To evaluate a Sentence Transformer model on tasks like semantic textual similarity (STS) or retrieval accuracy, you need specific metrics, datasets, and evaluation processes tailored to each task. Here's a structured approach:
For Semantic Textual Similarity (STS): The primary goal is to measure how well the model's similarity scores align with human judgments. Use datasets like the STS Benchmark or SICK-R, which contain sentence pairs annotated with similarity scores (e.g., 0–5). Encode the sentences into embeddings, compute cosine similarity between each pair, and compare these scores to the human annotations using Spearman’s rank correlation coefficient. Spearman is preferred over Pearson because it measures how well the model preserves the ranking of similarities (e.g., whether "cat vs. dog" is ranked higher than "cat vs. car"), which is more critical than linear correlation. For example, if the model assigns similarity scores of [0.8, 0.5, 0.3] to three pairs, and human scores are [5, 3, 1], Spearman checks if the order matches, not the exact numerical values.
For Retrieval Accuracy: Here, the model must retrieve relevant documents or sentences from a corpus given a query. Use datasets like MS MARCO (for real-world web search) or TREC-CAR (for domain-specific retrieval). After encoding queries and corpus documents into embeddings, compute similarity scores (e.g., cosine) between each query and all documents. Rank the documents by similarity and evaluate using metrics like:
- Recall@k: The percentage of queries where the correct document appears in the top k results.
- Mean Average Precision (MAP): Measures precision across all relevant documents, accounting for their positions in the ranked list.
- NDCG (Normalized Discounted Cumulative Gain): Rewards higher rankings for relevant documents.
For example, if a query has three relevant documents and the model retrieves two of them at positions 1 and 5, Recall@5 would be 2/3 ≈ 66.7%, while NDCG would penalize the lower-ranked document.
Implementation Steps and Tools:
- Data Preparation: Split data into training/validation/test sets to avoid overfitting. For retrieval, ensure queries and documents are separate.
- Encoding: Use the Sentence Transformer to generate embeddings for all sentences/documents.
- Evaluation Libraries: Leverage built-in utilities like
sentence-transformers’evaluationmodule, which supports STS and retrieval metrics. For custom pipelines, use libraries likescipyfor Spearman orrank-evalfor retrieval metrics. - Efficiency Considerations: For large corpora, pair the model with vector databases (e.g., FAISS) to speed up similarity searches during retrieval evaluation.
By combining task-specific metrics, standardized datasets, and efficient tooling, you can systematically assess whether the model meets performance requirements for real-world applications.