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by ogrisel
4641 days ago
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No but with n binary dimensions (with value 0 or 1) you can encode 2^n unique identifiers. So with 1000 continuous dimensions (typically values between -1 and 1 coded on 32 bit floats) you can encode quite a bunch of concepts and their nuances. Note: the default dimensionality of word2vec is 100 instead of 1000. Apparently you can get better results with dim=300 and a very large training corpus. To leverage higher dimensions you need: more CPU time to reach convergence and a lot more data to leverage the added model capacity. |
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