To evaluate whether one Sentence Transformer model outperforms another for your use case, start by defining task-specific metrics that align with your application. For semantic similarity tasks, use cosine similarity between embeddings of related sentences and measure correlation with human judgments (e.g., using Pearson or Spearman correlation). For retrieval tasks, metrics like recall@k (how often the correct result appears in the top k matches) or mean average precision (MAP) quantify how well the model ranks relevant items. For classification or clustering, use accuracy, F1-score, or silhouette score to assess embedding quality. For example, if your goal is document clustering, a higher silhouette score indicates embeddings better preserve semantic groupings.
Next, leverage standardized benchmarks like the Semantic Textual Similarity (STS) dataset, which measures correlation between model-predicted similarity scores and human-annotated labels. The Massive Text Embedding Benchmark (MTEB) evaluates models across diverse tasks (classification, retrieval, clustering, etc.) and provides aggregate scores. However, these general benchmarks may not reflect your specific data or requirements. To address this, create a custom evaluation dataset mirroring your real-world data distribution and annotate ground-truth labels (e.g., pairs of related/unrelated sentences). For instance, if your use case involves matching user queries to product descriptions, test both models on a labeled dataset of query-product pairs and compare recall@10 scores.
Finally, assess practical considerations like inference speed, memory usage, and scalability. A model with marginally better accuracy but 10x slower inference may not be viable for real-time applications. Test both models on hardware matching your deployment environment and measure latency (e.g., milliseconds per embedding). Also, validate robustness by testing edge cases (e.g., typos, domain-specific jargon) to ensure performance consistency. For example, if your application processes medical texts, verify that the model handles abbreviations like "MRI" and "CT scan" correctly. Combining task-specific metrics, custom benchmarks, and practical constraints provides a comprehensive evaluation framework.