TextClassifier

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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def apply[T](classNum: Int, embeddingFile: String, wordIndex: Map[String, Int] = null, sequenceLength: Int = 500, encoder: String = "cnn", encoderOutputDim: Int = 256)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): TextClassifier[T]

The factory method to create a TextClassifier instance with WordEmbedding as its first layer.
The factory method to create a TextClassifier instance with WordEmbedding as its first layer.
T
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
classNum
The number of text categories to be classified. Positive integer.
embeddingFile
The path to the word embedding file. Currently only the following GloVe files are supported: "glove.6B.50d.txt", "glove.6B.100d.txt", "glove.6B.200d.txt", "glove.6B.300d.txt", "glove.42B.300d.txt", "glove.840B.300d.txt". You can download from: https://nlp.stanford.edu/projects/glove/.
wordIndex
Map of word (String) and its corresponding index (integer). The index is supposed to start from 1 with 0 reserved for unknown words. During the prediction, if you have words that are not in the wordIndex for the training, you can map them to index 0. Default is null. In this case, all the words in the embeddingFile will be taken into account and you can call WordEmbedding.getWordIndex(embeddingFile) to retrieve the map.
sequenceLength
The length of a sequence. Positive integer. Default is 500.
encoder
The encoder for input sequences. String. "cnn" or "lstm" or "gru" are supported. Default is "cnn".
encoderOutputDim
The output dimension for the encoder. Positive integer. Default is 256.
final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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protected[java.lang]
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final def eq(arg0: AnyRef): Boolean

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def finalize(): Unit

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final def getClass(): Class[_]

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final def isInstanceOf[T0]: Boolean

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def loadModel[T](path: String, weightPath: String = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): TextClassifier[T]

Load an existing TextClassifier model (with weights).
Load an existing TextClassifier model (with weights).
T
Numeric type of parameter(e.g. weight, bias). Only support float/double now.
path
The path for the pre-defined model. Local file system, HDFS and Amazon S3 are supported. HDFS path should be like "hdfs://[host]:[port]/xxx". Amazon S3 path should be like "s3a://bucket/xxx".
weightPath
The path for pre-trained weights if any. Default is null.
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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

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final def wait(): Unit

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Deprecated Value Members

def apply[T](classNum: Int, tokenLength: Int, sequenceLength: Int, encoder: String, encoderOutputDim: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): TextClassifier[T]

The factory method to create a TextClassifier instance that takes word vectors as input.
The factory method to create a TextClassifier instance that takes word vectors as input.

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@deprecated
Deprecated

Related Docs: class TextClassifier | package textclassification

object TextClassifier extends Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def apply[T](classNum: Int, embeddingFile: String, wordIndex: Map[String, Int] = null, sequenceLength: Int = 500, encoder: String = "cnn", encoderOutputDim: Int = 256)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): TextClassifier[T]

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def loadModel[T](path: String, weightPath: String = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): TextClassifier[T]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Deprecated Value Members

def apply[T](classNum: Int, tokenLength: Int, sequenceLength: Int, encoder: String, encoderOutputDim: Int)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): TextClassifier[T]

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Inherited from Serializable

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