您的bi-gram列值为空，因为“values”列的每行中都没有bi-gram。
如果输入数据框中的值如下所示：

+--------------------------------------------+
|values                                      |
+--------------------------------------------+
|embodiment present invention include pairing|
|two wireless device placing                 |
|least one two                               |
+--------------------------------------------+

然后您可以得到如下的双克输出：

+--------------------------------------------+--------------------------------------------------+---------------------------------------------------------------------------+
|values                                      |testing                                           |ngrams                                                                     |
+--------------------------------------------+--------------------------------------------------+---------------------------------------------------------------------------+
|embodiment present invention include pairing|[embodiment, present, invention, include, pairing]|[embodiment present, present invention, invention include, include pairing]|
|two wireless device placing                 |[two, wireless, device, placing]                  |[two wireless, wireless device, device placing]                            |
|least one two                               |[least, one, two]                                 |[least one, one two]                                                       |
+--------------------------------------------+--------------------------------------------------+---------------------------------------------------------------------------+

执行此操作的scala spark代码是：

val df_new = df.withColumn("testing", split(df("values")," "))
val ngram = new NGram().setN(2).setInputCol("testing").setOutputCol("ngrams")
val ngramDataFrame = ngram.transform(df_new)

双字元是一个符号串中两个相邻元素的序列，通常是字母、音节或单词。
但是在输入Dataframe中，每行只有一个标记，因此不能从中获得任何bi-gram。
所以，对于你的问题，你可以这样做：

Input: df1
+----------+
|values    |
+----------+
|embodiment|
|present   |
|invention |
|include   |
|pairing   |
|two       |
|wireless  |
|devic     |
|placing   |
|least     |
|one       |
|two       |
+----------+

Output: ngramDataFrameInRows
+------------------+
|ngrams            |
+------------------+
|embodiment present|
|present invention |
|invention include |
|include pairing   |
|pairing two       |
|two wireless      |
|wireless devic    |
|devic placing     |
|placing least     |
|least one         |
|one two           |
+------------------+

spark scala代码：

val df_new=df1.agg(collect_list("values").alias("testing"))
val ngram = new NGram().setN(2).setInputCol("testing").setOutputCol("ngrams")
val ngramDataFrame = ngram.transform(df_new)
val ngramDataFrameInRows=ngramDataFrame.select(explode(col("ngrams")).alias("ngrams"))