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analyzerConfig
Lucene Analyzer Schema
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LuceneTextAnalyzer schema for tokenization (JSON-encoded)
type: string
default value: '{ "analyzers": [{ "name": "StdTokLowerStop","charFilters": [ { "type": "htmlstrip" } ],"tokenizer": { "type": "standard" },"filters": [{ "type": "lowercase" },{ "type": "KStem" },{ "type": "length", "min": "2", "max": "32767" },{ "type": "fusionstop", "ignoreCase": "true", "format": "snowball", "words": "org/apache/lucene/analysis/snowball/english_stop.txt" }] }],"fields": [{ "regex": ".+", "analyzer": "StdTokLowerStop" } ]}'
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dataFormat
Data format
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Spark-compatible format which training data comes in (like 'solr', 'hdfs', 'file', 'parquet' etc)
type: string
default value: 'solr'
enum: {
solr
hdfs
file
parquet
}
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fieldToVectorize
Field to Vectorize
required
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Solr field containing text training data. Data from multiple fields with different weights can be combined by specifying them as field1:weight1,field2:weight2 etc.
type: string
minLength: 1
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id
Spark Job ID
required
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The ID for this Spark job. Used in the API to reference this job. Allowed characters: a-z, A-Z, dash (-) and underscore (_)
type: string
maxLength: 63
pattern: ^[A-Za-z0-9_\-]+$
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maxDF
Max Term Document Frequency
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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'
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minDF
Minimum Term Document Frequency
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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'
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minSparkPartitions
Minimum Number of Spark Partitions
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Minimum number of Spark partitions for training job.
type: integer
default value: '200'
exclusiveMinimum: false
minimum: 1
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modelId
Model ID
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Identifier for the model to be trained; uses the supplied Spark Job ID if not provided.
type: string
minLength: 1
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norm
Vector normalization
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p-norm to normalize vectors with (choose -1 to turn normalization off)
type: integer
default value: '2'
enum: {
-1
0
1
2
}
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numRelatedTerms
Number of Related Words
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For each collection of input words, find this many word2vec-related words
type: integer
default value: '10'
exclusiveMinimum: false
minimum: 1
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outputCollection
Output Collection
required
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Solr Collection to store model-labeled data to
type: string
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outputField
Output Field
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Solr field which will contain terms which the word2vec model considers are related to the input
type: string
default value: 'related_terms_txt'
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overwriteExistingModel
Overwrite existing model
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If a model exists in the model store, overwrite when this job runs
type: boolean
default value: 'true'
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overwriteOutput
Overwrite Output
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Overwrite output collection
type: boolean
default value: 'true'
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predictedLabelField
Word2Vec Feature Field
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Solr field which will contain vector features when the word2vec model is applied to documents
type: string
default value: 'w2vFeatures'
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randomSeed
Random seed
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For any deterministic pseudorandom number generation
type: integer
default value: '1234'
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serializeAsMleap
Serialize as Mleap Bundle
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Serialize the output model as Mleap Bundle
type: boolean
default value: 'true'
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sourceFields
Fields to Load
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Solr fields to load (comma-delimited). Leave empty to allow the job to select the required fields to load at runtime.
type: string
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sparkConfig
Spark Settings
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Spark configuration settings.
type: array of object
object attributes: {
key
(required)
: {
display name: Parameter Name
type: string
}
value
: {
display name: Parameter Value
type: string
}
}
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stopwordsList
List of stopwords
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Stopwords defined in Lucene analyzer config
type: array of string
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trainingCollection
Training Collection
required
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Solr Collection containing labeled training data
type: string
minLength: 1
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trainingDataFilterQuery
Training data filter query
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Solr query to use when loading training data
type: string
default value: '*:*'
minLength: 3
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trainingDataFrameConfigOptions
Dataframe Config Options
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Additional spark dataframe loading configuration options
type: object
object attributes: {
}
object attributes: {
}
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trainingDataSamplingFraction
Training data sampling fraction
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Fraction of the training data to use
type: number
default value: '1'
exclusiveMaximum: false
maximum: 1
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type
Spark Job Type
required
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type: string
default value: 'word2vec'
enum: {
word2vec
}
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uidField
ID Field Name
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Field containing the unique ID for each document
type: string
minLength: 1
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w2vDimension
Embedding Dimension
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Word-vector dimensionality to represent text
type: integer
default value: '50'
exclusiveMinimum: false
minimum: 0
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w2vMaxIter
Max Iterations
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Maximum number of iterations of the word2vec training
type: integer
default value: '1'
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w2vMaxSentenceLength
Max Sentence Length
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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
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w2vStepSize
Step Size
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Training parameter for word2vec convergence (change at your own peril)
type: number
default value: '0.025'
exclusiveMinimum: false
minimum: 0.005
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w2vWindowSize
Window Size
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The window size (context words from [-window, window]) for word2vec
type: integer
default value: '5'
exclusiveMinimum: false
minimum: 3
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withIdf
IDF Weighting
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Weight vector components based on inverse document frequency
type: boolean
default value: 'true'
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