To achieve better accuracy when fine-tuning Sentence Transformers, focus on these key practices:
1. Data Preparation and Augmentation High-quality, task-specific data is critical. Ensure your dataset closely mirrors the target task’s requirements. For tasks involving similarity comparisons (e.g., retrieval), include hard negatives—samples that are semantically close but not true matches—to help the model distinguish fine-grained differences. For example, in a FAQ-matching task, hard negatives could be questions with overlapping keywords but different intents. Data augmentation techniques like synonym replacement or back-translation can diversify limited datasets. If labeled pairs are scarce, consider generating synthetic pairs using domain-specific rules or leveraging unsupervised methods like SimCSE to create contrastive examples.
2. Loss Function and Model Selection
Choose a loss function aligned with your task. For sentence pairs (e.g., duplicate detection), use ContrastiveLoss or CosineSimilarityLoss to optimize embedding similarity. For triplets (anchor, positive, negative), TripletLoss is effective. For classification tasks, SoftmaxLoss with label embeddings works well. Select a base model pretrained on data similar to your domain—for example, all-mpnet-base-v2 for general purposes or BioBERT for biomedical texts. If your task involves short texts (e.g., tweets), models trained on conversational data may perform better. Experiment with pooling strategies (mean, max, or CLS token) if the default approach underperforms.
3. Hyperparameter Tuning and Regularization Use a lower learning rate (e.g., 2e-5) than pretraining to avoid overwriting useful weights. Larger batch sizes (e.g., 32–64) improve contrastive learning by providing more in-batch negatives. Implement early stopping based on a validation metric (e.g., accuracy on a held-out set). For regularization, apply dropout (e.g., 0.1–0.3) and weight decay (e.g., 0.01) to prevent overfitting. Adjust the temperature parameter in contrastive loss to control similarity scoring—higher values soften the distribution, aiding in harder tasks. Use mixed-precision training and gradient checkpointing to manage memory and scale batch sizes. Monitor embeddings via tools like TensorBoard or UMAP to ensure they capture meaningful clusters.
By systematically addressing data quality, loss function alignment, and hyperparameter tuning, you can significantly enhance the model’s performance on your specific task while avoiding common pitfalls like overfitting or poor generalization.