Packages

class NerDLModel extends AnnotatorModel[NerDLModel] with HasBatchedAnnotate[NerDLModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable with HasEngine

This Named Entity recognition annotator is a generic NER model based on Neural Networks.

Neural Network architecture is Char CNNs - BiLSTM - CRF that achieves state-of-the-art in most datasets.

This is the instantiated model of the NerDLApproach. For training your own model, please see the documentation of that class.

Pretrained models can be loaded with pretrained of the companion object:

val nerModel = NerDLModel.pretrained()
  .setInputCols("sentence", "token", "embeddings")
  .setOutputCol("ner")

The default model is "ner_dl", if no name is provided.

For available pretrained models please see the Models Hub. Additionally, pretrained pipelines are available for this module, see Pipelines.

Note that some pretrained models require specific types of embeddings, depending on which they were trained on. For example, the default model "ner_dl" requires the WordEmbeddings "glove_100d".

For extended examples of usage, see the Spark NLP Workshop and the NerDLSpec.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.annotators.sbd.pragmatic.SentenceDetector
import com.johnsnowlabs.nlp.embeddings.WordEmbeddingsModel
import com.johnsnowlabs.nlp.annotators.ner.dl.NerDLModel
import org.apache.spark.ml.Pipeline

// First extract the prerequisites for the NerDLModel
val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val sentence = new SentenceDetector()
  .setInputCols("document")
  .setOutputCol("sentence")

val tokenizer = new Tokenizer()
  .setInputCols("sentence")
  .setOutputCol("token")

val embeddings = WordEmbeddingsModel.pretrained()
  .setInputCols("sentence", "token")
  .setOutputCol("bert")

// Then NER can be extracted
val nerTagger = NerDLModel.pretrained()
  .setInputCols("sentence", "token", "bert")
  .setOutputCol("ner")

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  sentence,
  tokenizer,
  embeddings,
  nerTagger
))

val data = Seq("U.N. official Ekeus heads for Baghdad.").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("ner.result").show(false)
+------------------------------------+
|result                              |
+------------------------------------+
|[B-ORG, O, O, B-PER, O, O, B-LOC, O]|
+------------------------------------+
See also

NerCrfModel for a generic CRF approach

NerConverter to further process the results

Linear Supertypes
HasEngine, HasStorageRef, WriteTensorflowModel, HasBatchedAnnotate[NerDLModel], AnnotatorModel[NerDLModel], CanBeLazy, RawAnnotator[NerDLModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[NerDLModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. NerDLModel
  2. HasEngine
  3. HasStorageRef
  4. WriteTensorflowModel
  5. HasBatchedAnnotate
  6. AnnotatorModel
  7. CanBeLazy
  8. RawAnnotator
  9. HasOutputAnnotationCol
  10. HasInputAnnotationCols
  11. HasOutputAnnotatorType
  12. ParamsAndFeaturesWritable
  13. HasFeatures
  14. DefaultParamsWritable
  15. MLWritable
  16. Model
  17. Transformer
  18. PipelineStage
  19. Logging
  20. Params
  21. Serializable
  22. Serializable
  23. Identifiable
  24. AnyRef
  25. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new NerDLModel()
  2. new NerDLModel(uid: String)

    uid

    required uid for storing annotator to disk

Type Members

  1. type AnnotationContent = Seq[Row]

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  12. def batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]]

    takes a document and annotations and produces new annotations of this annotator's annotation type

    takes a document and annotations and produces new annotations of this annotator's annotation type

    batchedAnnotations

    Annotations in batches that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every batch of input annotations. Not necessary one to one relationship IMPORTANT: !MUST! return sequences of equal lengths !! IMPORTANT: !MUST! return sentences that belong to the same original row !! (challenging)

    Definition Classes
    NerDLModelHasBatchedAnnotate
  13. def batchProcess(rows: Iterator[_]): Iterator[Row]
    Definition Classes
    HasBatchedAnnotate
  14. val batchSize: IntParam

    Size of every batch (Default depends on model).

    Size of every batch (Default depends on model).

    Definition Classes
    HasBatchedAnnotate
  15. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    NerDLModelAnnotatorModel
  16. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  17. val classes: StringArrayParam
  18. final def clear(param: Param[_]): NerDLModel.this.type
    Definition Classes
    Params
  19. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  20. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  21. def copy(extra: ParamMap): NerDLModel

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  22. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  23. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  24. val datasetParams: StructFeature[DatasetEncoderParams]

    datasetParams

  25. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  26. val engine: Param[String]

    This param is set internally once via loadSavedModel.

    This param is set internally once via loadSavedModel. That's why there is no setter

    Definition Classes
    HasEngine
  27. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  28. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  29. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  30. def explainParams(): String
    Definition Classes
    Params
  31. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  32. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  33. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  34. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  35. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  36. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  37. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  38. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  39. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  40. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  41. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  42. def getBatchSize: Int

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  43. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  44. def getClasses: Array[String]

    get the tags used to trained this NerDLModel

  45. def getConfigProtoBytes: Option[Array[Byte]]

    datasetParams

  46. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  47. def getEngine: String

    Definition Classes
    HasEngine
  48. def getIncludeAllConfidenceScores: Boolean

    whether to include all confidence scores in annotation metadata or just the score of the predicted tag

  49. def getIncludeConfidence: Boolean

    Whether to include confidence scores in annotation metadata

  50. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  51. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  52. def getMinProba: Float

    Minimum probability.

