Relocating public examples back to the main repository (#13292)

* First init of relocating examples

* Add Scala examples

* Rename offline notebook

* Fix a bad import

* remove outdated example

* Create text, image, and audio categories

* Example codes for configs should be language agnostic

example/README.md

@@ -1,5 +1,10 @@
 # Spark NLP Examples
 
-We have moved all the examples to a repository dedicated to showcasing Spark NLP use cases!
+Under construction
 
-Let's go! [spark-nlp-workshop](https://github.com/JohnSnowLabs/spark-nlp-workshop)
+Required maintained examples
+
+- Python
+- Scala
+- Java
+- Docker

example/java/annotation/AnnotationExamples.java

@@ -0,0 +1,78 @@
+package com.johnsnowlabs.nlp;
+
+import com.johnsnowlabs.nlp.annotators.LemmatizerModel;
+import com.johnsnowlabs.nlp.annotators.Tokenizer;
+import com.johnsnowlabs.nlp.embeddings.EmbeddingsHelper;
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline;
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Encoders;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SparkSession;
+
+import java.util.LinkedList;
+
+public class AnnotationExamples {
+
+    public static void main(String args[]) {
+
+        DocumentAssembler document = new DocumentAssembler();
+        document.setInputCol("text");
+        document.setOutputCol("document");
+        document.setCleanupMode("disabled");
+
+        Tokenizer tokenizer = new Tokenizer();
+        tokenizer.setInputCols(new String[] {"document"});
+        tokenizer.setOutputCol("token");
+
+        Pipeline pipeline = new Pipeline();
+        pipeline.setStages(new PipelineStage[] {document, tokenizer});
+
+        SparkSession spark = com.johnsnowlabs.nlp.SparkNLP.start();
+
+        LinkedList<String> text = new java.util.LinkedList<String>();
+
+        text.add("Peter is a very good person");
+
+        Dataset<Row> data = spark.createDataset(text, Encoders.STRING()).toDF("text");
+
+        PipelineModel pipelineModel = pipeline.fit(data);
+
+        Dataset<Row> transformed = pipelineModel.transform(data);
+        transformed.show();
+
+        PretrainedPipeline pretrained = new PretrainedPipeline("explain_document_dl");
+        pretrained.transform(data).show();
+
+        LemmatizerModel lemmatizer = (LemmatizerModel) LemmatizerModel.pretrained("lemma_antbnc");
+        lemmatizer.setInputCols(new String[] {"token"});
+        lemmatizer.setOutputCol("lemma");
+
+        lemmatizer.transform(transformed).show();
+
+        LightPipeline lightPipeline = new LightPipeline(pipelineModel, true);
+
+        java.util.Map<String, java.util.List<String>> result = lightPipeline.annotateJava("Peter is a very good person.");
+
+        System.out.println(result.get("token"));
+
+        java.util.ArrayList<String> list = new java.util.ArrayList<String>();
+        list.add("Peter is a good person.");
+        list.add("Roy lives in Germany.");
+
+        System.out.println(lightPipeline.annotateJava(list));
+
+        EmbeddingsHelper.load(
+                "./random_embeddings_dim4.txt",
+                spark,
+                "TEXT",
+                "random",
+                4,
+                false);
+
+        System.out.println("\nFinished testing Spark NLP on JAVA");
+
+    }
+}