SageLite: Harmonizing Text and Code Through Multi-Stage Training

Authors: Dejiao Zhang, Sam Mayers, Jun Wang, Sanjay Krishna Gouda, Nihal Jain, Jiyang Zhang, Xiaofei Ma, Anoop Deoras
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: unified embedding for text and code, unsupervised learning, multi-stage training

Abstract:
Creating versatile embedding models that excel across both text and code domains is essential, as modern applications often involve diverse, heterogeneous data. While data mixing is a typical starting point, we take a significant step forward by addressing the limitations of naive data mixing. In this work, we introduce SageLite, a unified embedding model capable of handling both text and code within a single framework. Our approach begins with pretraining on a blended dataset of text and code, fostering shared representations that are crucial for strong cross-domain performance. We then enhance domain-specific capabilities by independently applying large-scale contrastive learning to text and code from various web sources. Our key finding is that, despite the inherent differences between text and code, starting from a model pretrained on mixed data enables the domain-specific contrastive learning stages to produce models that remain closely aligned. This alignment allows us to effectively integrate domain-specific improvements at the constrastive learning stage into a final model through model weights interpolation. Through comprehensive ablation studies, we explore the mechanisms behind our approach, offering insights to guide future research in this area.

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