Is there a way to measure the performance of a Word2Vec model?
New Altair Community Member
Updated by Jocelyn
I am using the word2vec extension to train a model for polynomial text classification.
None of the standard Performance operators seem to "stack" with the word2vec model (RMWord2VecModel).
Is there any way that I could measure the performance of the model on the training dataset?
None of the standard Performance operators seem to "stack" with the word2vec model (RMWord2VecModel).
Is there any way that I could measure the performance of the model on the training dataset?
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MartinLiebig
Altair EmployeeHi @Christos_Karapapas ,
good question! Usually Word2Vec is either evaluated empircally (close words should be "synonyms", close in meaning) or by using down stream tasks like Entity Recognition.
Cheers,
Martin
New Altair Community Member
OPSo, if I understand it correctly it's just another way of finding synonyms just like when grouping words by their root (lemmas)?
And if so why it exports a model? To be used in later processes with different datasets and still be able to find the lemma of a word?
And if so why it exports a model? To be used in later processes with different datasets and still be able to find the lemma of a word?
New Altair Community Member
Yes, it is more of a processing model than a predictive model. Most unsupervised ML approaches don't have th same kind of performance measurement as you are used to from standard predictive models.