To recognize if a Sentence Transformer model is underfitting or overfitting during fine-tuning, monitor its performance on both training and validation data. Underfitting occurs when the model performs poorly on both sets, indicating it fails to learn meaningful patterns. For example, if the training loss plateaus at a high value early in training and validation metrics (e.g., accuracy, cosine similarity) remain low, the model isn’t capturing the task’s complexity. This might happen if the learning rate is too low, the training data is insufficient, or the model architecture (e.g., pooling layers) is mismatched to the task. For instance, if fine-tuning for semantic similarity but the model’s embeddings show no correlation with human-labeled similarity scores, underfitting is likely.
Overfitting is identified when the model excels on training data but performs poorly on validation data. A clear sign is a steady decrease in training loss alongside a stagnant or increasing validation loss. For example, in a classification task using embeddings, high training accuracy but low validation accuracy suggests the model memorizes training examples instead of generalizing. Overfitting often arises from limited training data, excessive model complexity (e.g., unfreezing too many layers), or inadequate regularization. If the model achieves near-perfect training metrics but embeddings for unseen sentences cluster incorrectly, overfitting is likely.
Addressing underfitting involves increasing model capacity or improving training dynamics. Start by adjusting hyperparameters: raise the learning rate to enable meaningful weight updates or train for more epochs. If the dataset is small, augment it with paraphrasing, back-translation, or synonym replacement. Switch to a larger pre-trained model (e.g., from all-mpnet-base-v2 to all-roberta-large) if the task requires deeper semantic understanding. Ensure the task-specific components (e.g., pooling layers or classifiers) align with the objective—for example, using mean pooling instead of CLS token pooling for sentence embeddings. If labels are noisy, clean the data or use robust loss functions.
To combat overfitting, apply regularization techniques. Introduce dropout in the transformer layers or add weight decay (e.g., L2 regularization) to the optimizer. Implement early stopping by halting training when validation metrics plateau. Reduce model complexity by freezing more layers (e.g., only fine-tuning the top 2-4 transformer layers) or using a smaller pre-trained model. Improve data diversity by adding more examples or balancing class distributions. For contrastive learning tasks, ensure hard negatives are included in triplet loss to force the model to learn discriminative features. Lastly, validate data quality—mislabeled examples or irrelevant training pairs can mislead the model. For example, in a retrieval task, if the model retrieves training duplicates perfectly but fails on new queries, revisit sampling strategies and data preprocessing.