Class

com.johnsnowlabs.ml.crf

CrfParams

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case class CrfParams(minEpochs: Int = 10, maxEpochs: Int = 1000, l2: Float = 1f, c0: Int = 1500000, lossEps: Float = 1e-4f, randomSeed: Option[Int] = None, verbose: nlp.annotators.ner.Verbose.Value = Verbose.Silent) extends Product with Serializable

Hyper Parameters and Setting for LinearChainCrf training

minEpochs

- Minimum number of epochs to train

maxEpochs

- Maximum number of epochs to train

l2

- l2 regularization coefficient

c0

- Initial number of steps in decay strategy

lossEps

- If loss after a SGD epochs haven't improved (absolutely) more than lossEps, then training is stopped

randomSeed

- Seed for random

verbose

- Level of verbosity during training procedure

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Instance Constructors

  1. new CrfParams(minEpochs: Int = 10, maxEpochs: Int = 1000, l2: Float = 1f, c0: Int = 1500000, lossEps: Float = 1e-4f, randomSeed: Option[Int] = None, verbose: nlp.annotators.ner.Verbose.Value = Verbose.Silent)

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    minEpochs

    - Minimum number of epochs to train

    maxEpochs

    - Maximum number of epochs to train

    l2

    - l2 regularization coefficient

    c0

    - Initial number of steps in decay strategy

    lossEps

    - If loss after a SGD epochs haven't improved (absolutely) more than lossEps, then training is stopped

    randomSeed

    - Seed for random

    verbose

    - Level of verbosity during training procedure

Value Members

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

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  2. final def ##(): Int

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  4. final def asInstanceOf[T0]: T0

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  5. val c0: Int

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    - Initial number of steps in decay strategy

  6. def clone(): AnyRef

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  10. final def isInstanceOf[T0]: Boolean

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  11. val l2: Float

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    - l2 regularization coefficient

  12. val lossEps: Float

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    - If loss after a SGD epochs haven't improved (absolutely) more than lossEps, then training is stopped

  13. val maxEpochs: Int

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    - Maximum number of epochs to train

  14. val minEpochs: Int

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    - Minimum number of epochs to train

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. val randomSeed: Option[Int]

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    - Seed for random

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

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  20. val verbose: nlp.annotators.ner.Verbose.Value

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    - Level of verbosity during training procedure

  21. final def wait(): Unit

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

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

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