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- def charsToIntegers(text: String, chars: Map[Char, Int]): Vector[Int]
- def charsToIntegers(text: String): (Map[Char, Int], Vector[Int])
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def
convertIntegersToText(tensor: STen, vocab: Map[Int, Char]): Seq[String]
Convert back to text.
Convert back to text. Tensor shape: time x batch
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def
convertLogitsToText(tensor: STen, vocab: Map[Int, Char])(implicit scope: Scope): Seq[String]
Convert back to text.
Convert back to text. Tensor shape: time x batch x dim
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- def makePredictionBatch(examples: Seq[Vector[Long]], device: Device)(implicit scope: Scope): Variable
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def
minibatchesForSeq2Seq(text: IndexedSeq[(Vector[Long], Vector[Long])], minibatchSize: Int, timeSteps: Int, pad: Long, rng: Random): BatchStream[(Variable, Variable), Int]
Yields tensors of shape (time step x batch size)
Yields tensors of shape (time step x batch size)
- text
pairs of (source,destination) units
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def
minibatchesFromText(text: Vector[Int], minibatchSize: Int, timeSteps: Int, rng: Random): BatchStream[Constant, Int]
Yields tensors of shape (time step x batch size)
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- def sentencesToPaddedMatrix(sentences: Seq[String], maxLength: Int, pad: Int, vocabulary: Map[Char, Int]): Seq[Array[Int]]
- def sequencePrediction[T, M <: StatefulModule[Variable, Variable, T]](batch: Seq[Vector[Long]], device: Device, module: M with StatefulModule[Variable, Variable, T], steps: Int)(implicit is: InitState[M, T], scope: Scope): STen
- def sequencePredictionBeam[T, M <: StatefulModule[Variable, Variable, T]](prefix: Vector[Long], device: Device, module: M with StatefulModule[Variable, Variable, T], steps: Int, startSequence: Int, endOfSequence: Int)(implicit is: InitState[M, T], scope: Scope): Seq[(STen, Double)]
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- def wordsToIntegers(text: String, minimumTokenId: Int, minimumFrequency: Int): (Array[Int], Map[String, Int])
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