To fine-tune a Sentence Transformer using triplet or contrastive loss, follow these steps: prepare data in the required format, configure the model and loss function, and train with evaluation. Triplet loss uses anchor-positive-negative text triplets, while contrastive loss operates on pairs labeled as similar or dissimilar. Both approaches train the model to create embeddings where semantically similar texts are closer in vector space than dissimilar ones. This process requires a pre-trained Sentence Transformer, a dataset tailored to your task, and a training loop optimized for the chosen loss function.
First, prepare your dataset based on the loss function. For triplet loss, each training example must include an anchor text (e.g., a search query), a positive text (a relevant result), and a negative text (an irrelevant result). For example, in a product recommendation system, the anchor could be “wireless headphones,” the positive could be a product description of Bluetooth earphones, and the negative could be a description of wired earbuds. For contrastive loss, use pairs of texts labeled as similar (1) or dissimilar (0). For instance, in a duplicate question detection task, similar pairs might include “How to reset a password?” and “Forgot my login credentials,” while dissimilar pairs could pair “Best hiking trails” with “How to bake a cake.” Tools like the datasets library in Python help structure these examples, and hard negative mining (selecting challenging negatives) is often critical for triplet loss performance.
Next, set up the model and loss function. Initialize a Sentence Transformer (e.g., all-MiniLM-L6-v2) using the sentence-transformers library. For triplet loss, use TripletLoss with a margin hyperparameter (e.g., 0.5) to enforce a separation threshold between positive and negative embeddings. For contrastive loss, apply ContrastiveLoss, which calculates similarity scores between pairs and penalizes mismatches. Configure the training data loader—batch size impacts triplet loss performance, as larger batches allow more triplets to be mined. For example, a batch size of 16 with triplet loss might generate 48 text entries (16 anchors, 16 positives, 16 negatives). Use an optimizer like AdamW with a learning rate (e.g., 2e-5) and warm-up steps to stabilize training. Optionally, add evaluation metrics like cosine similarity accuracy on a validation set.
Finally, train and evaluate the model. Run the training loop for several epochs (3-5 is common) and monitor loss convergence. For evaluation, use tasks like semantic textual similarity (STS) benchmarks or custom retrieval tests. For example, after training on a FAQ dataset, test if the model retrieves correct answers for unseen queries. Adjust hyperparameters (margin, learning rate) if performance plateaus. Save the fine-tuned model and deploy it for inference. Key considerations include balancing dataset quality (avoiding noisy labels) and computational efficiency—triplet loss can be slower due to triplet mining. Both approaches work best when domain-specific data aligns closely with the target application.