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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da59978fc7f067ad52bbfd8e860331e58d2bae3b6acf339023c05afe45fc2c71
- Size of remote file:
- 595 MB
- SHA256:
- c109bcad9734a188a0c1319873d83eb86062fb43f727537acdc77798c6ca5b79
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