Hi. How exacly MLlib implementation of word2vec converts word vectors
into one feature vector per row?
TEXT
[Hi, I, heard, ab..]
[I, wish, Java, c..]
[Logistic, regres.]
| word2vec
V
WORD VECTOR
heard [0.14950960874557...|
are [-0.1639076173305...|
neat [0.13949351012706...|
classes [0.03703496977686...|
I [-0.0189154129475...|
regression [0.15298652648925...|
Logistic [-0.1270201653242...|
Spark [-0.0535793155431...|
could [0.12216471135616...|
use [0.08246973901987...|
Hi [0.16548289358615...|
models [-0.0568316541612...|
case [0.11626788973808...|
about [-0.1500445008277...|
Java [-0.0407485179603...|
wish [0.11882393807172...|
| HOW?
V
TEXT RESULT
[Hi, I, heard, ab... ] [0.01849065460264...|
[I, wish, Java, c... ] [0.05958533100783...|
[Logistic, regres...] [-0.0110558800399...|
Is there a way to change this default method?
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