com.johnsnowlabs.nlp.annotators.ner.NerApproach.scala Maven / Gradle / Ivy
/*
* Copyright 2017-2022 John Snow Labs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.johnsnowlabs.nlp.annotators.ner
import org.apache.spark.ml.param.{IntParam, Param, Params, StringArrayParam}
/** @groupname anno Annotator types
* @groupdesc anno
* Required input and expected output annotator types
* @groupname Ungrouped Members
* @groupname param Parameters
* @groupname setParam Parameter setters
* @groupname getParam Parameter getters
* @groupname Ungrouped Members
* @groupprio param 1
* @groupprio anno 2
* @groupprio Ungrouped 3
* @groupprio setParam 4
* @groupprio getParam 5
* @groupdesc param
* A list of (hyper-)parameter keys this annotator can take. Users can set and get the
* parameter values through setters and getters, respectively.
*/
trait NerApproach[T <: NerApproach[_]] extends Params {
/** Column with label per each token
*
* @group param
*/
val labelColumn = new Param[String](this, "labelColumn", "Column with label per each token")
/** Entities to recognize
*
* @group param
*/
val entities = new StringArrayParam(this, "entities", "Entities to recognize")
/** Minimum number of epochs to train
*
* @group param
*/
val minEpochs = new IntParam(this, "minEpochs", "Minimum number of epochs to train")
/** Maximum number of epochs to train
*
* @group param
*/
val maxEpochs = new IntParam(this, "maxEpochs", "Maximum number of epochs to train")
/** Random seed
*
* @group param
*/
val randomSeed = new IntParam(this, "randomSeed", "Random seed")
/** Column with label per each token
*
* @group setParam
*/
def setLabelColumn(column: String): T = set(labelColumn, column).asInstanceOf[T]
/** Entities to recognize
*
* @group setParam
*/
def setEntities(tags: Array[String]): T = set(entities, tags).asInstanceOf[T]
/** Minimum number of epochs to train
*
* @group setParam
*/
def setMinEpochs(epochs: Int): T = set(minEpochs, epochs).asInstanceOf[T]
/** Maximum number of epochs to train
*
* @group setParam
*/
def setMaxEpochs(epochs: Int): T = set(maxEpochs, epochs).asInstanceOf[T]
/** Random seed
*
* @group setParam
*/
def setRandomSeed(seed: Int): T = set(randomSeed, seed).asInstanceOf[T]
/** Minimum number of epochs to train
*
* @group getParam
*/
def getMinEpochs: Int = $(minEpochs)
/** Maximum number of epochs to train
*
* @group getParam
*/
def getMaxEpochs: Int = $(maxEpochs)
/** Random seed
*
* @group getParam
*/
def getRandomSeed: Int = $(randomSeed)
}
object Verbose extends Enumeration {
type Level = Value
val All: Verbose.Value = Value(0)
val PerStep: Verbose.Value = Value(1)
val Epochs: Verbose.Value = Value(2)
val TrainingStat: Verbose.Value = Value(3)
val Silent: Verbose.Value = Value(4)
}
object ModelMetrics {
val testMicroF1 = "test_micro_f1"
val testMacroF1 = "test_macro_f1"
val valMicroF1 = "val_micro_f1"
val valMacroF1 = "val_macro_f1"
val loss = "loss"
val values: Set[String] =
Set(testMicroF1, testMacroF1, valMicroF1, valMacroF1, loss)
}