To fine-tune a pre-trained Sentence Transformer model for a custom task or domain, start by preparing your dataset and selecting an appropriate training objective. Sentence Transformers excel at learning semantic similarities, so your dataset should include pairs or triplets of texts labeled to reflect their relationships. For example, if you’re training for semantic search, create pairs of queries and relevant documents labeled as "similar," or triplets with an anchor, a positive example (related text), and a negative example (unrelated text). Use the InputExample class from the sentence_transformers library to structure your data, ensuring compatibility with the training pipeline. If your task requires domain-specific terminology (e.g., medical or legal jargon), ensure your dataset sufficiently represents these terms to help the model adapt.
Next, configure the training setup by initializing the pre-trained model and selecting a loss function. Load a base model like all-mpnet-base-v2 using SentenceTransformer(), which provides a strong starting point for most tasks. Choose a loss function aligned with your data structure: ContrastiveLoss for pairs of similar/dissimilar texts, TripletLoss for anchor-positive-negative triplets, or MultipleNegativesRankingLoss for tasks like retrieval where negatives are inferred from the batch. Wrap your data in a DataLoader and use train_dataloader to feed batches into the training loop. Set hyperparameters like a small learning rate (e.g., 2e-5) to avoid overwriting the pre-trained knowledge, a batch size of 16–32 (adjust based on GPU memory), and 3–10 epochs depending on dataset size. Enable gradient checkpointing if memory is constrained.
Finally, run the training loop and validate performance. Use the fit() method with your model, dataloader, and evaluator objects. For validation, create an evaluator like EmbeddingSimilarityEvaluator to measure correlation between predicted and ground-truth similarity scores on a held-out dataset. Monitor training loss and validation metrics to detect overfitting; if performance plateaus, consider increasing dataset diversity or adjusting the learning rate. After training, save the model with save_to_path() and test it on unseen examples to verify improvements in your task. If results are suboptimal, experiment with different loss functions, data augmentation (e.g., back-translation), or model architectures (e.g., adding a dense layer). Iterate until the model reliably captures domain-specific semantics.