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gokceuludogan/
t2t-adeX-prompt

Name: t2t-adeX-prompt
Rating: 2.5 (52 reviews)

social-media-mining-for-health

SMM4H

mit

text-generation-inference

endpoints_compatible

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t2t-adeX-prompt

t2t-adeX-prompt is a text-to-text (t2t) adverse drug event (ade) extraction model trained with prompting 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.

The model utilizes the T5 model and its text-to-text formulation. The inputs are fed to the model with the prompt "Did the patient suffer from a side effect?" while the outputs use the template "Yes, the patient suffered from <ADE>".

Requirements

sentencepiece
transformers

Usage

python

from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("gokceuludogan/t2t-adeX-prompt")
model = AutoModelForSeq2SeqLM.from_pretrained("gokceuludogan/t2t-adeX-prompt")
predictor = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
predictor("Did the patient suffer from a side effect?: weird thing about paxil: feeling fully energized and feeling completely tired at the same time")

Citation

bibtex

@inproceedings{uludogan-gokce-yirmibesoglu-zeynep-2022-boun-tabi-smm4h22,
    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",
    author = "Uludo{\u{g}}an, G{\"{o}}k{\c{c}}e  and Yirmibe{\c{s}}o{\u{g}}lu, Zeynep",
    booktitle = "Proceedings of the Seventh Social Media Mining for Health ({\#}SMM4H) Workshop and Shared Task",
    year = "2022",
}