Word2Vec Model Training Jobs

Configuration Properties

Train a shallow neural model, and project each document onto this vector embedding space.

Note	This job is deprecated as of Fusion 5.2.0.

Configuration Properties

Property Description, Type

analyzerConfig

Lucene Analyzer Schema

LuceneTextAnalyzer schema for tokenization (JSON-encoded)

type: string

default value: '{ "analyzers": [{ "name": "StdTokLowerStop","charFilters": [ { "type": "htmlstrip" } ],"tokenizer": { "type": "standard" },"filters": [{ "type": "lowercase" },{ "type": "stop", "ignoreCase": "true", "format": "snowball", "words": "org/apache/lucene/analysis/snowball/english_stop.txt" }] }],"fields": [{ "regex": ".+", "analyzer": "StdTokLowerStop" } ]}'

dataFormat

Data format

Spark-compatible format which training data comes in (like 'solr', 'hdfs', 'file', 'parquet' etc)

type: string

default value: 'solr'

enum: { solr hdfs file parquet }

fieldToVectorize

Field to Vectorize

required

Solr field containing text training data for prediction/clustering instances,if want to analyze multiple fields with different weights please use the format field1:weight1,field2:weight2

type: string

minLength: 1

Spark Job ID

required

The ID for this Spark job. Used in the API to reference this job

type: string

maxLength: 128

pattern: ^[A-Za-z0-9_\-]+$

maxDF

Max Term Document Frequency

To be kept, terms must occur in no more than this number of documents (if > 1.0), or no more than this fraction of documents (if <= 1.0)

type: number

default value: '1.0'

minDF

Minimum Term Document Frequency

To be kept, terms must occur in at least this number of documents (if > 1.0), or at least this fraction of documents (if <= 1.0)

type: number

default value: '0.0'

modelId

Model ID

Identifier for the model to be trained; uses the supplied Spark Job ID if not provided.

type: string

minLength: 1

norm

Vector normalization

p-norm to normalize vectors with (choose -1 to turn normalization off)

type: integer

default value: '2'

enum: { -1 0 1 2 }

numRelatedTerms

Number of Related Words

For each collection of input words, find this many word2vec-related words

type: integer

default value: '10'

exclusiveMinimum: false

minimum: 1

outputCollection

Output Collection

required

Solr Collection to store model-labeled data to

type: string

outputField

Output Field

Solr field which will contain terms which the word2vec model considers are related to the input

type: string

default value: 'related_terms_txt'

overwriteOutput

Overwrite Output

Overwrite output collection

type: boolean

default value: 'true'

predictedLabelField

Word2Vec Feature Field

Solr field which will contain vector features when the word2vec model is applied to documents

type: string

default value: 'w2vFeatures'

randomSeed

Random seed

For any deterministic pseudorandom number generation

type: integer

default value: '1234'

sourceFields

Fields to Load

Solr fields to load (comma-delimited). Leave empty to allow the job to select the required fields to load at runtime.

type: string

trainingCollection

Training Collection

required

Solr Collection containing labeled training data

type: string

minLength: 1

trainingDataFilterQuery

Training data filter query

Solr query to use when loading training data

type: string

default value: '*:*'

minLength: 3

trainingDataFrameConfigOptions

Dataframe Config Options

Additional spark dataframe loading configuration options

type: object

object attributes: {
}

trainingDataSamplingFraction

Training data sampling fraction

Fraction of the training data to use

type: number

default value: '1.0'

exclusiveMaximum: false

maximum: 1.0

type

Spark Job Type

required

type: string

default value: 'word2vec'

enum: { word2vec }

uidField

ID Field Name

Field containing the unique ID for each document

type: string

minLength: 1

w2vDimension

Embedding Dimension

Word-vector dimensionality to represent text

type: integer

default value: '50'

exclusiveMinimum: false

minimum: 2

w2vMaxIter

Max Iterations

Maximum number of iterations of the word2vec training

type: integer

default value: '1'

w2vMaxSentenceLength

Max Sentence Length

Sets the maximum length (in words) of each sentence in the input data. Any sentence longer than this threshold will be divided into chunks of up to `maxSentenceLength` size.

type: integer

default value: '1000'

exclusiveMinimum: false

minimum: 3

w2vStepSize

Step Size

Training parameter for word2vec convergence (change at your own peril)

type: number

default value: '0.025'

exclusiveMinimum: false

minimum: 0.005

w2vWindowSize

Window Size

The window size (context words from [-window, window]) for word2vec

type: integer

default value: '5'

exclusiveMinimum: false

minimum: 3

withIdf

IDF Weighting

Weight vector components based on inverse document frequency

type: boolean

default value: 'true'