Embeddings are evaluated using a combination of intrinsic and extrinsic methods to measure how well they capture meaningful patterns in data. Intrinsic evaluation focuses on the internal properties of embeddings, such as their ability to group similar items or solve analogy tasks. Extrinsic evaluation tests embeddings in real-world applications like classification or search. Both approaches are necessary because embeddings that perform well in isolated tests might not translate to practical tasks, and vice versa.
For intrinsic evaluation, common techniques include word similarity tasks and analogy solving. For example, embeddings can be tested on datasets like WordSim-353, where pairs of words (e.g., “car” and “vehicle”) are rated for similarity. The embeddings’ cosine similarity scores are compared to human judgments to assess accuracy. Another example is solving analogies like “king - man + woman = queen” by checking if the closest vector to the result matches the expected word. Tools like the Gensim library provide built-in functions for these tests. However, these methods have limitations—they focus on specific linguistic patterns and may not reflect performance in broader contexts.
Extrinsic evaluation involves integrating embeddings into downstream tasks and measuring their impact. For instance, in a sentiment analysis model, embeddings might be evaluated by replacing them with alternatives (e.g., switching from Word2Vec to BERT) and comparing accuracy improvements. In search systems, embeddings are tested by retrieving relevant documents for a query and measuring metrics like recall@k. Frameworks like Hugging Face Transformers or scikit-learn pipelines are often used to streamline these experiments. Additionally, clustering metrics (e.g., silhouette score) or dimensionality reduction visualizations (t-SNE, UMAP) can reveal how well embeddings separate distinct categories. Cross-modal tasks, like image-to-text retrieval, are evaluated using metrics such as mean reciprocal rank (MRR). The choice of evaluation depends on the use case, but combining intrinsic and extrinsic methods provides a balanced assessment of embedding quality.