o
com.johnsnowlabs.nlp.annotators.classifier.dl
MultiClassifierDLModel
Companion class MultiClassifierDLModel
object MultiClassifierDLModel extends ReadablePretrainedMultiClassifierDL with ReadMultiClassifierDLTensorflowModel with Serializable
This is the companion object of MultiClassifierDLModel. Please refer to that class for the documentation.
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- MultiClassifierDLModel
- Serializable
- Serializable
- ReadMultiClassifierDLTensorflowModel
- ReadTensorflowModel
- ReadablePretrainedMultiClassifierDL
- HasPretrained
- ParamsAndFeaturesReadable
- DefaultParamsReadable
- MLReadable
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def
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final
def
==(arg0: Any): Boolean
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def
addReader(reader: (MultiClassifierDLModel, String, SparkSession) ⇒ Unit): Unit
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final
def
asInstanceOf[T0]: T0
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def
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val
defaultLang: String
- Definition Classes
- HasPretrained
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lazy val
defaultLoc: String
- Definition Classes
- HasPretrained
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val
defaultModelName: Some[String]
- Definition Classes
- ReadablePretrainedMultiClassifierDL → HasPretrained
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val
defaultPreferredEngine: String
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- HasPretrained
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final
def
isInstanceOf[T0]: Boolean
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def
load(path: String): MultiClassifierDLModel
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def
notifyAll(): Unit
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def
onRead(instance: MultiClassifierDLModel, path: String, session: SparkSession): Unit
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def
pretrained(name: String, lang: String): MultiClassifierDLModel
- Definition Classes
- ReadablePretrainedMultiClassifierDL → HasPretrained
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def
pretrained(name: String): MultiClassifierDLModel
- Definition Classes
- ReadablePretrainedMultiClassifierDL → HasPretrained
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def
pretrained(): MultiClassifierDLModel
Java compliant-overrides
Java compliant-overrides
- Definition Classes
- ReadablePretrainedMultiClassifierDL → HasPretrained
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def
pretrained(name: String, lang: String, remoteLoc: String): MultiClassifierDLModel
- Definition Classes
- ReadablePretrainedMultiClassifierDL → HasPretrained
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def
pretrained(name: String, lang: String, remoteLoc: String, preferredEngine: String = "onnx"): MultiClassifierDLModel
Java default argument interoperability
Java default argument interoperability
- Definition Classes
- HasPretrained
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def
pretrainedEngine(name: String, lang: String, preferredEngine: String): MultiClassifierDLModel
- Definition Classes
- HasPretrained
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def
pretrainedEngine(name: String, preferredEngine: String): MultiClassifierDLModel
- Definition Classes
- HasPretrained
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def
read: MLReader[MultiClassifierDLModel]
- Definition Classes
- ParamsAndFeaturesReadable → DefaultParamsReadable → MLReadable
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def
readModel(instance: MultiClassifierDLModel, path: String, spark: SparkSession): Unit
- Definition Classes
- ReadMultiClassifierDLTensorflowModel
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def
readTensorflowChkPoints(path: String, spark: SparkSession, suffix: String, zipped: Boolean = true, tags: Array[String] = Array.empty, initAllTables: Boolean = false): TensorflowWrapper
- Definition Classes
- ReadTensorflowModel
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def
readTensorflowHub(path: String, spark: SparkSession, suffix: String, zipped: Boolean = true, useBundle: Boolean = false, tags: Array[String] = Array.empty): TensorflowWrapper
- Definition Classes
- ReadTensorflowModel
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def
readTensorflowModel(path: String, spark: SparkSession, suffix: String, zipped: Boolean = true, useBundle: Boolean = false, tags: Array[String] = Array.empty, initAllTables: Boolean = false, savedSignatures: Option[Map[String, String]] = None): TensorflowWrapper
- Definition Classes
- ReadTensorflowModel
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def
readTensorflowWithSPModel(path: String, spark: SparkSession, suffix: String, zipped: Boolean = true, useBundle: Boolean = false, tags: Array[String] = Array.empty, initAllTables: Boolean = false, loadSP: Boolean = false): TensorflowWrapper
- Definition Classes
- ReadTensorflowModel
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val
readers: ArrayBuffer[(MultiClassifierDLModel, String, SparkSession) ⇒ Unit]
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- protected
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- ParamsAndFeaturesReadable
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val
skipPreferredEngine: Boolean
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- HasPretrained
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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val
tfFile: String
- Definition Classes
- ReadMultiClassifierDLTensorflowModel → ReadTensorflowModel
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def
toString(): String
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final
def
wait(): Unit
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