To evaluate if fine-tuning improved your embedding model, you need to compare its performance before and after fine-tuning using both task-specific metrics and intrinsic quality checks. Start by defining clear evaluation criteria tied to your use case, then measure changes in downstream task performance, embedding quality, and real-world applicability. Focus on consistent test datasets, controlled comparisons, and practical scenarios to avoid misleading conclusions.
First, measure task-specific performance on downstream applications. If your embedding model is used for search, classification, or clustering, test it on those tasks using the same benchmark datasets before and after fine-tuning. For example, if you’re using embeddings for document retrieval, calculate metrics like recall@k (how often the correct result appears in the top k matches) or mean reciprocal rank (MRR). Suppose your pre-fine-tuning model achieved 72% recall@10 on a tech support article dataset—if the fine-tuned version reaches 85% with the same test queries, that’s a clear improvement. For classification tasks, compare accuracy or F1-scores using a fixed validation set. Ensure test data isn’t leaked into training to avoid inflated results.
Second, evaluate intrinsic embedding quality using semantic similarity benchmarks and embedding space analysis. Use standardized datasets like the Semantic Textual Similarity (STS) benchmark, where human-annotated sentence pairs are scored for similarity. Calculate the correlation (e.g., Spearman’s rank) between your model’s cosine similarity scores and human judgments. If pre-fine-tuning scores averaged 0.65 correlation and post-fine-tuning reach 0.78, that suggests better alignment with human intuition. Additionally, analyze the embedding space for properties like alignment (similar concepts cluster together) and uniformity (embeddings are spread without unnecessary gaps). For example, after fine-tuning, check if “Python programming” and “coding in Python” embeddings move closer, while “Python programming” and “snake species” grow farther apart. Tools like t-SNE plots or UMAP can visualize these changes.
Finally, validate with real-world scenarios and edge cases. Metrics might improve on benchmarks but fail in practical use. Test the model with actual user queries or domain-specific edge cases. For instance, if your model is for medical text, verify if “myocardial infarction” and “heart attack” embeddings become indistinguishable after fine-tuning, even if they weren’t explicitly paired in training. Also, check for overfitting by evaluating a holdout dataset not used during fine-tuning. If the model performs well on training-related data but poorly on new topics (e.g., handling “AI ethics” queries when trained on general tech articles), it might have over-optimized for the training domain. A/B testing in production (comparing old and new embeddings for real users) can provide the most direct evidence of improvement, though it requires infrastructure to deploy safely.