Github user ygcao commented on a diff in the pull request:
https://github.com/apache/spark/pull/10152#discussion_r47552249
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
@@ -281,17 +294,28 @@ 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 { sentenceIter
=>
--- End diff --
This is very close to my original version exception for will throw out all
words after MAX_SENTENCE_LENGTH, and are you preferring to make the
maxSentenceLength static config?
The latest version of mine will still try to take use of the rest of
sentences for training after cutting by maxSentenceLength. e.g. for a 2200 word
long sentence, it will be used as three cut sentences just like the old version
except for the last/third sentence from the cut will be 200 words long without
words padded from the next sentence. This way, we can maximize the usage of our
data with both respecting sentence boundary and sentence length restriction.
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