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/*
* 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.pos.perceptron
import com.johnsnowlabs.nlp.annotators.common._
import com.johnsnowlabs.nlp.serialization.StructFeature
import com.johnsnowlabs.nlp._
import org.apache.spark.ml.util.Identifiable
/** Averaged Perceptron model to tag words part-of-speech. Sets a POS tag to each word within a
* sentence.
*
* This is the instantiated model of the
* [[com.johnsnowlabs.nlp.annotators.pos.perceptron.PerceptronApproach PerceptronApproach]]. For
* training your own model, please see the documentation of that class.
*
* Pretrained models can be loaded with `pretrained` of the companion object:
* {{{
* val posTagger = PerceptronModel.pretrained()
* .setInputCols("document", "token")
* .setOutputCol("pos")
* }}}
* The default model is `"pos_anc"`, if no name is provided.
*
* For available pretrained models please see the
* [[https://sparknlp.org/models?task=Part+of+Speech+Tagging Models Hub]]. Additionally,
* pretrained pipelines are available for this module, see
* [[https://sparknlp.org/docs/en/pipelines Pipelines]].
*
* For extended examples of usage, see the
* [[https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/training/french/Train-Perceptron-French.ipynb Examples]].
*
* ==Example==
* {{{
* import spark.implicits._
* import com.johnsnowlabs.nlp.base.DocumentAssembler
* import com.johnsnowlabs.nlp.annotators.Tokenizer
* import com.johnsnowlabs.nlp.annotators.pos.perceptron.PerceptronModel
* import org.apache.spark.ml.Pipeline
*
* val documentAssembler = new DocumentAssembler()
* .setInputCol("text")
* .setOutputCol("document")
*
* val tokenizer = new Tokenizer()
* .setInputCols("document")
* .setOutputCol("token")
*
* val posTagger = PerceptronModel.pretrained()
* .setInputCols("document", "token")
* .setOutputCol("pos")
*
* val pipeline = new Pipeline().setStages(Array(
* documentAssembler,
* tokenizer,
* posTagger
* ))
*
* val data = Seq("Peter Pipers employees are picking pecks of pickled peppers").toDF("text")
* val result = pipeline.fit(data).transform(data)
*
* result.selectExpr("explode(pos) as pos").show(false)
* +-------------------------------------------+
* |pos |
* +-------------------------------------------+
* |[pos, 0, 4, NNP, [word -> Peter], []] |
* |[pos, 6, 11, NNP, [word -> Pipers], []] |
* |[pos, 13, 21, NNS, [word -> employees], []]|
* |[pos, 23, 25, VBP, [word -> are], []] |
* |[pos, 27, 33, VBG, [word -> picking], []] |
* |[pos, 35, 39, NNS, [word -> pecks], []] |
* |[pos, 41, 42, IN, [word -> of], []] |
* |[pos, 44, 50, JJ, [word -> pickled], []] |
* |[pos, 52, 58, NNS, [word -> peppers], []] |
* +-------------------------------------------+
* }}}
*
* @param uid
* Internal constructor requirement for serialization of params
* @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.
*/
class PerceptronModel(override val uid: String)
extends AnnotatorModel[PerceptronModel]
with HasSimpleAnnotate[PerceptronModel]
with PerceptronPredictionUtils {
import com.johnsnowlabs.nlp.AnnotatorType._
/** POS model
*
* @group param
*/
val model: StructFeature[AveragedPerceptron] =
new StructFeature[AveragedPerceptron](this, "POS Model")
/** Output annotator types : POS
*
* @group anno
*/
override val outputAnnotatorType: AnnotatorType = POS
/** Input annotator types : TOKEN, DOCUMENT
*
* @group anno
*/
override val inputAnnotatorTypes: Array[AnnotatorType] = Array(TOKEN, DOCUMENT)
def this() = this(Identifiable.randomUID("POS"))
/** @group getParam */
def getModel: AveragedPerceptron = $$(model)
/** @group setParam */
def setModel(targetModel: AveragedPerceptron): this.type = set(model, targetModel)
/** One to one annotation standing from the Tokens perspective, to give each word a
* corresponding Tag
*/
override def annotate(annotations: Seq[Annotation]): Seq[Annotation] = {
val tokenizedSentences = TokenizedWithSentence.unpack(annotations)
val tagged = tag($$(model), tokenizedSentences.toArray)
PosTagged.pack(tagged)
}
}
trait ReadablePretrainedPerceptron
extends ParamsAndFeaturesReadable[PerceptronModel]
with HasPretrained[PerceptronModel] {
override val defaultModelName = Some("pos_anc")
/** Java compliant-overrides */
override def pretrained(): PerceptronModel = super.pretrained()
override def pretrained(name: String): PerceptronModel = super.pretrained(name)
override def pretrained(name: String, lang: String): PerceptronModel =
super.pretrained(name, lang)
override def pretrained(name: String, lang: String, remoteLoc: String): PerceptronModel =
super.pretrained(name, lang, remoteLoc)
}
/** This is the companion object of [[PerceptronModel]]. Please refer to that class for the
* documentation.
*/
object PerceptronModel extends ReadablePretrainedPerceptron
