To measure semantic similarity accuracy with embedding models, you typically compare how well the model's vector-based similarity scores align with human judgments. This process involves three main steps: using benchmark datasets with ground-truth similarity scores, generating embeddings for text pairs, and calculating statistical correlations between model outputs and human ratings. For example, datasets like the Semantic Textual Similarity (STS) benchmark or SICK dataset provide sentence pairs annotated with similarity scores (e.g., 0 to 5). You’d convert each sentence into an embedding vector using models like BERT, SBERT, or OpenAI's embeddings, compute cosine similarity between the vectors, and then measure how closely those scores match the human ratings using metrics like Pearson or Spearman correlation.
A practical example using Python might involve loading a pre-trained model from Hugging Face’s sentence-transformers library. For instance, after installing the library, you could load the all-MiniLM-L6-v2 model, encode sentences like "A man is playing guitar" and "A musician performs a song," and compute their cosine similarity. You’d repeat this for all pairs in the STS dataset, then calculate the Pearson correlation between your model’s similarity scores and the dataset’s human ratings. Tools like scikit-learn’s cosine_similarity function and pearsonr method simplify this process. If the correlation is high (e.g., 0.85), it suggests the model’s embeddings capture semantic relationships well. For reproducibility, split the data into training/validation sets to avoid overfitting to specific examples.
Key considerations include choosing the right evaluation dataset for your use case. For example, STS focuses on general-purpose sentences, while MedSTS targets medical text. Preprocessing steps like lowercasing or removing stopwords might affect results depending on the embedding model’s requirements. Also, test multiple models (e.g., compare SBERT against FastText) to identify which performs best for your domain. Finally, report both Pearson (linear relationship) and Spearman (rank-order correlation) metrics, as they highlight different aspects of alignment with human judgments. If your model scores poorly (e.g., 0.3 correlation), consider fine-tuning it on domain-specific data or trying larger architectures.