readme: ---
  language: "en"
  tags:
  - longformer
  - clinical
  
  ---
  
  <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. It allows up to 4,096 tokens as the model input. Clinical-Longformer consistently out-performs ClinicalBERT across 10 baseline dataset for at least 2 percent. Those downstream experiments broadly cover named entity recognition (NER), question answering (QA), natural language inference (NLI) and text classification tasks. For more details, please refer to [our paper](https://arxiv.org/pdf/2201.11838.pdf). We also provide a sister model at [Clinical-BigBIrd](https://huggingface.co/yikuan8/Clinical-BigBird)
  
  
  ### Pre-training
  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. 
  
  
  ### Usage
  Load the model directly from Transformers:
  ```
  from transformers import AutoTokenizer, AutoModelForMaskedLM
  tokenizer = AutoTokenizer.from_pretrained("yikuan8/Clinical-Longformer")
  model = AutoModelForMaskedLM.from_pretrained("yikuan8/Clinical-Longformer")
  ```
  ### Citing
  If you find our model helps, please consider citing this :)
  ```
  @article{li2023comparative,
    title={A comparative study of pretrained language models for long clinical text},
    author={Li, Yikuan and Wehbe, Ramsey M and Ahmad, Faraz S and Wang, Hanyin and Luo, Yuan},
    journal={Journal of the American Medical Informatics Association},
    volume={30},
    number={2},
    pages={340--347},
    year={2023},
    publisher={Oxford University Press}
  }
  ```
  
  ### Questions
  Please email yikuanli2018@u.northwestern.edu
  
  
  
  