Description

Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.

reference: https://en.wikipedia.org/wiki/Word2vec Mikolov, Tomas; et al. (2013). “Efficient Estimation of Word Representations in Vector Space” Mikolov, Tomas; Sutskever, Ilya; Chen, Kai; Corrado, Greg S.; Dean, Jeff (2013). Distributed representations of words and phrases and their compositionality. https://code.google.com/archive/p/word2vec/

Parameters

Name Description Type Required？ Default Value
numIter Number of iterations, The default value is 1 Integer 1
selectedCol Name of the selected column used for processing String ✓
vectorSize vector size of embedding Integer 100
alpha learning rate of sgd Double 0.025
wordDelimiter Delimiter of words String “ “
minCount minimum count of word Integer 5
randomWindow Is random window or not String “true”
window the length of window in w2v Integer 5
selectedCol Name of the selected column used for processing String ✓
reservedCols Names of the columns to be retained in the output table String[] null
outputCol Name of the output column String null
wordDelimiter Delimiter of words String “ “
predMethod Method to predict doc vector, support 3 method: avg, min and max, default value is avg. String “avg”

Script Example

Code

  1. import numpy as np
  2. import pandas as pd
  4. data = np.array([
  5.     ["A B C"]
  6. ])
  8. df = pd.DataFrame({"tokens": data[:, 0]})
  9. inOp = dataframeToOperator(df, schemaStr='tokens string', op_type='batch')
  10. word2vec = Word2Vec().setSelectedCol("tokens").setMinCount(1).setVectorSize(4)
  11. word2vec.fit(inOp).transform(inOp).print()

Results

Prediction
  1. rowID    tokens
  2. 0  0.7346309627024759 0.5270851926937304 0.201858...