While BERT produces high-quality sentence embeddings, its pre-training computational cost is a significant drawback. In contrast, ELECTRA delivers a cost-effective pre-training objective and downstream task performance improvements, but not as performant sentence embeddings. The community tacitly stopped utilizing ELECTRA's sentence embeddings for semantic textual similarity (STS). We notice a significant drop in performance when using the ELECTRA discriminator's last layer in comparison to earlier layers. We explore this drop and devise a way to repair ELECTRA's embeddings, proposing a novel truncated model fine-tuning (TMFT) method. TMFT improves the Spearman correlation coefficient by over 8 points while increasing parameter efficiency on the STS benchmark dataset. We extend our analysis to various model sizes and languages. Further, we discover the surprising efficacy of ELECTRA's generator model, which performs on par with BERT, using significantly fewer parameters and a substantially smaller embedding size. Finally, we observe further boosts by combining TMFT with a word similarity task or domain adaptive pre-training.
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