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One way to test an embedding space is to use word analogies as unit tests. A properly trained embedding space should successfully complete the analogy “Man is to king as woman is to _____" with "queen". Google has released a collection of 19,000+ word analogies to evaluate word embedding models.


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If you are unhappy with using your training-validation split set for evaluating your model, here are a few additional ways to compare your performance: Metric tracking. This is often used when data is abundant (for example - MSMarco uses MRR to evaluate the quality of their embeddings). You can find that here: https://microsoft.github.io/msmarco/ Another ...


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