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--- |
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license: mit |
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language: en |
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tags: |
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- adverse-drug-events |
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- twitter |
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- social-media-mining-for-health |
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- SMM4H |
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widget: |
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- text: "assert ade: joints killing me now i have gone back up on the lamotrigine. sick of side effects. sick of meds. want my own self back. knackered today" |
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example_title: "ADE" |
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- text: "assert ade: bout to have a kick ass summer then it's time to get serious fer school #vyvanse #geekmode" |
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example_title: "noADE" |
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--- |
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## t2t-assert-ade-balanced |
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t2t-assert-ade-balanced is a text-to-text (**t2t**) adverse drug event (**ade**) detection model trained with over- and undersampled (balanced) English tweets reporting adverse drug events. It is trained as part of BOUN-TABI system for the Social Media Mining for Health (SMM4H) 2022 shared task. The system description paper has been accepted for publication in *Proceedings of the Seventh Social Media Mining for Health (#SMM4H) Workshop and Shared Task* and will be available soon. The source code has been released on GitHub at [https://github.com/gokceuludogan/boun-tabi-smm4h22](https://github.com/gokceuludogan/boun-tabi-smm4h22). |
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The model utilizes the T5 model and its text-to-text formulation. The inputs are fed to the model with the task prefix "assert ade:", followed with a sentence/tweet. In turn, the output "adverse event problem" or "healthy okay" is received. |
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## Requirements |
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``` |
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sentencepiece |
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transformers |
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``` |
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## Usage |
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```python |
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from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("yirmibesogluz/t2t-assert-ade-balanced") |
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model = AutoModelForSeq2SeqLM.from_pretrained("yirmibesogluz/t2t-assert-ade-balanced") |
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predictor = pipeline("text2text-generation", model=model, tokenizer=tokenizer) |
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predictor("assert ade: joints killing me now i have gone back up on the lamotrigine. sick of side effects. sick of meds. want my own self back. knackered today") |
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``` |
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## Citation |
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```bibtex |
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@inproceedings{uludogan-gokce-yirmibesoglu-zeynep-2022-boun-tabi-smm4h22, |
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title = "{BOUN}-{TABI}@{SMM4H}'22: Text-to-{T}ext {A}dverse {D}rug {E}vent {E}xtraction with {D}ata {B}alancing and {P}rompting", |
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author = "Uludo{\u{g}}an, G{\"{o}}k{\c{c}}e and Yirmibe{\c{s}}o{\u{g}}lu, Zeynep", |
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booktitle = "Proceedings of the Seventh Social Media Mining for Health ({\#}SMM4H) Workshop and Shared Task", |
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year = "2022", |
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} |
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``` |
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