Several metrics are commonly used to measure the performance of embeddings. For tasks like classification, accuracy, precision, recall, and F1-score are commonly used to evaluate how well embeddings help in predicting categories or labels. These metrics are particularly useful when embeddings are used as input to classification models, such as for sentiment analysis or text categorization.
For tasks like clustering or nearest neighbor search, metrics like silhouette score, Rand index, or normalized mutual information (NMI) are used to measure how well embeddings group similar data points together. For instance, in image or text retrieval, the quality of embeddings is evaluated by how relevant the retrieved items are when compared to a given query.
In some cases, cosine similarity or Euclidean distance between embeddings can be used directly as a measure of their effectiveness in capturing semantic similarity. Additionally, the performance of embeddings in downstream tasks can be evaluated using task-specific metrics, such as BLEU for machine translation or mean reciprocal rank (MRR) for information retrieval. Ultimately, the choice of metric depends on the specific application and the task at hand.