Fine-tuning a Sentence Transformer model can sometimes lead to worse performance than the original pre-trained model due to three primary factors: data mismatch, training configuration issues, and overfitting or underfitting. Let’s break these down with practical examples.
First, data mismatch occurs when your fine-tuning dataset doesn’t align well with the original model’s training data or your target task. For example, if the pre-trained model was trained on general-purpose text (e.g., Wikipedia, books) but you fine-tune it on a narrow domain (e.g., medical jargon), the model may lose its ability to generalize. Worse, if your fine-tuning data is noisy, mislabeled, or lacks sufficient examples of key relationships (e.g., pairs of semantically similar sentences), the model might learn incorrect patterns. Imagine training a model to recognize legal document similarity but using a dataset where most "positive" pairs are just paraphrases of the same sentence—this could weaken its ability to handle real-world legal text variations.
Second, training configuration issues often play a role. For instance, using an inappropriate learning rate can destabilize training. If the learning rate is too high, the model may overshoot optimal parameter values and degrade performance. Conversely, a learning rate that’s too low might fail to adapt the model to your task. Additionally, incorrect loss function choices (e.g., using MultipleNegativesRankingLoss without hard negatives) or improper batch sizes can lead to suboptimal updates. For example, a small batch size might prevent the contrastive loss from effectively distinguishing between positive and negative examples, especially if your dataset lacks diversity. Developers often overlook the need to validate hyperparameters (via grid search or small-scale experiments) before full fine-tuning.
Third, overfitting or underfitting can sabotage results. Overfitting happens when the model memorizes noise or specifics of the training data, reducing its ability to generalize. This is common with small datasets or excessive training epochs. For example, fine-tuning for 50 epochs on a 1,000-sentence dataset might cause the model to "forget" the original embeddings’ robustness. Underfitting, on the other hand, occurs when the model isn’t trained enough or lacks capacity to learn the task. For example, freezing all layers except the final dense layer might prevent the model from adapting sentence embeddings to your task. A balanced approach—like freezing early layers, using dropout, or applying early stopping—is critical.
In summary, poor performance after fine-tuning often stems from mismatched data, suboptimal training configurations, or improper regularization. To mitigate this, validate your dataset’s quality, tune hyperparameters rigorously, and monitor training dynamics (e.g., loss curves, validation metrics) to catch issues early.