    Minimum probability. Used only if there is no CRF on top of LSTM layer.

  53. def getModelIfNotSet: TensorflowNer

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  54. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  55. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  56. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  57. def getStorageRef: String
    Definition Classes
    HasStorageRef
  58. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  59. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  60. def hasParent: Boolean
    Definition Classes
    Model
  61. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  62. val includeAllConfidenceScores: BooleanParam

    whether to include all confidence scores in annotation metadata or just score of the predicted tag

  63. val includeConfidence: BooleanParam

    Whether to include confidence scores in annotation metadata (Default: false)

  64. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  65. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. val inputAnnotatorTypes: Array[String]

    Input Annotator Types: DOCUMENT, TOKEN, WORD_EMBEDDINGS

    Input Annotator Types: DOCUMENT, TOKEN, WORD_EMBEDDINGS

    Definition Classes
    NerDLModelHasInputAnnotationCols
  67. final val inputCols: StringArrayParam

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  68. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  69. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  70. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  71. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  72. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  73. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  74. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  81. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. val minProba: FloatParam

    Minimum probability.

    Minimum probability. Used only if there is no CRF on top of LSTM layer.

  86. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  87. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  88. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  89. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  90. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    NerDLModelParamsAndFeaturesWritable
  91. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  92. val outputAnnotatorType: String

    Output Annnotator type: NAMED_ENTITY

    Output Annnotator type: NAMED_ENTITY

    Definition Classes
    NerDLModelHasOutputAnnotatorType
  93. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  94. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  95. var parent: Estimator[NerDLModel]
    Definition Classes
    Model
  96. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  97. def set[T](feature: StructFeature[T], value: T): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  98. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  99. def set[T](feature: SetFeature[T], value: Set[T]): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  100. def set[T](feature: ArrayFeature[T], value: Array[T]): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  101. final def set(paramPair: ParamPair[_]): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  102. final def set(param: String, value: Any): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  103. final def set[T](param: Param[T], value: T): NerDLModel.this.type
    Definition Classes
    Params
  104. def setBatchSize(size: Int): NerDLModel.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  105. def setConfigProtoBytes(bytes: Array[Int]): NerDLModel.this.type

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  106. def setDatasetParams(params: DatasetEncoderParams): NerDLModel.this.type

    datasetParams

  107. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  108. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  109. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  110. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  111. final def setDefault(paramPairs: ParamPair[_]*): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  112. final def setDefault[T](param: Param[T], value: T): NerDLModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  113. def setIncludeAllConfidenceScores(value: Boolean): NerDLModel.this.type

    whether to include confidence scores for all tags rather than just for the predicted one

  114. def setIncludeConfidence(value: Boolean): NerDLModel.this.type

    Whether to include confidence scores in annotation metadata

  115. final def setInputCols(value: String*): NerDLModel.this.type
    Definition Classes
    HasInputAnnotationCols
  116. def setInputCols(value: Array[String]): NerDLModel.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  117. def setLazyAnnotator(value: Boolean): NerDLModel.this.type
    Definition Classes
    CanBeLazy
  118. def setMinProbability(minProba: Float): NerDLModel.this.type

    Minimum probability.

    Minimum probability. Used only if there is no CRF on top of LSTM layer.

  119. def setModelIfNotSet(spark: SparkSession, tf: TensorflowWrapper): NerDLModel.this.type
  120. final def setOutputCol(value: String): NerDLModel.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  121. def setParent(parent: Estimator[NerDLModel]): NerDLModel
    Definition Classes
    Model
  122. def setStorageRef(value: String): NerDLModel.this.type
    Definition Classes
    HasStorageRef
  123. val storageRef: Param[String]

    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  124. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  125. def tag(tokenized: Array[Array[WordpieceEmbeddingsSentence]]): Seq[Array[NerTaggedSentence]]
  126. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  127. final def transform(dataset: Dataset[_]): DataFrame

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    dataset

    Dataset[Row]

    Definition Classes
    AnnotatorModel → Transformer
  128. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  129. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  130. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    RawAnnotator → PipelineStage
  131. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  132. val uid: String
    Definition Classes
    NerDLModel → Identifiable
  133. def validate(schema: StructType): Boolean

    takes a Dataset and checks to see if all the required annotation types are present.

    takes a Dataset and checks to see if all the required annotation types are present.

    schema

    to be validated

    returns

    True if all the required types are present, else false

    Attributes
    protected
    Definition Classes
    RawAnnotator
  134. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  135. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  136. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  137. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  138. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  139. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  140. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  141. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  142. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None, savedSignatures: Option[Map[String, String]] = None): Unit
    Definition Classes
    WriteTensorflowModel

Inherited from HasEngine

Inherited from HasStorageRef

Inherited from WriteTensorflowModel

Inherited from HasBatchedAnnotate[NerDLModel]

Inherited from AnnotatorModel[NerDLModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[NerDLModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[NerDLModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters