Instructions to use yikuan8/Clinical-Longformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yikuan8/Clinical-Longformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="yikuan8/Clinical-Longformer")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("yikuan8/Clinical-Longformer") model = AutoModelForMaskedLM.from_pretrained("yikuan8/Clinical-Longformer") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5c4f9f5726cbd6852a6def62a6c631efad0e723ba51abebef9fd403467d44a1
- Size of remote file:
- 595 MB
- SHA256:
- 87e239f555459c3668993fbf5b53bea93f503be86f2f4285f431fdb2c6c33c75
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