To test the robustness of Sentence Transformer embeddings across domains, start by evaluating their performance on diverse, domain-specific datasets. For example, if a model was trained on general text (e.g., Wikipedia), test it on medical data (e.g., MIMIC-III clinical notes), legal documents (e.g., COLIEE case law), or technical forums (e.g., StackExchange). Measure consistency using tasks like semantic similarity, clustering, or classification. Use standardized benchmarks like STS Benchmark (general text), BIOSSES (biomedical text), or custom datasets from target domains. Compute metrics like cosine similarity scores for semantic tasks, or accuracy/F1-score for classification. If performance drops significantly in specific domains (e.g., embeddings fail to distinguish “patient” in medical vs. “client” in business contexts), this signals domain sensitivity.
Next, perform adversarial testing by introducing perturbations to input text. For instance, add typos (“teh” instead of “the”), swap synonyms (“car” vs. “automobile”), or alter word order. Compare embedding distances (e.g., using Euclidean or cosine similarity) between original and modified sentences—small changes should not drastically shift embeddings. Test edge cases like domain-specific jargon (e.g., “PCI” meaning “payment card industry” in finance vs. “peripheral component interconnect” in tech). Use tools like TextAttack to automate this. Additionally, vary input lengths (short phrases vs. paragraphs) and noise levels (e.g., HTML tags in scraped data). Track how metrics like average pairwise similarity across perturbed samples deviate from baseline performance.
Finally, validate through fine-tuning and ablation studies. Fine-tune the model on a small sample from a target domain (e.g., 1,000 legal sentences) and compare pre- and post-tuning performance on domain-specific tasks. If accuracy improves sharply, the original embeddings were suboptimal for that domain. Conduct ablation by removing layers (e.g., pooling heads) or disabling components (e.g., token type embeddings) to identify critical parts of the model. For example, test if removing position embeddings harms performance on structured data (e.g., code snippets). Use cross-validation with datasets like SNLI for natural language inference or Amazon Reviews for sentiment analysis. Track performance consistency across multiple runs to rule out randomness. Tools like Weights & Biases or TensorBoard can help visualize embedding drift over training iterations or domains.