ModelWithWordEmbeddings

abstract def annotate(annotations: Seq[Annotation]): Seq[Annotation]

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

abstract val annotatorType: AnnotatorType

abstract val requiredAnnotatorTypes: Array[String]

Annotator reference id.

abstract val uid: String

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

def $$[T](feature: StructFeature[T]): T

def $$[K, V](feature: MapFeature[K, V]): Map[K, V]

def $$[T](feature: ArrayFeature[T]): Array[T]

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final def clear(param: Param[_]): ModelWithWordEmbeddings.this.type

def clone(): AnyRef

def close(): Unit

def copy(extra: ParamMap): M

requirement for annotators copies

def copyValues[T <: Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: Params](extra: ParamMap): T

def deserializeEmbeddings(path: String, spark: SparkContext): Unit

lazy val embeddings: Option[WordEmbeddings]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

val features: ArrayBuffer[Feature[_, _, _]]

def finalize(): Unit

def get[T](feature: StructFeature[T]): Option[T]

def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]

def get[T](feature: ArrayFeature[T]): Option[Array[T]]

final def get[T](param: Param[T]): Option[T]

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

def getEmbeddingsSerializedPath(path: String): Path

final def getInputCols: Array[String]

final def getOrDefault[T](param: Param[T]): T

final def getOutputCol: String

Gets annotation column name going to generate

def getParam(paramName: String): Param[Any]

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hashCode(): Int

val indexPath: Param[String]

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

def moveFolderFiles(folderSrc: String, folderDst: String): Unit

val nDims: IntParam

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def onWritten(path: String, spark: SparkSession): Unit

final val outputCol: Param[String]

lazy val params: Array[Param[_]]

var parent: Estimator[M]

def save(path: String): Unit

def serializeEmbeddings(path: String, spark: SparkContext): Unit

def set[T](feature: StructFeature[T], value: T): ModelWithWordEmbeddings.this.type

def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): ModelWithWordEmbeddings.this.type

def set[T](feature: ArrayFeature[T], value: Array[T]): ModelWithWordEmbeddings.this.type

final def set(paramPair: ParamPair[_]): ModelWithWordEmbeddings.this.type

final def set(param: String, value: Any): ModelWithWordEmbeddings.this.type

final def set[T](param: Param[T], value: T): ModelWithWordEmbeddings.this.type

def setDefault[T](feature: StructFeature[T], value: () ⇒ T): ModelWithWordEmbeddings.this.type

def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): ModelWithWordEmbeddings.this.type

def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): ModelWithWordEmbeddings.this.type

final def setDefault(paramPairs: ParamPair[_]*): ModelWithWordEmbeddings.this.type

final def setDefault[T](param: Param[T], value: T): ModelWithWordEmbeddings.this.type

def setDims(nDims: Int): ModelWithWordEmbeddings.this.type

def setIndexPath(path: String): ModelWithWordEmbeddings.this.type

final def setInputCols(value: String*): ModelWithWordEmbeddings.this.type

final def setInputCols(value: Array[String]): ModelWithWordEmbeddings.this.type

Overrides required annotators column if different than default

final def setOutputCol(value: String): ModelWithWordEmbeddings.this.type

Overrides annotation column name when transforming

def setParent(parent: Estimator[M]): M

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

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

def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

final def transformSchema(schema: StructType): StructType

requirement for pipeline transformation validation.

def transformSchema(schema: StructType, logging: Boolean): StructType

def validate(schema: StructType): Boolean

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

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

def write: MLWriter