Stereotype detection at aequa-tech

cite this work

@inproceedings{arthur2023debunker,
  title={Debunker Assistant: a support for detecting online misinformation},
  author={Arthur, Thomas Edward Capozzi Lupi and Cignarella, Alessandra Teresa and Frenda, Simona and Lai, Mirko and Stranisci, Marco Antonio and Urbinati, Alessandra and others},
  booktitle={Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023)},
  volume={3596},
  pages={1--5},
  year={2023},
  organization={Federico Boschetti, Gianluca E. Lebani, Bernardo Magnini, Nicole Novielli}
}

Model Description

• Developed by: aequa-tech
• Funded by: NGI-Search
• Language(s) (NLP): Italian
• License: apache-2.0
• Finetuned from model: AlBERTo

Training Details

Training Data

• HaSpeeDe 2020
• Sarcastic Hate Speech dataset
• Racial stereotypes corpus available upon request to the authors of A Multilingual Dataset of Racial Stereotypes in Social Media Conversational Threads
• Debunker-Assistant corpus

Training Hyperparameters

• learning_rate: 2e-5
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam

Evaluation

Testing Data

Metrics and Results

• macro F1: 0.75
• accuracy: 0.75
• precision of positive class: 0.66
• recall of positive class: 0.94
• F1 of positive class: 0.78

• macro F1: 0.72
• accuracy: 0.77
• precision of positive class: 0.73
• recall of positive class: 0.52
• F1 of positive class: 0.61

Framework versions

• Transformers 4.30.2
• Pytorch 2.1.2
• Datasets 2.19.0
• Accelerate 0.30.0

How to use this model: