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https://issues.apache.org/jira/browse/SPARK-10105?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Antonio Murgia updated SPARK-10105:
-----------------------------------
Description:
When training Word2Vec on a really big dataset, it's really hard to evaluate
the right minCount parameter, it would really help having a parameter to choose
how many words you want to be in the vocabulary.
Furthermore, the original Word2Vec paper, state that they took into account the
most frequent 1M words.
was:
When training Word2Vec on a really big dataset, it's really hard to evaluate
the right minCount parameter, it would really help having a parameter to choose
how many words you want to be in the vocabulary.
Furthermore, the original Word2Vec paper, state that they took into account the
first 30k words.
> Adding most k frequent words parameter to Word2Vec implementation
> -----------------------------------------------------------------
>
> Key: SPARK-10105
> URL: https://issues.apache.org/jira/browse/SPARK-10105
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Antonio Murgia
> Priority: Minor
> Labels: mllib, top-k, word2vec
>
> When training Word2Vec on a really big dataset, it's really hard to evaluate
> the right minCount parameter, it would really help having a parameter to
> choose how many words you want to be in the vocabulary.
> Furthermore, the original Word2Vec paper, state that they took into account
> the most frequent 1M words.
>
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