class SentenceDetectorDLModel extends AnnotatorModel[SentenceDetectorDLModel] with HasSimpleAnnotate[SentenceDetectorDLModel] with HasStorageRef with ParamsAndFeaturesWritable with WriteTensorflowModel

Annotator that detects sentence boundaries using a deep learning approach.

Instantiated Model of the SentenceDetectorDLApproach. Detects sentence boundaries using a deep learning approach.

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

val sentenceDL = SentenceDetectorDLModel.pretrained()
  .setInputCols("document")
  .setOutputCol("sentencesDL")

The default model is "sentence_detector_dl", if no name is provided. For available pretrained models please see the Models Hub.

Each extracted sentence can be returned in an Array or exploded to separate rows, if explodeSentences is set to true.

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

Example

In this example, the normal SentenceDetector is compared to the SentenceDetectorDLModel. In a pipeline, SentenceDetectorDLModel can be used as a replacement for the SentenceDetector.

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.SentenceDetector
import com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLModel
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

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

val sentenceDL = SentenceDetectorDLModel
  .pretrained("sentence_detector_dl", "en")
  .setInputCols("document")
  .setOutputCol("sentencesDL")

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  sentence,
  sentenceDL
))

val data = Seq("""John loves Mary.Mary loves Peter
  Peter loves Helen .Helen loves John;
  Total: four people involved.""").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(sentences.result) as sentences").show(false)
+----------------------------------------------------------+
|sentences                                                 |
+----------------------------------------------------------+
|John loves Mary.Mary loves Peter\n     Peter loves Helen .|
|Helen loves John;                                         |
|Total: four people involved.                              |
+----------------------------------------------------------+

result.selectExpr("explode(sentencesDL.result) as sentencesDL").show(false)
+----------------------------+
|sentencesDL                 |
+----------------------------+
|John loves Mary.            |
|Mary loves Peter            |
|Peter loves Helen .         |
|Helen loves John;           |
|Total: four people involved.|
+----------------------------+
See also

SentenceDetectorDLApproach for training a model yourself

SentenceDetector for non deep learning extraction

Linear Supertypes
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. SentenceDetectorDLModel
  2. WriteTensorflowModel
  3. HasStorageRef
  4. HasSimpleAnnotate
  5. AnnotatorModel
  6. CanBeLazy
  7. RawAnnotator
  8. HasOutputAnnotationCol
  9. HasInputAnnotationCols
  10. HasOutputAnnotatorType
  11. ParamsAndFeaturesWritable
  12. HasFeatures
  13. DefaultParamsWritable
  14. MLWritable
  15. Model
  16. Transformer
  17. PipelineStage
  18. Logging
  19. Params
  20. Serializable
  21. Serializable
  22. Identifiable
  23. AnyRef
  24. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new SentenceDetectorDLModel()
  2. new SentenceDetectorDLModel(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
    SentenceDetectorDLModelAnnotatorModel
  11. def annotate(annotations: Seq[Annotation]): 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

    annotations

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    SentenceDetectorDLModelHasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  14. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  15. final def clear(param: Param[_]): SentenceDetectorDLModel.this.type
    Definition Classes
    Params
  16. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  17. def copy(extra: ParamMap): SentenceDetectorDLModel

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  18. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  19. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  20. val customBounds: StringArrayParam

    characters used to explicitly mark sentence bounds

  21. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  22. def dfAnnotate: UserDefinedFunction

    Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column

    Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column

    returns

    udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation

    Definition Classes
    HasSimpleAnnotate
  23. var encoder: SentenceDetectorDLEncoderParam
  24. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  25. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  26. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  27. def explainParams(): String
    Definition Classes
    Params
  28. def explodeSentences: BooleanParam

    A flag indicating whether to split sentences into different Dataset rows.

    A flag indicating whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows (Default: false)

  29. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  30. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  31. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  32. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  33. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  34. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  35. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  36. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  37. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  38. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  39. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  40. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  41. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  42. def getEncoder: SentenceDetectorDLEncoder
  43. def getExplodeSentences: Boolean

    Whether to split sentences into different Dataset rows.

    Whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.

