If your Sentence Transformer model overfits quickly during fine-tuning (e.g., training loss drops sharply while validation loss stagnates or rises), focus on reducing model complexity, improving regularization, and adjusting training dynamics. Here’s a structured approach:
1. Address Data Limitations and Augmentation Small datasets are prone to overfitting. If you can’t gather more data, use text-specific augmentation techniques like synonym replacement, back-translation, or controlled paraphrasing. For example, replace 10-20% of words in a sentence with synonyms while preserving meaning. If your task involves paired examples (e.g., similarity), ensure augmentations maintain the original label relationships. For instance, if two sentences are semantically equivalent, their augmented versions should still be labeled as similar. This artificially expands the training distribution, helping the model generalize.
2. Apply Regularization and Training Adjustments
- Increase Dropout: Sentence Transformers have built-in dropout layers (default
0.1). Raise this to0.3or0.5viamodel[0].auto_model.config.hidden_dropout_prob = 0.3(forbert-basearchitectures). - Weight Decay: Add L2 regularization via the optimizer. For AdamW, use
weight_decay=0.01. - Reduce Learning Rate: Lower the initial LR (e.g., from
2e-5to5e-6) and add a warmup phase (10% of training steps) to prevent aggressive early updates. - Early Stopping: Halt training if validation loss doesn’t improve for 2-3 epochs. Use libraries like
pytorch_lightningfor automated tracking.
3. Simplify the Model and Training Objective
- Freeze Layers: For small datasets, freeze lower transformer layers and train only the top 2-4 layers and pooling head. This limits capacity.
- Reduce Embedding Size: Use
model[1].word_embedding_dimension = 256to shrink output dimensions. - Batch Size and Negatives: For contrastive loss, increase the number of hard negatives per anchor to make the task harder. For example, mine 8 negatives per anchor instead of 4.
Example Workflow
If training on 5k examples with all-mpnet-base-v2, start by freezing all layers except the last 2. Use a batch size of 16, dropout=0.4, weight decay=0.01, and LR=3e-6 with 10% warmup. Add back-translation augmentation (e.g., translate sentences to French and back to English). Monitor validation loss and stop after 2 epochs without improvement.
By balancing model capacity, data diversity, and training stability, you can mitigate overfitting while retaining the benefits of fine-tuning.