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
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README.md
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### Citing
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If you find our model helps, please consider citing this :)
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```
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@article{
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title={
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author={Li, Yikuan and Wehbe, Ramsey M and Ahmad, Faraz S and Wang, Hanyin and Luo, Yuan},
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journal={
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}
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```
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### Citing
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If you find our model helps, please consider citing this :)
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```
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@article{li2023comparative,
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title={A comparative study of pretrained language models for long clinical text},
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author={Li, Yikuan and Wehbe, Ramsey M and Ahmad, Faraz S and Wang, Hanyin and Luo, Yuan},
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journal={Journal of the American Medical Informatics Association},
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volume={30},
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number={2},
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pages={340--347},
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year={2023},
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publisher={Oxford University Press}
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}
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```
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