Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6181#discussion_r30457642
--- Diff: docs/ml-features.md ---
@@ -106,6 +106,84 @@ for features_label in featurized.select("features",
"label").take(3):
</div>
</div>
+## Word2Vec
+
+`Word2Vec` is an `Estimator` which takes sequences of words that
represents documents and trains a `Word2VecModel`. The model is a `Map(String,
Vector)` essentially, which maps each word to an unique fix-sized vector. The
`Word2VecModel` transforms each documents into a vector using the average of
all words in the document, which aims to other computations of documents such
as similarity calculation consequencely. Please refer to the [MLlib user guide
on Word2Vec](mllib-feature-extraction.html#Word2Vec) for more details on
Word2Vec.
+
+Word2Vec is implemented in
[Word2Vec](api/scala/index.html#org.apache.spark.ml.feature.Word2Vec). In the
following code segment, we start with a set of documents, each of them is
represented as a sequence of words. For each document, we transform it into a
feature vector. This feature vector could then be passed to a learning
algorithm.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.feature.Word2Vec
+
+val documentDF = sqlContext.createDataFrame(Seq(
--- End diff --
Add comment in line above:
```
Input data: Each row is a bag of words from a sentence or document.
```
(Please add to other examples too.)
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