Spark-nlp: how to feed word and sentence embeddings to an MLlib classifier?

[CyborgDroid]

The output of the bert sentence embeddings are a list of floats, this is not accepted by any mllib classification model.

The output of a VectorAssembler is a udt (user defined type)

assembler = VectorAssembler(inputCols=assemblerInputs, outputCol="features")

A simple logistic regression function will not take a list of floats but will accept the udt above.

from pyspark.ml.classification import LogisticRegression
LR = LogisticRegression(featuresCol = 'features', labelCol = 'label', maxIter=15)
LR_model = LR.fit(train)

Besides the formatting issue with list of floats per sentence, the output of all word embeddings (bert and glove) is a list of floats per word. How can this be fed to a LogisticRegression classifier that is predicting by sentence? The only word embedding model that has a sentence_embeddings output is bert (and it's currently broken anyways, see other ticket).

[CyborgDroid]

Here is the error when trying to feed sentence embeddings to LogisticRegression:

Column features must be of type struct<type:tinyint,size:int,indices:array<int>,values:array<double>>
 but was actually array<float>.

[maziyarpanahi]

Our WordEmbeddings and BertEmbeddings are meant to be used by TensorFlow graph and BiLSTM+CNN algorithm which is not compatible with Spark ML. For instance, the Word2Vec in Spark is really naive when it comes to a sentence. It averages all the vectors for each word and just outputs an array of vector, one for each word.
It would be very helpful to be able to do document classification by the use of GloVe, BERT, or FastText in the future with the help of Spark NLP.

I'll label this as a feature request pending further research whether we support Spark ML functions as they are mostly Machine Learning algorithms at the moment or extend the use of embeddings with our own TensorFLow graphs that support the same format as it is. (deep learning)

I've been working on this myself actually and been waiting for TF 2.0 to be at least general release before adding anything more on that side.

[srowen]

BTW this should be easy to fix; you just need to make a dense vector out of the float array with Vectors.dense in Spark.

[maziyarpanahi]

Hi @srowen
Exactly! In the upcoming release from this pull request(https://github.com/JohnSnowLabs/spark-nlp/pull/638), the documentation has been updated to achieve this:

https://github.com/JohnSnowLabs/spark-nlp/blob/b95ac300d4fe9b0c6ebdb29ef774d55e672f3067/docs/en/annotators.md#sentenceembeddings

import org.apache.spark.ml.linalg.{Vector, Vectors}

// Let's create a UDF to take array of embeddings and output Vectors
val convertToVectorUDF = udf((matrix : Seq[Float]) => {
    Vectors.dense(matrix.toArray.map(_.toDouble))
})

// Now let's explode the sentence_embeddings column and have a new feature column for Spark ML
pipelineDF.select(explode($"sentence_embeddings.embeddings").as("sentence_embedding"))
.withColumn("features", convertToVectorUDF($"sentence_embedding"))

PS: We have a new annotator SentenceEmbeddings to get the sentence/document embeddings from word embeddings to feed Spark ML/MLlib. (https://github.com/JohnSnowLabs/spark-nlp/pull/638)

[maziyarpanahi]

I am closing this issue. We have now:

• SentenceEmbeddings (to convert word embeddings into sentence embeddings)
• EmbeddingsFinisher (to output Array or Vectors compatible with Spark ML)
• ClassifierDL (Multi-class text classifier that uses Universal Sentence Encoder or SentenceEmbeddings output)