word2vector v2.2.1

word2vector NodeJS Interface

This is a Node.js interface for Google's word2vector. Here is an example of how to load large model like GoogleNews-vectors-negative300.bin by this package.

Supports both binary model and raw text model.

Installation

Linux, Unix OS are supported. Install it via npm:

npm install word2vector --save

In Node.js, require the module as below:

var w2v = require( 'word2vector' );

API Document:

Overview

train load getVector getVectors getSimilarWords getNeighbors similarity substract add

w2v.train( trainFile, modelFile, options, callback )

Click here to see example TrainFile format. Example:

var w2v = require("./lib");
var trainFile = "./data/train.data",
    modelFile = "./data/test.model.bin";
w2v.train(trainFile, modelFile, {
  	cbow: 1,           // use the continuous bag of words model //default
  	size: 10,          // sets the size (dimension) of word vectors // default 100
  	window: 8,         // sets maximal skip length between words // default 5
    binary: 1,         // save the resulting vectors in binary mode // default off
  	negative: 25,      // number of negative examples; common values are 3 - 10 (0 = not used) // default 5
  	hs: 0,             // 1 = use  Hierarchical Softmax // default 0
  	sample: 1e-4,      
  	threads: 20,
  	iter: 15,
  	minCount: 1,       // This will discard words that appear less than *minCount* times // default 5
    logOn: false       // sets whether any output should be printed to the console // default false
  });

w2v.load( modelFile,?readType = "")

var w2v = require("../lib");
var modelFile = "./test.model.bin";
w2v.load( modelFile );
// console.log(w2v.getSimilarWordsWords());

w2v.getVector(word="word")

'use strict';
var w2v = require("./lib");
var modelFile = "./data/test.model.bin";
w2v.load( modelFile );
console.log(w2v.getVector("孫悟空"));
console.log(w2v.getVector("李洵"));

Sample Output:

// Array Type Only
[ 0.104406,
  -0.160019,
  -0.604506,
  -0.622804,
  0.039482,
  -0.120058,
  0.073555,
  0.05646,
  0.099059,
  -0.419282 ]

null // Return null if this word is not in model.

w2v.getVectors(words="word1", "word2", ?options = {})

var w2v = require("./lib");  
var modelFile = "./data/test.model.bin";
w2v.load( modelFile );
console.log(w2v.getVectors(["孫悟空", "李洵"]));

Sample Output:

[ { word: '孫悟空',
    vector:
     [ 0.104406,
       -0.160019,
       -0.604506,
       -0.622804,
       0.039482,
       -0.120058,
       0.073555,
       0.05646,
       0.099059,
       -0.419282 ] },
  { word: '李洵', vector: null } ]
  // this will trigger a error log in console:
  //'李洵' is not found in the model.

w2v.getSimilarWords(word = "word", ?options = {})

Return 40ish words that is similar to "word".

var w2v = require("./lib");
var modelFile = "./data/test.model.bin";
w2v.load( modelFile );
console.log(w2v.getSimilarWords("唐三藏"));
console.log(w2v.getSimilarWords("李洵"));

Sample Output:

// Array Type
[ { word: '孫悟空', similarity: 0.974369 },
  { word: '吳承恩', similarity: 0.96686 },
  { word: '林黛玉', similarity: 0.966664 },
  { word: '北地', similarity: 0.96264 },
  { word: '賈寶玉', similarity: 0.962137 },
  { word: '楚霸王', similarity: 0.955795 },
  { word: '梁山泊', similarity: 0.932804 },
  { word: '濮陽', similarity: 0.927542 },
  { word: '黃天霸', similarity: 0.927459 },
  { word: '英雄豪傑', similarity: 0.921575 }]
// Return empty [] if this word is not in model.
'李洵' is not found in the model.
[]

getNeighbors(vector, ?options = {})

var w2v = require("./lib");
var modelFile = "./data/test.model.bin";
w2v.load( modelFile );
var a = w2v.getNeighbors(w2v.getVector("唐三藏"), {N: 9});
// These are equal to use w2v.getSimilarWords("唐三藏");
console.log(a);

Sample Output1:

[ { word: '唐三藏', similarity: 0.9999993515200001 },
  { word: '孫悟空', similarity: 0.974368825898 },
  { word: '吳承恩', similarity: 0.966859435824 },
  { word: '林黛玉', similarity: 0.966663471323 },
  { word: '北地', similarity: 0.962639240211 },
  { word: '賈寶玉', similarity: 0.9621371820049999 },
  { word: '楚霸王', similarity: 0.9557946924850002 },
  { word: '梁山泊', similarity: 0.9328033548890001 },
  { word: '濮陽', similarity: 0.9275417727409999 } ]
{ '唐三藏': 0.9999993515200001,
  '孫悟空': 0.974368825898,
  '吳承恩': 0.966859435824,
  '林黛玉': 0.966663471323,
  '北地': 0.962639240211,
  '賈寶玉': 0.9621371820049999,
  '楚霸王': 0.9557946924850002,
  '梁山泊': 0.9328033548890001,
  '濮陽': 0.9275417727409999 }

w2v.similarity(word1 = "word1", word2 = "word2")

w2v.similarity(vector1 = [], word2 = "word2")

w2v.similarity(word1 = "word1", vector2 = [])

w2v.similarity(vector1 = [], vector2 = [])

'use strict';
var w2v = require("./lib");
var modelFile = "./data/test.model.bin";
w2v.load( modelFile );
var a = w2v.similarity("唐三藏", "孫悟空"); //  0.974368825898
console.log(a);
var b = w2v.similarity("唐三藏", "李洵"); //  0.974368825898
// same as var b = w2v.similarity("唐三藏", w2v.getVector("李洵"));
// same as var b = w2v.similarity(w2v.getVector("唐三藏"), "李洵");
// same as var b = w2v.similarity(w2v.getVector("唐三藏"), w2v.getVector("李洵"));
console.log(b);

Sample Output:

0.974368825898
// '李洵' is not found in the model. // error alert in console
false

w2v.substract(word1 = "word1", word2 = "word2")

w2v.substract(vector1 = [], word2 = "word2")

w2v.substract(word1 = "word1", vector2 = [])

w2v.substract(vector1 = [], vector2 = [])

Example:

'use strict';
var w2v = require("./lib");
var modelFile = "./data/test.model.bin";
w2v.load( modelFile );
var a = w2v.substract("孫悟空", "孫悟空");
console.log(a);

Sample Output:

[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]

w2v.add(word1 = "word1", word2 = "word2")

w2v.add(vector1 = [], word2 = "word2")

w2v.add(word1 = "word1", vector2 = [])

w2v.add(vector1 = [], vector2 = [])

Example:

'use strict';
var w2v = require("./lib");
var modelFile = "./data/test.model.bin";
w2v.load( modelFile );
var a = w2v.add("孫悟空", "孫悟空");
var b = w2v.getVector("孫悟空");
console.log(a);
console.log(b);

Sample Output:

[ 0.208812,
  -0.320038,
  -1.209012,
  -1.245608,
  0.078964,
  -0.240116,
  0.14711,
  0.11292,
  0.198118,
  -0.838564 ]
[ 0.104406,
  -0.160019,
  -0.604506,
  -0.622804,
  0.039482,
  -0.120058,
  0.073555,
  0.05646,
  0.099059,
  -0.419282 ]