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README.md

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 # T-FREX RoBERTa base model
 
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+Please cite this research as:
+_Q. Motger, A. Miaschi, F. Dell’Orletta, X. Franch, and J. Marco, ‘T-FREX: A Transformer-based Feature Extraction Method from Mobile App Reviews’, in Proceedings of The IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2024. Pre-print available at: https://arxiv.org/abs/2401.03833_
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 T-FREX is a transformer-based feature extraction method for mobile app reviews based on fine-tuning Large Language Models (LLMs) for a named entity recognition task. We collect a dataset of ground truth features from users in a real crowdsourced software recommendation platform, and we use this dataset to fine-tune multiple LLMs under different data configurations. We assess the performance of T-FREX with respect to this ground truth, and we complement our analysis by comparing T-FREX with a baseline method from the field. Finally, we assess the quality of new features predicted by T-FREX through an external human evaluation. Results show that T-FREX outperforms on average the traditional syntactic-based method, especially when discovering new features from a domain for which the model has been fine-tuned.
 
 Source code for data generation, fine-tuning and model inference are available in the original [GitHub repository](https://github.com/gessi-chatbots/t-frex/).