Evaluating the quality of embeddings involves assessing how well the embeddings capture the underlying structure and relationships in the data. One common evaluation method is using downstream tasks, such as classification, clustering, or retrieval, to measure how well the embeddings perform on specific problems. For example, embeddings for words or documents can be tested by their ability to improve the accuracy of a classifier or the relevance of search results.
Another approach is to measure the cosine similarity between embeddings to check if similar items are placed closer together in the vector space. For word embeddings, techniques like analogy tasks (e.g., "man" is to "woman" as "king" is to "queen") can be used to assess how well the embeddings capture semantic relationships.
For specialized domains like images or product recommendations, the quality of embeddings can also be evaluated by their effectiveness in nearest neighbor searches, where similar items are retrieved based on their embeddings. In general, a combination of quantitative measures (e.g., accuracy or recall) and qualitative assessments (e.g., human evaluation) is used to evaluate the quality of embeddings.