When a Sentence Transformer fine-tuned on a niche dataset underperforms on general semantic similarity tasks, the primary cause is domain overfitting. The model adapts its embeddings to prioritize patterns specific to the niche data, losing the broader linguistic understanding it originally had. For example, a model trained on medical texts might learn to emphasize clinical terminology or rare acronyms, making it less effective at recognizing everyday phrases. The original model’s strength—generalizing across diverse contexts—is reduced because fine-tuning narrows its focus. This is similar to training an image classifier exclusively on cats and expecting it to recognize dogs; the model becomes overly specialized.
A second factor is changes to the training objective or loss function. Sentence Transformers are typically pre-trained using contrastive loss (e.g., MultipleNegativesRankingLoss) to maximize similarity between semantically related pairs and minimize it for unrelated ones. If your fine-tuning process altered the loss function or hyperparameters (e.g., margin values in triplet loss), the model might optimize for a different definition of similarity. For instance, using a triplet loss with a large margin could force embeddings to spread apart aggressively, disrupting the subtle relationships needed for general tasks. Similarly, if your niche dataset has imbalanced or noisy labels (e.g., weak similarity pairs mislabeled as strong matches), the model learns incorrect associations that don’t generalize.
Finally, reduced exposure to diverse linguistic structures during fine-tuning can degrade performance. Pre-trained models like SBERT are trained on vast, varied datasets (e.g., Wikipedia, forums, news), enabling them to handle nuances like synonyms, polysemy, and syntax. If your niche dataset lacks this diversity, the model’s ability to generalize diminishes. For example, a model fine-tuned on legal contracts might struggle with conversational phrases like “break a leg” (idiom) versus “break a bone” (literal). Additionally, if the niche data is small, the model may memorize examples instead of learning transferable features. To mitigate this, consider hybrid training: combine your niche data with a subset of general-purpose examples to preserve broader linguistic knowledge while adapting to the target domain.