Metric Learning Tips & Tricks

Metric learning offers a flexible and scalable alternative to traditional classification models, particularly in scenarios where class labels are unavailable or impractical, such as matching job positions with candidates. Unlike classification, which requires a fixed number of classes and substantial labeled data, metric learning focuses on determining the similarity between objects using distance functions. This approach allows for the comparison of pairs of objects, facilitating tasks like matching job descriptions without needing a predefined set of categories. The article discusses two main approaches to metric learning: interaction-based and representation-based, with the latter being highlighted for its efficiency and flexibility. Representation-based models employ an encoder to transform objects into embeddings and a comparator to measure similarity, enabling operations such as adding new references without retraining the model. Through the use of techniques like hard negative mining and adjustments to loss functions, the article illustrates how metric learning can be effectively trained even with limited data. Additionally, a novel method for estimating model confidence is proposed, leveraging modifications to the embedding generator to mimic the confidence estimation seen in classification models. The article concludes by introducing Qdrant, an open-source vector search engine designed to manage and query embeddings efficiently, enhancing the deployment of metric learning models in production settings.