sparknlp.annotator.classifier_dl.classifier_dl#

Classes#

class ClassifierDLApproach[source]#

Trains a ClassifierDL for generic Multi-class Text Classification.

ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text classifications. The ClassifierDL annotator uses a deep learning model (DNNs) we have built inside TensorFlow and supports up to 100 classes.

For instantiated/pretrained models, see ClassifierDLModel.

Setting a test dataset to monitor model metrics can be done with .setTestDataset. The method expects a path to a parquet file containing a dataframe that has the same required columns as the training dataframe. The pre-processing steps for the training dataframe should also be applied to the test dataframe. The following example will show how to create the test dataset:

>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> embeddings = UniversalSentenceEncoder.pretrained() \
...     .setInputCols(["document"]) \
...     .setOutputCol("sentence_embeddings")
>>> preProcessingPipeline = Pipeline().setStages([documentAssembler, embeddings])
>>> (train, test) = data.randomSplit([0.8, 0.2])
>>> preProcessingPipeline \
...     .fit(test) \
...     .transform(test)
...     .write \
...     .mode("overwrite") \
...     .parquet("test_data")
>>> classifier = ClassifierDLApproach() \
...     .setInputCols(["sentence_embeddings"]) \
...     .setOutputCol("category") \
...     .setLabelColumn("label") \
...     .setTestDataset("test_data")

For extended examples of usage, see the Examples Examples.

Input Annotation types	Output Annotation type
SENTENCE_EMBEDDINGS	CATEGORY

Parameters:

batchSize

Batch size, by default 64

configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

dropout

Dropout coefficient, by default 0.5

enableOutputLogs

Whether to use stdout in addition to Spark logs, by default False

evaluationLogExtended

Whether logs for validation to be extended: it displays time and evaluation of each label. Default is False.

labelColumn

Column with label per each token

lr

Learning Rate, by default 0.005

maxEpochs

Maximum number of epochs to train, by default 30

outputLogsPath

Folder path to save training logs

randomSeed

Random seed for shuffling

testDataset

Path to test dataset. If set used to calculate statistic on it during training.

validationSplit

Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

verbose

Level of verbosity during training

See also

MultiClassifierDLApproach

for multi-class classification

SentimentDLApproach

for sentiment analysis

Notes

• This annotator accepts a label column of a single item in either type of String, Int, Float, or Double.

• UniversalSentenceEncoder, Transformer based embeddings, or SentenceEmbeddings can be used for the inputCol.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline

In this example, the training data "sentiment.csv" has the form of:

text,label
This movie is the best movie I have wached ever! In my opinion this movie can win an award.,0
This was a terrible movie! The acting was bad really bad!,1
...

Then traning can be done like so:

>>> smallCorpus = spark.read.option("header","True").csv("src/test/resources/classifier/sentiment.csv")
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> useEmbeddings = UniversalSentenceEncoder.pretrained() \
...     .setInputCols("document") \
...     .setOutputCol("sentence_embeddings")
>>> docClassifier = ClassifierDLApproach() \
...     .setInputCols("sentence_embeddings") \
...     .setOutputCol("category") \
...     .setLabelColumn("label") \
...     .setBatchSize(64) \
...     .setMaxEpochs(20) \
...     .setLr(5e-3) \
...     .setDropout(0.5)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     useEmbeddings,
...     docClassifier
... ])
>>> pipelineModel = pipeline.fit(smallCorpus)

setDropout(v)[source]#

Sets dropout coefficient, by default 0.5

Parameters:

vfloat

Dropout coefficient

class ClassifierDLModel(classname='com.johnsnowlabs.nlp.annotators.classifier.dl.ClassifierDLModel', java_model=None)[source]#

ClassifierDL for generic Multi-class Text Classification.

ClassifierDL uses the state-of-the-art Universal Sentence Encoder as an input for text classifications. The ClassifierDL annotator uses a deep learning model (DNNs) we have built inside TensorFlow and supports up to 100 classes.

This is the instantiated model of the ClassifierDLApproach. For training your own model, please see the documentation of that class.

Pretrained models can be loaded with pretrained() of the companion object:

>>> classifierDL = ClassifierDLModel.pretrained() \
...     .setInputCols(["sentence_embeddings"]) \
...     .setOutputCol("classification")

The default model is "classifierdl_use_trec6", if no name is provided. It uses embeddings from the UniversalSentenceEncoder and is trained on the TREC-6 dataset.

For available pretrained models please see the Models Hub.

For extended examples of usage, see the Examples.

Input Annotation types	Output Annotation type
SENTENCE_EMBEDDINGS	CATEGORY

Parameters:

configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

classes

Get the tags used to trained this ClassifierDLModel

See also

MultiClassifierDLModel

for multi-class classification

SentimentDLModel

for sentiment analysis

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> sentence = SentenceDetector() \
...     .setInputCols("document") \
...     .setOutputCol("sentence")
>>> useEmbeddings = UniversalSentenceEncoder.pretrained() \
...     .setInputCols("document") \
...     .setOutputCol("sentence_embeddings")
>>> sarcasmDL = ClassifierDLModel.pretrained("classifierdl_use_sarcasm") \
...     .setInputCols("sentence_embeddings") \
...     .setOutputCol("sarcasm")
>>> pipeline = Pipeline() \
...     .setStages([
...       documentAssembler,
...       sentence,
...       useEmbeddings,
...       sarcasmDL
...     ])
>>> data = spark.createDataFrame([
...     ["I'm ready!"],
...     ["If I could put into words how much I love waking up at 6 am on Mondays I would."]
... ]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(arrays_zip(sentence, sarcasm)) as out") \
...     .selectExpr("out.sentence.result as sentence", "out.sarcasm.result as sarcasm") \
...     .show(truncate=False)
+-------------------------------------------------------------------------------+-------+
|sentence                                                                       |sarcasm|
+-------------------------------------------------------------------------------+-------+
|I'm ready!                                                                     |normal |
|If I could put into words how much I love waking up at 6 am on Mondays I would.|sarcasm|
+-------------------------------------------------------------------------------+-------+

setConfigProtoBytes(b)[source]#

Sets configProto from tensorflow, serialized into byte array.

Parameters:

bList[int]

ConfigProto from tensorflow, serialized into byte array

static pretrained(name='classifierdl_use_trec6', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters:

namestr, optional

Name of the pretrained model, by default “classifierdl_use_trec6”

langstr, optional

Language of the pretrained model, by default “en”

remote_locstr, optional

Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:

ClassifierDLModel

The restored model