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com.intel.analytics.bigdl.example.textclassification

TextClassifier

Related Docs: object TextClassifier | package textclassification

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class TextClassifier extends AnyRef

This example use a (pre-trained GloVe embedding) to convert word to vector, and uses it to train a text classification model on the 20 Newsgroup dataset with 20 different categories. This model can achieve around 90% accuracy after 2 epochs training.

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Instance Constructors

  1. new TextClassifier(param: TextClassificationParams)

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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. def analyzeTexts(dataRdd: RDD[(String, Float)]): (Map[String, WordMeta], Map[Float, Array[Float]])

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    Go through the whole data set to gather some meta info for the tokens.

    Go through the whole data set to gather some meta info for the tokens. Tokens would be discarded if the frequency ranking is less then maxWordsNum

  5. final def asInstanceOf[T0]: T0

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  6. def buildModel(classNum: Int): Sequential[Float]

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  7. var classNum: Int

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  8. def clone(): AnyRef

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

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  11. def finalize(): Unit

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

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  13. val gloveDir: String

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. val log: Logger

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

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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

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  21. val textDataDir: String

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  22. def toString(): String

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  23. def train(): Unit

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  24. final def wait(): Unit

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

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

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