Spark-Word2Vec creates vector representation of words in a text corpus. It is based on the implementation of word2vec in Spark MLlib. Several optimization techniques are used to make this algorithm more scalable and accurate.
- Two models CBOW and Skip-gram are used in our implementation.
- Both hierarchical softmax and negative sampling methods are supported to train the model.
- The sub-sampling trick can be used to achieve both faster training and significantly better representations of uncommon words.
val spark = SparkSession
.builder
.appName("Word2Vec example")
.master("local[*]")
.getOrCreate()
// $example on$
// Input data: Each row is a bag of words from a sentence or document.
val documentDF = spark.createDataFrame(Seq(
"Hi I heard about Spark".split(" "),
"I wish Java could use case classes".split(" "),
"Logistic regression models are neat".split(" ")
).map(Tuple1.apply)).toDF("text")
// Learn a mapping from words to Vectors.
val word2Vec = new Word2Vec()
.setInputCol("text")
.setOutputCol("result")
.setVectorSize(3)
.setMinCount(0)
val model = word2Vec.fit(documentDF)
val result = model.transform(documentDF)
result.collect().foreach { case Row(text: Seq[_], features: Vector) =>
println(s"Text: [${text.mkString(", ")}] => \nVector: $features\n") }
// $example off$
spark.stop()Spark-Word2Vec is built against Spark 2.1.1.
sbt packageSpark-Word2Vec is available under Apache Licenses 2.0.
If you encounter bugs, feel free to submit an issue or pull request. Also you can mail to:
- hibayesian ([email protected]).
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