This is a fork of the original word2vec by Tomas Mikolov.
The main changes are as follows:
- fix compilation on macOS
- fix several memory leaks
- apply a fix from the main repo PRs
- reorganize the repo into directories
- clean up the scripts & don't automatically retrain data
- clang-format the code
I've only built it on macOS, but it should work on Linux as well.
% make
gcc ./src/word2vec.c -o ./bin/word2vec -lm -pthread -O3 -march=native -Wall -funroll-loops -Wno-unused-result
gcc ./src/word2phrase.c -o ./bin/word2phrase -lm -pthread -O3 -march=native -Wall -funroll-loops -Wno-unused-result
gcc ./src/distance.c -o ./bin/distance -lm -pthread -O3 -march=native -Wall -funroll-loops -Wno-unused-result
gcc ./src/word-analogy.c -o ./bin/word-analogy -lm -pthread -O3 -march=native -Wall -funroll-loops -Wno-unused-result
gcc ./src/compute-accuracy.c -o ./bin/compute-accuracy -lm -pthread -O3 -march=native -Wall -funroll-loops -Wno-unused-result
It will put the executables in the bin directory.
There are several demos in the scripts directory. Each of them will download the data they need to the data directory and run an example. Take a look at the scripts to see how they work. (The "big model" one hasn't been fixed for the new file layout yet.)
Example:
% ./scripts/demo-phrases.sh
<...data downloading progress...>
Starting training using file ./data/news.2012.en.shuffled-norm0
Words processed: 296M Vocab size: 33133K
Vocab size (unigrams + bigrams): 18838711
Words in train file: 296901342
Words written: 296M
real 3m11.759s
user 3m0.623s
sys 0m5.546s
Starting training using file ./data/news.2012.en.shuffled-norm0-phrase0
Words processed: 280M Vocab size: 38715K
Vocab size (unigrams + bigrams): 21728781
Words in train file: 280513979
Words written: 280M
real 3m0.606s
user 2m48.924s
sys 0m5.841s
Starting training using file ./data/news.2012.en.shuffled-norm1-phrase1
Vocab size: 681320
Words in train file: 283545447
Alpha: 0.002334 Progress: 95.33% Words/thread/sec: 150.82k
real 30m45.654s
user 446m15.739s
sys 2m14.087s
Enter word or sentence (EXIT to break): phrase
Word: phrase Position in vocabulary: 4437
Word Cosine distance
------------------------------------------------------------------------
word 0.801410
words 0.755938
phrases 0.738470
adjective 0.656568
quote 0.612604
adverb 0.611929
common_usage 0.600758
aphorism 0.598873
pejorative 0.591596
slang 0.586171
prepositional 0.586160
mantra 0.579630
lexicon 0.577740
slogan 0.576222
epithet 0.575722
adjectives 0.572316
catchphrase 0.572061
uttered 0.570064
metaphor 0.566445
verb 0.557801
terminology 0.555637
quotation 0.549663
syllable 0.548051
catch_phrase 0.546580
term_romnesia 0.545465
neologisms 0.541982
utterance 0.540865
dictionary 0.535510
shorthand 0.535362
yiddish_word 0.532841
rhetorical_device 0.532453
language 0.531050
oxford_english_dictionary 0.531029
clich 0.526269
memorable_phrase 0.525383
reference 0.524749
verse 0.524645
refrain 0.523493
swear_word 0.522884
pronoun 0.521555
Enter word or sentence (EXIT to break): computer
Word: computer Position in vocabulary: 1922
Word Cosine distance
------------------------------------------------------------------------
computers 0.817271
software 0.712940
laptop 0.680752
computer's 0.654755
keystrokes 0.636880
electronic 0.618771
server 0.613043
mobile_device 0.607748
device 0.607426
spyware 0.605617
malicious_code 0.604674
thumb_drive 0.597072
tracking_software 0.594464
user 0.590326
automated 0.590318
desktop 0.587996
computers' 0.585649
desktop_computer 0.584500
web 0.582986
malware 0.582174
usb_drives 0.580727
computer_servers 0.580529
usb_drive 0.580131
servers 0.578256
word_processing 0.575762
take_screenshots 0.572279
mobile_phone 0.571906
flash_drive 0.571705
devices 0.569640
laptop_computer 0.569629
pdf_files 0.567755
encryption 0.565766
encrypt 0.562853
web_servers 0.561529
via_usb 0.561428
remote_server 0.560288
internet_connection 0.559574
machine_learning 0.559485
handheld_devices 0.558564
cellphone 0.558308
Enter word or sentence (EXIT to break): EXIT
We provide an implementation of the Continuous Bag-of-Words (CBOW) and the Skip-gram model (SG), as well as several demo scripts.
Given a text corpus, the word2vec tool learns a vector for every word in the vocabulary using the Continuous Bag-of-Words or the Skip-Gram neural network architectures. The user should to specify the following:
- desired vector dimensionality
- the size of the context window for either the Skip-Gram or the Continuous Bag-of-Words model
- training algorithm: hierarchical softmax and / or negative sampling
- threshold for downsampling the frequent words
- number of threads to use
- the format of the output word vector file (text or binary)
Usually, the other hyper-parameters such as the learning rate do not need to be tuned for different training sets.
The script demo-word.sh downloads a small (100MB) text corpus from the web, and trains a small word vector model. After the training is finished, the user can interactively explore the similarity of the words.
More information about the scripts is provided at https://code.google.com/p/word2vec/