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)

    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. val c0: Int
  2. val l2: Float
  3. val lossEps: Float
  4. val maxEpochs: Int
  5. val minEpochs: Int
  6. val randomSeed: Option[Int]
  7. val verbose: nlp.annotators.ner.Verbose.Value