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  license: mit
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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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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+
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+ widget:
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+ - text: "ner ade: i'm so irritable when my vyvanse wears off"
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+ example_title: "ADE"
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+ - text: "ner 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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+
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+ ## t2t-ner-ade-balanced
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+
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+ t2t-ner-ade-balanced is a text-to-text (**t2t**) adverse drug event (**ade**) extraction (NER) 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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+
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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 "ner ade:", followed with a sentence/tweet. In turn, either the extracted adverse event span is returned, or "none".
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+
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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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+
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+ ## Usage
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+
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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-ner-ade-balanced")
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+ model = AutoModelForSeq2SeqLM.from_pretrained("yirmibesogluz/t2t-ner-ade-balanced")
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+ predictor = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
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+ predictor("ner ade: i'm so irritable when my vyvanse wears off")
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+ ```
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+
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+ ## Citation
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+
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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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+ ```