:
- d403d676c4fad1a94d2a5633be563af60dfb9f1a3d70f5bc4670fc1f7a9f9431
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