pytorch longformer fill-mask en arxiv:2201.11838 transformers clinical autotrain_compatible
---
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{li2022clinical,
  title={Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences},
  author={Li, Yikuan and Wehbe, Ramsey M and Ahmad, Faraz S and Wang, Hanyin and Luo, Yuan},
  journal={arXiv preprint arXiv:2201.11838},
  year={2022}
}
```

### Questions
Please email yikuanli2018@u.northwestern.edu



