When fine-tuning a Sentence Transformer, key parameters include learning rate, batch size, number of epochs, optimizer settings, loss function choice, and temperature (for contrastive losses). These parameters directly influence training stability, convergence speed, and model performance. Below is a breakdown of their roles and impacts:
Learning Rate The learning rate determines how much the model’s weights are updated during each training step. A higher rate (e.g., 1e-4) can speed up convergence but risks overshooting optimal solutions or causing instability. A lower rate (e.g., 2e-5) improves stability but may require more epochs to converge. For Sentence Transformers, starting with a rate between 2e-5 and 5e-5 is common, often paired with a warmup period (gradually increasing the rate) to stabilize early training. Schedulers like linear decay can further refine convergence. For example, a rate too high might cause loss fluctuations, while a rate too low might result in slow progress or getting stuck in suboptimal embeddings.
Batch Size Batch size affects memory usage and gradient estimation. Larger batches (e.g., 32–64) provide more stable gradient estimates and improve hardware utilization but require more memory. Smaller batches (e.g., 8–16) can act as a regularizer, introducing noise that may prevent overfitting. For contrastive loss tasks (e.g., MultipleNegativesRankingLoss), larger batches implicitly include more negative examples, improving the model’s ability to distinguish between similar pairs. However, memory constraints often limit batch size, especially with long text sequences. Gradient accumulation (updating weights after multiple batches) can mimic larger batches when hardware is limited.
Number of Epochs Epochs define how many times the model processes the entire dataset. Too few epochs (e.g., 1–2) may leave the model underfit, while too many (e.g., 10+) can cause overfitting, especially with small datasets. Early stopping based on validation loss is a practical strategy. For example, training for 3–5 epochs is common for datasets with 10k–100k examples. Monitoring metrics like validation loss or retrieval accuracy helps determine the optimal stopping point. Overfitting manifests as a growing gap between training and validation performance, indicating the model is memorizing data rather than generalizing.
Additional Parameters
- Optimizer: AdamW is standard, with beta parameters (e.g., beta1=0.9, beta2=0.999) controlling momentum. Weight decay (e.g., 0.01) regularizes the model to prevent overfitting.
- Loss Function: Choices like CosineSimilarityLoss or Triplet Loss dictate how embeddings are structured. For instance, Triplet Loss requires careful mining of hard negatives.
- Temperature: In contrastive losses, this scales similarity scores. Lower temperatures sharpen distinctions between positive and negative pairs.
- Pooling: Methods like mean-pooling or CLS token pooling affect how sentence embeddings are derived from token outputs.
Balancing these parameters requires experimentation. Start with defaults (e.g., learning rate=2e-5, batch_size=16, epochs=3) and adjust based on validation performance. Tools like hyperparameter tuning libraries (Optuna) or gradient accumulation can help optimize these settings efficiently.