Packages

class NerCrfModel extends AnnotatorModel[NerCrfModel] with HasSimpleAnnotate[NerCrfModel] with HasStorageRef

Extracts Named Entities based on a CRF Model.

This Named Entity recognition annotator allows for a generic model to be trained by utilizing a CRF machine learning algorithm. The data should have columns of type DOCUMENT, TOKEN, POS, WORD_EMBEDDINGS. These can be extracted with for example

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

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

val nerTagger = NerCrfModel.pretrained()
  .setInputCols("sentence", "token", "word_embeddings", "pos")
  .setOutputCol("ner"

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

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

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.pos.perceptron.PerceptronModel
import com.johnsnowlabs.nlp.annotators.ner.crf.NerCrfModel
import org.apache.spark.ml.Pipeline

// First extract the prerequisites for the NerCrfModel
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("word_embeddings")

val posTagger = PerceptronModel.pretrained()
  .setInputCols("sentence", "token")
  .setOutputCol("pos")

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

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  sentence,
  tokenizer,
  embeddings,
  posTagger,
  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                              |
+------------------------------------+
|[I-ORG, O, O, I-PER, O, O, I-LOC, O]|
+------------------------------------+
See also

NerDLModel for a deep learning based approach

NerConverter to further process the results

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

Instance Constructors

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

    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. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  21. 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
  22. val dictionaryFeatures: MapFeature[String, String]

    Additional dictionary to use as for features (Default: Map.empty[String, String])

  23. val entities: StringArrayParam

    List of Entities to recognize

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

  42. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  43. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  44. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  45. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  46. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  47. def getStorageRef: String
    Definition Classes
    HasStorageRef
  48. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  49. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  50. def hasParent: Boolean
    Definition Classes
    Model
  51. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  52. val includeConfidence: BooleanParam

    Whether or not to calculate prediction confidence by token, included in metadata (Default: false)

  53. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  54. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. val inputAnnotatorTypes: Array[String]

    Input Annotator Types: DOCUMENT, TOKEN, POS, WORD_EMBEDDINGS

    Input Annotator Types: DOCUMENT, TOKEN, POS, WORD_EMBEDDINGS

    Definition Classes
    NerCrfModelHasInputAnnotationCols
  56. 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
  57. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  58. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  59. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  60. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  61. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  62. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  63. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  70. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. val model: StructFeature[LinearChainCrfModel]

    The CRF model

  75. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  76. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  77. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  78. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  79. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  80. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  81. val outputAnnotatorType: AnnotatorType

    Output Annotator Types: NAMED_ENTITY

    Output Annotator Types: NAMED_ENTITY

    Definition Classes
    NerCrfModelHasOutputAnnotatorType
  82. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  83. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  84. var parent: Estimator[NerCrfModel]
    Definition Classes
    Model
  85. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  86. def set[T](feature: StructFeature[T], value: T): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  87. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  88. def set[T](feature: SetFeature[T], value: Set[T]): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  89. def set[T](feature: ArrayFeature[T], value: Array[T]): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  90. final def set(paramPair: ParamPair[_]): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  91. final def set(param: String, value: Any): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  92. final def set[T](param: Param[T], value: T): NerCrfModel.this.type
    Definition Classes
    Params
  93. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  96. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  97. final def setDefault(paramPairs: ParamPair[_]*): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  98. final def setDefault[T](param: Param[T], value: T): NerCrfModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  99. def setDictionaryFeatures(dictFeatures: DictionaryFeatures): NerCrfModel.this.type

  100. def setEntities(toExtract: Array[String]): NerCrfModel

  101. def setIncludeConfidence(c: Boolean): NerCrfModel.this.type

  102. final def setInputCols(value: String*): NerCrfModel.this.type
    Definition Classes
    HasInputAnnotationCols
  103. final def setInputCols(value: Array[String]): NerCrfModel.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  104. def setLazyAnnotator(value: Boolean): NerCrfModel.this.type
    Definition Classes
    CanBeLazy
  105. def setModel(crf: LinearChainCrfModel): NerCrfModel

  106. final def setOutputCol(value: String): NerCrfModel.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  107. def setParent(parent: Estimator[NerCrfModel]): NerCrfModel
    Definition Classes
    Model
  108. def setStorageRef(value: String): NerCrfModel.this.type
    Definition Classes
    HasStorageRef
  109. def shrink(minW: Float): NerCrfModel
  110. val storageRef: Param[String]

    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  111. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  112. def tag(sentences: Seq[(PosTaggedSentence, WordpieceEmbeddingsSentence)]): Seq[NerTaggedSentence]

    Predicts Named Entities in input sentences

    Predicts Named Entities in input sentences

    sentences

    POS tagged and WordpieceEmbeddings sentences

    returns

    sentences with recognized Named Entities

  113. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  114. 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
  115. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  116. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  117. 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
  118. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  119. val uid: String
    Definition Classes
    NerCrfModel → Identifiable
  120. 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
  121. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  122. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  123. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  124. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  125. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  126. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from HasStorageRef

Inherited from HasSimpleAnnotate[NerCrfModel]

Inherited from AnnotatorModel[NerCrfModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[NerCrfModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[NerCrfModel]

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