trait IAnnotation extends AnyRef
IAnnotation trait is used to abstract the annotator's output for each NLP tasks available in Spark NLP.
Currently Spark NLP supports three types of outputs:
- Text Output: com.johnsnowlabs.nlp.Annotation
- Image Output: com.johnsnowlabs.nlp.AnnotationImage
- Audio Output: com.johnsnowlabs.nlp.AnnotationAudio
LightPipeline models in Java/Scala returns an IAnnotation collection. All of these outputs are structs with the required data types to represent Text, Image and Audio.
If one wants to access the data as Annotation, AnnotationImage or AnnotationAudio, one just needs casting to the desired output.
Example
import com.johnsnowlabs.nlp.annotators.cv.ViTForImageClassification import org.apache.spark.ml.Pipeline import com.johnsnowlabs.nlp.annotators.Tokenizer import com.johnsnowlabs.nlp.annotators.sbd.pragmatic.SentenceDetector import com.johnsnowlabs.nlp.ImageAssembler import com.johnsnowlabs.nlp.LightPipeline import com.johnsnowlabs.util.PipelineModels val imageDf = spark.read .format("image") .option("dropInvalid", value = true) .load("./images") val imageAssembler = new ImageAssembler() .setInputCol("image") .setOutputCol("image_assembler") val imageClassifier = ViTForImageClassification .pretrained() .setInputCols("image_assembler") .setOutputCol("class") val pipeline: Pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier)) val vitModel = pipeline.fit(imageDf) val lightPipeline = new LightPipeline(vitModel) val predictions = lightPipeline.fullAnnotate("./images/hen.JPEG") val result = predictions.flatMap(prediction => prediction._2.map { case annotationText: Annotation => annotationText case annotationImage: AnnotationImage => annotationImage })
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- abstract def annotatorType: String
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