To test the robustness of Sentence Transformer embeddings across domains, start by evaluating their performance on diverse datasets using domain-specific tasks. For example, if embeddings are intended for semantic search, measure their accuracy in retrieving relevant documents from datasets like medical journals, legal contracts, or social media posts. Use metrics such as recall@k or normalized discounted cumulative gain (NDCG) to quantify performance. If performance varies significantly (e.g., high accuracy on news articles but low on technical manuals), this indicates domain sensitivity. Additionally, test the embeddings on classification tasks (e.g., sentiment analysis) across domains to see if their representations generalize. For instance, train a classifier on embeddings from movie reviews and evaluate it on product reviews without fine-tuning the embeddings. A stable model should maintain consistent accuracy, while a drop suggests overfitting to the source domain.
Next, analyze embedding consistency using similarity metrics and statistical tests. Compute cosine similarity scores between pairs of sentences with known relationships (e.g., paraphrases or domain-specific synonyms) across datasets. For example, check if "bank" (financial institution) and "bank" (river edge) are correctly distinguished in finance versus environmental texts. Use dimensionality reduction techniques like UMAP to visualize clusters of embeddings from different domains—overlapping clusters for semantically similar content (e.g., "customer support" in both software and retail contexts) indicate robustness. Measure variance in embedding norms or pairwise distances within and across domains: unusually high variance in a domain (e.g., 0.8 variance in tech docs vs. 0.2 in emails) might signal instability. Tools like the Massive Text Embedding Benchmark (MTEB) provide standardized cross-domain evaluation, but you can extend this by adding custom datasets (e.g., domain-specific FAQs or jargon-heavy documents).
Finally, perform adversarial testing to expose weaknesses. Introduce domain-specific noise, such as typos (e.g., "patient" in medical texts), slang, or rare acronyms (e.g., "NLP" as "non-linear programming" in engineering contexts), and measure embedding drift using metrics like average cosine similarity between perturbed and original sentences. For example, if adding "LOL" to a legal sentence reduces similarity to its unperturbed version by 40%, the embeddings may lack lexical robustness. Test edge cases like extremely short or domain-mixed sentences (e.g., "PCI compliance for GPU clusters") to ensure embeddings don’t degrade. Compare results against baselines like Word2Vec or BERT—if Sentence Transformers show smaller performance gaps (e.g., 5% drop vs. 15% in baselines) across domains, they’re more stable. Regularly retest after model updates to catch regressions.