To optimize fine-tuning hyperparameters for Sentence Transformers, focus on learning rate schedules, layer freezing, and complementary adjustments like batch size and evaluation. Here’s a structured approach:
1. Learning Rate Scheduling
Start with a low initial learning rate (e.g., 2e-5 to 5e-5) since the base model is pretrained and drastic updates could destabilize learned features. Use a warm-up phase (e.g., 10% of total steps) to gradually increase the learning rate, avoiding early instability. After warm-up, apply a decay schedule like linear or cosine annealing to reduce the rate, balancing exploration and convergence. For example, cosine annealing cycles between a base rate (e.g., 2e-5) and a lower bound (e.g., 1e-6), promoting smoother convergence. Avoid fixed rates, as they risk overshooting optimal minima or slowing progress. Tools like Hugging Face’s Trainer or PyTorch’s OneCycleLR simplify implementation of these schedules.
2. Layer Freezing Strategies Freeze the majority of the base model’s layers (e.g., BERT’s first 6–8 layers) during initial training to reduce computation and prevent overfitting on small datasets. For example, freeze all layers except the final transformer block and the pooling layer. After a few epochs, unfreeze layers incrementally (e.g., one layer per epoch) to fine-tune deeper features. This phased approach allows the model to adapt to new data without losing generalizable features. However, avoid freezing too many layers if your task diverges significantly from the pretraining objective (e.g., domain-specific jargon)—in such cases, unfreeze all layers but use a smaller learning rate (e.g., 1e-5) for earlier layers to balance adaptation and stability.
3. Complementary Adjustments
Use larger batch sizes (e.g., 32–64) to stabilize gradient estimates, but scale the learning rate linearly with batch size (e.g., double the batch size → double the learning rate). If memory constraints exist, employ gradient accumulation. Pair the AdamW optimizer with weight decay (e.g., 0.01) to regularize weights without interfering with the learning rate. Monitor validation loss (e.g., using a held-out set of sentence pairs) to detect overfitting or stagnation, and apply early stopping if performance plateaus. For example, stop training if validation loss doesn’t improve for 3 epochs. These steps, combined with task-specific loss functions (e.g., MultipleNegativesRankingLoss for retrieval tasks), ensure efficient convergence while maintaining performance. Always validate hyperparameters on a small subset of data before full training to save time and resources.