Training objectives like contrastive learning and triplet loss guide Sentence Transformers to produce meaningful sentence embeddings by structuring the embedding space. These methods focus on relative distances between embeddings rather than absolute predictions, ensuring semantically similar sentences are closer while dissimilar ones are farther apart. For example, contrastive learning compares pairs of sentences, pushing similar pairs closer and dissimilar pairs apart. Triplet loss uses triplets (anchor, positive, negative) to enforce that the anchor is closer to the positive example than to the negative by a predefined margin. Both approaches directly optimize the model’s ability to capture semantic relationships, which is critical for tasks like retrieval or clustering.
In Sentence Transformers, these objectives are applied during fine-tuning. The model (e.g., BERT) generates embeddings for input sentences, and the loss function adjusts the model’s weights based on the similarity structure. For contrastive learning, a common implementation is the MultipleNegativesRankingLoss, where the model is trained to distinguish a correct positive pair from random negatives within the same batch. For triplet loss, the model processes triplets where the anchor and positive share meaning (e.g., "programming" and "coding"), while the negative is unrelated (e.g., "hiking"). The loss penalizes the model if the anchor-positive distance isn’t smaller than the anchor-negative distance by at least a margin (e.g., 0.5). This forces the encoder to refine embeddings for better semantic separation.
A practical example of contrastive learning is training on question-answer pairs: the model learns to embed a question and its correct answer closer than incorrect answers. For triplet loss, consider a dataset with product descriptions: an anchor product, a similar product (positive), and a dissimilar one (negative). The model ensures embeddings for "wireless headphones" are closer to "Bluetooth earbuds" than to "running shoes." Implementation-wise, frameworks like Sentence Transformers simplify this by handling negative sampling automatically (e.g., using in-batch negatives) or providing utilities for triplet mining. Challenges include selecting hard negatives (samples that are confusing but still irrelevant) and balancing the margin to avoid overfitting. These techniques make Sentence Transformers effective for tasks requiring semantic understanding without relying on labeled classification data.