  44. def getImpossiblePenultimates: Array[String]

    Get impossible penultimates

  45. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  46. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  47. def getMetrics(text: String, injectNewLines: Boolean = false): Metrics
  48. def getModel: String

    Get model architecture

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  51. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  52. def getStorageRef: String
    Definition Classes
    HasStorageRef
  53. def getTFClassifier: TensorflowSentenceDetectorDL
  54. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  55. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  56. def hasParent: Boolean
    Definition Classes
    Model
  57. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  58. val impossiblePenultimates: StringArrayParam

    Impossible penultimates (Default: Array())

  59. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  60. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. val inputAnnotatorTypes: Array[AnnotatorType]

    Output annotator type : DOCUMENT

    Output annotator type : DOCUMENT

    Definition Classes
    SentenceDetectorDLModelHasInputAnnotationCols
  62. 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
  63. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  64. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  65. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  66. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  67. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  68. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  69. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  76. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. var modelArchitecture: Param[String]

    Model architecture (Default: "cnn")

  81. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  82. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  83. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  84. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  85. def onWrite(path: String, spark: SparkSession): Unit
  86. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  87. val outputAnnotatorType: String

    Output annotator type : DOCUMENT

    Output annotator type : DOCUMENT

    Definition Classes
    SentenceDetectorDLModelHasOutputAnnotatorType
  88. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  89. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  90. var parent: Estimator[SentenceDetectorDLModel]
    Definition Classes
    Model
  91. def processText(text: String): Iterator[(Int, Int, String)]
  92. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  93. def set[T](feature: StructFeature[T], value: T): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. def set[T](feature: SetFeature[T], value: Set[T]): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  96. def set[T](feature: ArrayFeature[T], value: Array[T]): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  97. final def set(paramPair: ParamPair[_]): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  98. final def set(param: String, value: Any): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  99. final def set[T](param: Param[T], value: T): SentenceDetectorDLModel.this.type
    Definition Classes
    Params
  100. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  101. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  104. final def setDefault(paramPairs: ParamPair[_]*): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  105. final def setDefault[T](param: Param[T], value: T): SentenceDetectorDLModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  106. def setEncoder(encoder: SentenceDetectorDLEncoder): SentenceDetectorDLModel.this.type
  107. def setExplodeSentences(value: Boolean): SentenceDetectorDLModel.this.type

    Whether to split sentences into different Dataset rows.

    Whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.

  108. def setImpossiblePenultimates(impossiblePenultimates: Array[String]): SentenceDetectorDLModel.this.type

    Set impossible penultimates

  109. final def setInputCols(value: String*): SentenceDetectorDLModel.this.type
    Definition Classes
    HasInputAnnotationCols
  110. final def setInputCols(value: Array[String]): SentenceDetectorDLModel.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  111. def setLazyAnnotator(value: Boolean): SentenceDetectorDLModel.this.type
    Definition Classes
    CanBeLazy
  112. def setModel(modelArchitecture: String): SentenceDetectorDLModel.this.type

    Set architecture

  113. final def setOutputCol(value: String): SentenceDetectorDLModel.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  114. def setParent(parent: Estimator[SentenceDetectorDLModel]): SentenceDetectorDLModel
    Definition Classes
    Model
  115. def setStorageRef(value: String): SentenceDetectorDLModel.this.type
    Definition Classes
    HasStorageRef
  116. def setupNew(spark: SparkSession, modelPath: String, vocabularyPath: String): SentenceDetectorDLModel.this.type
  117. def setupTFClassifier(spark: SparkSession, tfWrapper: TensorflowWrapper): SentenceDetectorDLModel.this.type
  118. val storageRef: Param[String]

    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  119. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  120. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  121. 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
  122. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  123. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  124. 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
  125. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  126. val uid: String
    Definition Classes
    SentenceDetectorDLModel → Identifiable
  127. val useCustomBoundsOnly: BooleanParam

    whether to only utilize custom bounds for sentence detection

  128. 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
  129. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  130. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  131. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  132. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  133. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  134. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  135. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  136. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  137. 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 WriteTensorflowModel

Inherited from HasStorageRef

Inherited from CanBeLazy

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[SentenceDetectorDLModel]

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