Created
April 28, 2022 09:37
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package com.stratio.governance.unstructured.rcn.inference | |
import com.johnsnowlabs.nlp.DocumentAssembler | |
import com.johnsnowlabs.nlp.annotator._ | |
import org.apache.spark.ml.Pipeline | |
import org.apache.spark.sql.SparkSession | |
// Needs one argument, the path to the NER model | |
object NerRcnModelSavePipeline { | |
def main(args: Array[String]): Unit = { | |
val spark:SparkSession= | |
SparkSession. | |
builder().appName("create-pipeline").master("local[4]") | |
.config("spark.driver.memory","6G").config("spark.kryoserializer.buffer.max","200M") | |
.config("spark.serializer","org.apache.spark.serializer.KryoSerializer").getOrCreate() | |
val document = new DocumentAssembler() | |
.setInputCol("text") | |
.setOutputCol("document") | |
val sentence = new SentenceDetector() | |
.setInputCols("document") | |
.setOutputCol("sentence") | |
val token = new Tokenizer() | |
.setInputCols("sentence") | |
.setOutputCol("token") | |
val glove_embeddings = BertEmbeddings.pretrained( | |
"bert_multi_cased", lang = "xx") | |
.setInputCols("document", "token") | |
.setOutputCol("embeddings") | |
val loaded_ner_model = NerDLModel.load(args(0)) | |
.setInputCols("sentence", "token", "embeddings") | |
.setOutputCol("ner") | |
.setIncludeConfidence(true) | |
val converter = new NerConverter() | |
.setInputCols("document", "token", "ner") | |
.setOutputCol("ner_span") | |
val pipeline = new Pipeline() | |
.setStages( | |
Array( | |
document, | |
sentence, | |
token, | |
glove_embeddings, | |
loaded_ner_model, | |
converter | |
) | |
) | |
val text = "Mi nombre es Felipe Alvarez Angulo y mi dirección es Avd Cerro Milano 143" | |
val empty_data = spark.createDataFrame(Seq( | |
(0, text) | |
)).toDF("id","text") | |
val prediction_model = pipeline.fit(empty_data) | |
val pathToModel = args(0) | |
var pathToPipeline = args(0) +"_pipeline" | |
if (pathToModel.substring( | |
pathToModel.length-1, | |
args(0).length).equals("\\")) { | |
pathToPipeline = pathToModel.substring(0,pathToModel.length-1)+"_pipeline" | |
} | |
prediction_model.write.overwrite().save(pathToPipeline) | |
spark.stop() | |
} | |
} |
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