Github user ygcao commented on a diff in the pull request:
https://github.com/apache/spark/pull/10152#discussion_r46925097
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
@@ -281,16 +280,17 @@ class Word2Vec extends Serializable with Logging {
val expTable = sc.broadcast(createExpTable())
val bcVocab = sc.broadcast(vocab)
val bcVocabHash = sc.broadcast(vocabHash)
-
- val sentences: RDD[Array[Int]] = words.mapPartitions { iter =>
+ //each partition is a collection of sentences, will be translated into
arrays of Index integer
+ val sentences: RDD[Array[Int]] = dataset.mapPartitions { iter =>
--- End diff --
Agreed. Will do.
Btw: the usage of maxsentencelenth in the python code is different than
the original mllib word2vec. It's actually respect sentence structure on
condition that it's shorter than the max allowance, which is a mixture of my
change with the adress of @srowen too long sentence concern. But as you
mentioned and I mentioned below, the too long sentence should not be a concern
in normal situation. The only exception I can think of is to use a poor
sentence splitter plus using concatenated large portion of a big corpus as one
individual document in the input.
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