Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/10152#discussion_r46768448
--- 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 --
I suspect the max length here is mostly because it was in the original
implementation. The problem here is: what if the 'sentence' ends up being very
very long?
Although a real "sentence" would not ever reasonably be 1000+ words,
word2vec is used in other contexts where a "sentence" is something else. I can
see making this configurable, but not removing it.
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