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--- |
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language: "en" |
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tags: |
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- fill-mask |
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--- |
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<span style="font-size:larger;">**Clinical-Longformer**</span> is a clinical knowledge enriched version of Longformer that was further pre-trained using MIMIC-III clinical notes. |
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### Pre-training |
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We initialized Clinical-Longformer from the pre-trained weights of the base version of Longformer. The pre-training process was distributed in parallel to 6 32GB Tesla V100 GPUs. FP16 precision was enabled to accelerate training. We pre-trained Clinical-Longformer for 200,000 steps with batch size of 6×3. The learning rates were 3e-5 for both models. The entire pre-training process took more than 2 weeks. |
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### Down-stream Tasks |
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Clinical-Longformer consistently out-perform ClinicalBERT across 10 baseline dataset for at least 2 percent. The dataset broadly cover NER, QA and text classification tasks. For more details, please refer to: |
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### Usage |
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Load the model directly from Transformers: |
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``` |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("yikuan8/Clinical-Longformer") |
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model = AutoModel.from_pretrained("yikuan8/Clinical-Longformer") |
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``` |
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If you find our implementation helps, please consider citing this :) |
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``` |
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@inproceedings{li2020comparison, |
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title={A comparison of pre-trained vision-and-language models for multimodal representation learning across medical images and reports}, |
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author={Li, Yikuan and Wang, Hanyin and Luo, Yuan}, |
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booktitle={2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, |
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pages={1999--2004}, |
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year={2020}, |
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organization={IEEE} |
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} |
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``` |
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### Questions |
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Please email yikuanli2018@u.northwestern.edu |
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