When fine-tuning embedding models, the choice of loss function depends on the specific task and data structure. Three widely used approaches are triplet loss, contrastive loss, and softmax-based classification with temperature scaling. Each addresses different aspects of similarity learning, and their effectiveness hinges on how well they align with the training data and end goal. For example, triplet loss is ideal when relationships between data points can be defined as "anchor-positive-negative" groups, while contrastive loss works well with pairwise similarity labels. Softmax-based methods are effective when embeddings need to be optimized for downstream classification tasks.
Triplet loss trains the model to ensure that an anchor example (e.g., a product image) is closer to a positive example (a similar product) than to a negative example (a dissimilar product) by a predefined margin. For instance, in facial recognition, the anchor could be a photo of a person, the positive another photo of the same person, and the negative a photo of someone else. The margin determines how distinct the embeddings must be. A key challenge is selecting meaningful triplets: using "hard" negatives (samples that are superficially similar but belong to different classes) improves performance. Libraries like SentenceTransformers implement triplet loss with strategies for mining these hard negatives. Contrastive loss, on the other hand, operates on pairs of examples. It minimizes the distance between similar pairs (e.g., two paraphrased sentences) and maximizes it for dissimilar pairs (e.g., unrelated sentences). This works well when you have clear binary labels (similar/dissimilar) but requires careful balancing of positive and negative pairs to avoid bias. For example, in semantic text similarity, overloading the training data with too many trivial negative pairs (e.g., completely unrelated sentences) can lead to uninformative gradients.
Softmax loss with temperature scaling is often used when embeddings are tied to a classification task. Here, the model generates embeddings that are fed into a softmax layer to predict class labels. The temperature parameter sharpens or softens the probability distribution, influencing how the model distinguishes between classes. For example, in a recommendation system, a lower temperature forces the model to focus on clear distinctions between user preferences, while a higher temperature allows softer class boundaries. This method is particularly effective in frameworks like SBERT, where sentence embeddings are fine-tuned for tasks like semantic search by combining softmax loss with a temperature parameter. Multi-task losses (e.g., combining triplet loss with classification loss) can also be useful when embeddings must serve multiple purposes, such as retrieval and categorization.
In practice, the choice depends on data availability and computational constraints. Triplet loss requires constructing triplets, which can be resource-intensive, while contrastive loss needs high-quality pairwise labels. Softmax-based methods are simpler to implement if labeled classification data exists. For example, in e-commerce product embeddings, triplet loss could group similar items, while a softmax approach might classify products into categories. Always validate by measuring downstream task performance (e.g., retrieval accuracy) rather than just loss values, as the correlation between loss and real-world effectiveness can vary. Libraries like PyTorch Metric Learning offer modular implementations, allowing developers to experiment with these options efficiently.