This is a Scala implementation of the word2vec toolkit's model representation.
This Scala interface allows the user to access the vector representation output by the word2vec toolkit. It also implements example operations that can be done on the vectors (e.g., word-distance, word-analogy).
Note that it does NOT implement the actual training algorithms. You will still need to download and compile the original word2vec tool if you wish to train new models.
The included model (vectors.bin) was trained on the text8 corpus, which contains the first 100 MB of the "clean" English Wikipedia corpus. The following training parameters were used:
./word2vec -train text8 -output vectors.bin -cbow 0 -size 200 -window 5 -negative 0 -hs 1 -sample 1e-3 -threads 12 -binary 1val model = new Word2Vec()
model.load("vectors.bin")val results = model.distance(List("france"), N = 10)
model.pprint(results) Word Cosine distance
------------------------------------------------------------------------
belgium 0.706633
spain 0.672767
netherlands 0.668178
italy 0.616545
switzerland 0.595572
luxembourg 0.591839
portugal 0.564891
germany 0.549196
russia 0.543569
hungary 0.519036
model.pprint( model.distance(List("france", "usa")) ) Word Cosine distance
------------------------------------------------------------------------
netherlands 0.691459
switzerland 0.672526
belgium 0.656425
canada 0.641793
russia 0.612469
. .
. .
. .
croatia 0.451900
vantaa 0.450767
roissy 0.448256
norway 0.447392
cuba 0.446168
model.pprint( model.distance(List("france", "usa", "usa")) ) Word Cosine distance
------------------------------------------------------------------------
canada 0.631119
switzerland 0.626366
netherlands 0.621275
russia 0.569951
belgium 0.560368
. .
. .
. .
osaka 0.418143
eas 0.417097
antholz 0.415458
fukuoka 0.414105
zealand 0.413075
model.pprint( model.analogy("king", "queen", "man", N = 10) ) Word Cosine distance
------------------------------------------------------------------------
woman 0.547376
girl 0.509787
baby 0.473137
spider 0.450589
love 0.433065
prostitute 0.433034
loves 0.422127
beauty 0.421060
bride 0.413417
lady 0.406856
model.pprint( model.rank("apple", Set("orange", "soda", "lettuce")) ) Word Cosine distance
------------------------------------------------------------------------
orange 0.203808
lettuce 0.132007
soda 0.075649
- [09/2013] The code was tested to work with models trained using revision r33 of the word2vec toolkit. It should also work with future revisions, assuming that the output format does not change.
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