I was reading somewhere that the BERT and USE style were "big-symantic space" designed to 0.0-1.0 so that things unrelated would be close to 0.0, and are classifiers.
But now, like the OpenAI embedding you're talking about the embedding are constrained, trained for retrieval in mind. The pairs are ordered closer, easier to search.
Comments
I was reading somewhere that the BERT and USE style were "big-symantic space" designed to 0.0-1.0 so that things unrelated would be close to 0.0, and are classifiers.
But now, like the OpenAI embedding you're talking about the embedding are constrained, trained for retrieval in mind. The pairs are ordered closer, easier to search.