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This paper demonstrates how the limitations of pre-trained models and openevaluation datasets factor into assessing the performance of binary semanticsimilarity classification tasks. As (1) end-user-facing documentation aroundthe curation of these datasets and pre-trained model training regimes is oftennot easily accessible and (2) given the lower friction and higher demand toquickly deploy such systems in real-world contexts, our study reinforces priorwork showing performance disparities across datasets, embedding techniques anddistance metrics, while highlighting the importance of understanding how datais collected, curated and analyzed in semantic similarity classification.

Contextualizing the Limits of Model & Evaluation Dataset Curation on Semantic Similarity Classification Tasks