When fine-tuning a Sentence Transformer model, key parameters include the learning rate, batch size, number of epochs, optimizer settings, loss function choice, and warmup steps. These parameters directly influence training stability, convergence speed, and final model performance. Adjusting them requires balancing computational resources, avoiding overfitting, and ensuring the model learns meaningful text representations. Below, we break down their roles and practical considerations.
The learning rate determines how much the model updates its weights during training. A rate too high (e.g., 1e-3) may cause unstable updates, leading to divergence or poor performance, while a rate too low (e.g., 1e-6) can slow convergence. A typical starting range is 1e-5 to 1e-4. The optimizer (e.g., AdamW) and weight decay (e.g., 0.01) also matter: weight decay regularizes the model to prevent overfitting. Warmup steps gradually increase the learning rate early in training to stabilize initial updates, especially useful for pretrained models. For example, a warmup over 10% of training steps helps avoid abrupt weight changes.
Batch size affects memory usage and gradient accuracy. Larger batches (e.g., 64-128) provide smoother gradient estimates but require more GPU memory. Smaller batches (e.g., 16-32) introduce noise, which can help generalization but may slow convergence. The number of epochs determines how often the model revisits the training data. Too few epochs (e.g., 1-2) risk underfitting, while too many (e.g., 20+) may overfit, especially on small datasets. For tasks like semantic similarity, 3-10 epochs are common. Adjusting epochs depends on dataset size: larger datasets may need fewer epochs due to more diverse examples per batch.
Other parameters include loss function selection (e.g., contrastive loss, triplet loss), which shapes how the model learns embeddings. For instance, triplet loss trains the model to distinguish between anchor, positive, and negative examples, while contrastive loss focuses on pairing similar examples. Evaluation frequency (e.g., every 500 steps) helps monitor validation performance to detect overfitting early. Gradient clipping (e.g., clipping norms to 1.0) prevents exploding gradients in unstable training scenarios. Developers should experiment with these settings based on their specific data and hardware constraints, validating changes through iterative testing.