Instructions to use simonlevine/clinical-longformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simonlevine/clinical-longformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="simonlevine/clinical-longformer")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("simonlevine/clinical-longformer") model = AutoModelForMaskedLM.from_pretrained("simonlevine/clinical-longformer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 293fa1fa5c6ff0dccdbd4c924a33c8302aa9ff8dee5c3fcbbb3a0a291e306af0
- Size of remote file:
- 510 MB
- SHA256:
- aaf48c5141499d35d47dcb7a2734299aad7b9343f05e200c780d46aca2af63cd
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