Class

org.apache.spark.mllib.feature

Word2Vec

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class Word2Vec extends Serializable with Logging

Word2Vec creates vector representation of words in a text corpus. The algorithm first constructs a vocabulary from the corpus and then learns vector representation of words in the vocabulary. The vector representation can be used as features in natural language processing and machine learning algorithms.

We used skip-gram model in our implementation and hierarchical softmax method to train the model. The variable names in the implementation matches the original C implementation.

For original C implementation, see https://code.google.com/p/word2vec/ For research papers, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality.

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@Since( "1.1.0" )
Source
Word2Vec.scala
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Instance Constructors

  1. new Word2Vec()

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Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. final def eq(arg0: AnyRef): Boolean

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  8. def finalize(): Unit

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  9. def fit[S <: Iterable[String]](dataset: JavaRDD[S]): Word2VecModel

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    Computes the vector representation of each word in vocabulary (Java version).

    Computes the vector representation of each word in vocabulary (Java version).

    dataset

    a JavaRDD of words

    returns

    a Word2VecModel

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    @Since( "1.1.0" )

  10. def fit[S <: Iterable[String]](dataset: RDD[S]): Word2VecModel

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    Computes the vector representation of each word in vocabulary.

    Computes the vector representation of each word in vocabulary.

    dataset

    an RDD of sentences, each sentence is expressed as an iterable collection of words

    returns

    a Word2VecModel

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    @Since( "1.1.0" )

  11. final def getClass(): Class[_]

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  12. def hashCode(): Int

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  13. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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  14. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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  15. final def isInstanceOf[T0]: Boolean

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  16. def isTraceEnabled(): Boolean

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  17. def log: Logger

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  18. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logDebug(msg: ⇒ String): Unit

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  20. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logError(msg: ⇒ String): Unit

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  22. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  23. def logInfo(msg: ⇒ String): Unit

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  24. def logName: String

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  25. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  26. def logTrace(msg: ⇒ String): Unit

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  27. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  28. def logWarning(msg: ⇒ String): Unit

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  29. final def ne(arg0: AnyRef): Boolean

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  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

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  32. def setLearningRate(learningRate: Double): Word2Vec.this.type

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    Sets initial learning rate (default: 0.025).

    Sets initial learning rate (default: 0.025).

    Annotations
    @Since( "1.1.0" )

  33. def setMaxSentenceLength(maxSentenceLength: Int): Word2Vec.this.type

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    Sets the maximum length (in words) of each sentence in the input data.

    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 (default: 1000)

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    @Since( "2.0.0" )

  34. def setMinCount(minCount: Int): Word2Vec.this.type

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    Sets minCount, the minimum number of times a token must appear to be included in the word2vec model's vocabulary (default: 5).

    Sets minCount, the minimum number of times a token must appear to be included in the word2vec model's vocabulary (default: 5).

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    @Since( "1.3.0" )

  35. def setNumIterations(numIterations: Int): Word2Vec.this.type

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    Sets number of iterations (default: 1), which should be smaller than or equal to number of partitions.

    Sets number of iterations (default: 1), which should be smaller than or equal to number of partitions.

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    @Since( "1.1.0" )

  36. def setNumPartitions(numPartitions: Int): Word2Vec.this.type

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    Sets number of partitions (default: 1).

    Sets number of partitions (default: 1). Use a small number for accuracy.

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    @Since( "1.1.0" )

  37. def setSeed(seed: Long): Word2Vec.this.type

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    Sets random seed (default: a random long integer).

    Sets random seed (default: a random long integer).

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    @Since( "1.1.0" )

  38. def setVectorSize(vectorSize: Int): Word2Vec.this.type

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    Sets vector size (default: 100).

    Sets vector size (default: 100).

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    @Since( "1.1.0" )

  39. def setWindowSize(window: Int): Word2Vec.this.type

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    Sets the window of words (default: 5)

    Sets the window of words (default: 5)

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    @Since( "1.6.0" )

  40. final def synchronized[T0](arg0: ⇒ T0): T0

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  41. def toString(): String

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  42. final def wait(): Unit

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  43. final def wait(arg0: Long, arg1: Int): Unit

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  44. final def wait(arg0: Long): Unit

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