通常在NLP中，人們基於一定的語料庫，可以利用N-Gram來預計或者評估一個句子是否合理。另外一方面，N-Gram的另外一個作用是用來評估兩個字串之間的差異程度。下面是spark官網（http://spark.apache.org/docs/latest/ml-features.html#tokenizer）給出的例子

def main(args:Array[String]):Unit={


    val spark: SparkSession = SparkSession.builder
      .appName("My")
      .master("local[*]")
      .
 
getOrCreate()

    val wordDataFrame = spark.createDataFrame(Seq(
      (0, Array("Hi", "I", "heard", "about", "Spark")),
      (1, Array("I", "wish", "Java", "could", "use", "case", "classes")),
      (2, Array("Logistic", "regression", "models", "are", "neat"))
    )).toDF("id", "words")

    val ngram =
 
 new NGram().setN(2).setInputCol("words").setOutputCol("ngrams")

    val ngramDataFrame = ngram.transform(wordDataFrame)
    ngramDataFrame.select("ngrams").show(false)

  }