water.bindings.pojos

Class Word2VecParametersV3

java.lang.Object

water.bindings.pojos.Schema

water.bindings.pojos.ModelParametersSchema

water.bindings.pojos.Word2VecParametersV3

public class Word2VecParametersV3
extends ModelParametersSchema

Field Summary

Fields

Modifier and Type	Field and Description
int	epochs Number of training iterations to run
boolean	ignore_const_cols Ignore constant columns
java.lang.String[]	ignored_columns Ignored columns
float	initLearningRate Set the starting learning rate
int	minWordFreq This will discard words that appear less than times
java.lang.String	model_id Destination id for this model; auto-generated if not specified
int	negSampleCnt Number of negative examples, common values are 3 - 10 (0 = not used)
NormModel	normModel Use Hierarchical Softmax or Negative Sampling
boolean	score_each_iteration Whether to score during each iteration of model training
float	sentSampleRate Set threshold for occurrence of words.
java.lang.String	training_frame Training frame
java.lang.String	validation_frame Validation frame
int	vecSize Set size of word vectors
int	windowSize Set max skip length between words
WordModel	wordModel Use the continuous bag of words model or the Skip-Gram model

Constructor Summary

Constructors

Constructor and Description
Word2VecParametersV3()

Method Summary

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

vecSize

public int vecSize

Set size of word vectors

windowSize

public int windowSize

Set max skip length between words

sentSampleRate

public float sentSampleRate

Set threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; useful range is (0, 1e-5)

normModel

public NormModel normModel

Use Hierarchical Softmax or Negative Sampling

negSampleCnt

public int negSampleCnt

Number of negative examples, common values are 3 - 10 (0 = not used)

epochs

public int epochs

Number of training iterations to run

minWordFreq

public int minWordFreq

This will discard words that appear less than times

initLearningRate

public float initLearningRate

Set the starting learning rate

wordModel

public WordModel wordModel

Use the continuous bag of words model or the Skip-Gram model

model_id

public java.lang.String model_id

Destination id for this model; auto-generated if not specified

training_frame

public java.lang.String training_frame

Training frame

validation_frame

public java.lang.String validation_frame

Validation frame

ignored_columns

public java.lang.String[] ignored_columns

Ignored columns

ignore_const_cols

public boolean ignore_const_cols

Ignore constant columns

score_each_iteration

public boolean score_each_iteration

Whether to score during each iteration of model training

Constructor Detail

Word2VecParametersV3

public Word2VecParametersV3()