We describe our participation in the tool competition in the scope of the 2nd International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on SETFIT, a framework for few-shot learning and sentence-BERT (SBERT), a variant of BERT for effective sentence embedding. We experimented with different settings, achieving the best performance by training and testing the SETFIT-based model on a subset of data with manually verified labels (Fl-micro =.8321 ). For the sake of the challenge, we evaluate the SETFIT model on the challenge test set, achieving Fl-micro =.7767.
Few-Shot Learning for Issue Report Classification
Giuseppe Colavito
;Filippo Lanubile;Nicole Novielli
2023-01-01
Abstract
We describe our participation in the tool competition in the scope of the 2nd International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on SETFIT, a framework for few-shot learning and sentence-BERT (SBERT), a variant of BERT for effective sentence embedding. We experimented with different settings, achieving the best performance by training and testing the SETFIT-based model on a subset of data with manually verified labels (Fl-micro =.8321 ). For the sake of the challenge, we evaluate the SETFIT model on the challenge test set, achieving Fl-micro =.7767.File in questo prodotto:
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