
## t2t-adeX-prompt

t2t-adeX-prompt is a text-to-text (**t2t**) adverse drug event (**ade**) e**x**traction model trained with **prompt**ing 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).

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",
}
```

{"language": "en", "license": "mit", "tags": ["adverse-drug-events", "twitter", "social-media-mining-for-health", "SMM4H"], "widget": [{"text": "Did the patient suffer from a side effect?: weird thing about paxil: feeling fully awake and energized and feeling completely tired and half-asleep at the same time", "example_title": "Adverse Drug Event Extraction"}]}
transformers pytorch t5 text2text-generation adverse-drug-events twitter social-media-mining-for-health SMM4H en license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #adverse-drug-events #twitter #social-media-mining-for-health #SMM4H #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


## 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 URL

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


## Usage




## 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 URL

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 ## Usage
TAGS
#transformers #pytorch #t5 #text2text-generation #adverse-drug-events #twitter #social-media-mining-for-health #SMM4H #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ## 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 URL

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 ## Usage
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #adverse-drug-events #twitter #social-media-mining-for-health #SMM4H #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
## 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 URL

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## Usage