class NerBatch extends AnyRef

Batch that contains data in Tensorflow input format.

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Instance Constructors

  1. new NerBatch(wordEmbeddings: Array[Array[Array[Float]]], charIds: Array[Array[Array[Int]]], wordLengths: Array[Array[Int]], sentenceLengths: Array[Int], maxLength: Int, isWordStart: Array[Array[Boolean]])

    wordEmbeddings

    Word vector representation. Shape: Batch x Max Sentence Length x Embeddings Dim

    charIds

    Char ids for every word in every sentence. Shape: Batch x Max Sentence Length x Max Word length

    wordLengths

    Word Length of every sentence. Shape: Batch x Max Sentence Length

    sentenceLengths

    Length of every batch sentence. Shape: Batch

    maxLength

    Max length of sentence

    isWordStart

    Is current wordpiece is token start? Shape: Batch x Max Sentence Length

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  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 batchSize: Int
  6. val charIds: Array[Array[Array[Int]]]
  7. def clone(): AnyRef
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  11. final def getClass(): Class[_]
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  12. def hashCode(): Int
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  13. final def isInstanceOf[T0]: Boolean
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  14. val isWordStart: Array[Array[Boolean]]
  15. val maxLength: Int
  16. final def ne(arg0: AnyRef): Boolean
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  17. final def notify(): Unit
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  18. final def notifyAll(): Unit
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  19. val sentenceLengths: Array[Int]
  20. final def synchronized[T0](arg0: ⇒ T0): T0
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  21. def toString(): String
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  22. final def wait(): Unit
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  25. val wordEmbeddings: Array[Array[Array[Float]]]
  26. val wordLengths: Array[Array[Int]]

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