Github user ygcao commented on a diff in the pull request:
https://github.com/apache/spark/pull/10152#discussion_r46763811
--- 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 --
This is the essential change, even make the max configurable is not
reasonable.
as I mentioned in the Jira issue:
sentence boundary matters for sliding window, we shouldn't train model from
a window across sentences. the current 100 word as a hard split for sentences
doesn't really make sense.
any hard setting is not a good choice.
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