Fix Repeating tokens in WordEmbeddings

src/main/scala/com/johnsnowlabs/nlp/annotators/common/TokenizedWithSentence.scala

@@ -26,7 +26,6 @@ object TokenizedWithSentence extends Annotated[TokenizedSentence] {
     val tokens = annotations
       .filter(_.annotatorType == annotatorType)
       .toArray
-
     val sentences = SentenceSplit.unpack(annotations)
 
     /** // Evaluate whether to enable this validation to check proper usage of DOCUMENT and
@@ -37,7 +36,10 @@ object TokenizedWithSentence extends Annotated[TokenizedSentence] {
     sentences
       .map(sentence => {
         val sentenceTokens = tokens
-          .filter(token => token.begin >= sentence.start & token.end <= sentence.end)
+          .filter(token =>
+            token.begin >= sentence.start &&
+              token.end <= sentence.end &&
+              token.metadata.getOrElse("sentence", "0").toInt == sentence.index)
           .map(token => IndexedToken(token.result, token.begin, token.end))
         sentenceTokens
       })

src/test/scala/com/johnsnowlabs/nlp/embeddings/WordEmbeddingsTestSpec.scala

@@ -23,6 +23,7 @@ import com.johnsnowlabs.nlp.{Annotation, AssertAnnotations}
 import com.johnsnowlabs.tags.{FastTest, SlowTest}
 import org.apache.spark.ml.{Pipeline, PipelineModel}
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions.col
 import org.scalatest.flatspec.AnyFlatSpec
 
 class WordEmbeddingsTestSpec extends AnyFlatSpec with SparkSessionTest {
@@ -31,6 +32,34 @@ class WordEmbeddingsTestSpec extends AnyFlatSpec with SparkSessionTest {
     .option("header", "true")
     .csv("src/test/resources/embeddings/clinical_words.txt")
 
+  "Word Embeddings" should "Should not repeat tokens" taggedAs FastTest in {
+
+    val loaded = spark.read.parquet("src/test/resources/word-embedding/test-repeated-tokens")
+
+    val embeddings = WordEmbeddingsModel
+      .pretrained("glove_100d", "en")
+      .setInputCols(Array("splitter", "token"))
+      .setOutputCol("embedding")
+
+    val pipeline = new Pipeline()
+      .setStages(Array(embeddings))
+
+    val model = pipeline.fit(loaded)
+
+    val result = model.transform(loaded)
+    val duplicateBegins = result
+      .selectExpr("explode(embedding) as e")
+      .select(col("e.begin").alias("begin"))
+      .groupBy("begin")
+      .count()
+      .filter(col("count") > 2)
+      .count()
+
+    assert(
+      duplicateBegins == 0,
+      s"Found $duplicateBegins repeated tokens (duplicate begin positions)")
+  }
+
   "Word Embeddings" should "correctly embed clinical words not embed non-existent words" taggedAs SlowTest in {
 
     val notWords = spark.read