Instructions to use griffin/clinical-summary-fact-corrector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use griffin/clinical-summary-fact-corrector with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("griffin/clinical-summary-fact-corrector") model = AutoModelForSeq2SeqLM.from_pretrained("griffin/clinical-summary-fact-corrector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3091ab6fbd0899e8957006a682f42c644ec4a03b90b512abf6ebe36ffb8defa5
- Size of remote file:
- 558 MB
- SHA256:
- b737b55dd50d9fbdc95817499f4168a1cc94ddeb82bb8aa36b35c4938ffcc029
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