sparknlp.annotator.MultiClassifierDLModel

class sparknlp.annotator.MultiClassifierDLModel(classname='com.johnsnowlabs.nlp.annotators.classifier.dl.MultiClassifierDLModel', java_model=None)[source]

Bases: sparknlp.common.AnnotatorModel, sparknlp.common.HasStorageRef

MultiClassifierDL for Multi-label Text Classification.

MultiClassifierDL Bidirectional GRU with Convolution model we have built inside TensorFlow and supports up to 100 classes.

In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y).

The input to MultiClassifierDL are Sentence Embeddings such as the state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, SentenceEmbeddings or other sentence embeddings.

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

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

>>> multiClassifier = MultiClassifierDLModel.pretrained() \
>>>     .setInputCols(["sentence_embeddings"]) \
>>>     .setOutputCol("categories")

The default model is "multiclassifierdl_use_toxic", if no name is provided. It uses embeddings from the UniversalSentenceEncoder and classifies toxic comments.

The data is based on the Jigsaw Toxic Comment Classification Challenge. For available pretrained models please see the Models Hub.

For extended examples of usage, see the Spark NLP Workshop.

Input Annotation types

Output Annotation type

SENTENCE_EMBEDDINGS

CATEGORY

Parameters
configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

threshold

The minimum threshold for each label to be accepted, by default 0.5

classes

Get the tags used to trained this MultiClassifierDLModel

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> useEmbeddings = UniversalSentenceEncoder.pretrained() \
...     .setInputCols("document") \
...     .setOutputCol("sentence_embeddings")
>>> multiClassifierDl = MultiClassifierDLModel.pretrained() \
...     .setInputCols("sentence_embeddings") \
...     .setOutputCol("classifications")
>>> pipeline = Pipeline() \
...     .setStages([
...         documentAssembler,
...         useEmbeddings,
...         multiClassifierDl
...     ])
>>> data = spark.createDataFrame([
...     ["This is pretty good stuff!"],
...     ["Wtf kind of crap is this"]
... ]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("text", "classifications.result").show(truncate=False)
+--------------------------+----------------+
|text                      |result          |
+--------------------------+----------------+
|This is pretty good stuff!|[]              |
|Wtf kind of crap is this  |[toxic, obscene]|
+--------------------------+----------------+

Methods

__init__([classname, java_model])

Initialize this instance with a Java model object.

clear(param)

Clears a param from the param map if it has been explicitly set.

copy([extra])

Creates a copy of this instance with the same uid and some extra params.

explainParam(param)

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap([extra])

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

getInputCols()

Gets current column names of input annotations.

getLazyAnnotator()

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)

Gets the value of a param in the user-supplied param map or its default value.

getOutputCol()

Gets output column name of annotations.

getParam(paramName)

Gets a param by its name.

getParamValue(paramName)

Gets the value of a parameter.

getStorageRef()

Gets unique reference name for identification.

hasDefault(param)

Checks whether a param has a default value.

hasParam(paramName)

Tests whether this instance contains a param with a given (string) name.

isDefined(param)

Checks whether a param is explicitly set by user or has a default value.

isSet(param)

Checks whether a param is explicitly set by user.

load(path)

Reads an ML instance from the input path, a shortcut of read().load(path).

pretrained([name, lang, remote_loc])

Downloads and loads a pretrained model.

read()

Returns an MLReader instance for this class.

save(path)

Save this ML instance to the given path, a shortcut of 'write().save(path)'.

set(param, value)

Sets a parameter in the embedded param map.

setConfigProtoBytes(b)

Sets configProto from tensorflow, serialized into byte array.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setOutputCol(value)

Sets output column name of annotations.

setParamValue(paramName)

Sets the value of a parameter.

setParams()

setStorageRef(value)

Sets unique reference name for identification.

setThreshold(v)

Sets minimum threshold for each label to be accepted, by default 0.5.

transform(dataset[, params])

Transforms the input dataset with optional parameters.

write()

Returns an MLWriter instance for this ML instance.

Attributes

classes

configProtoBytes

getter_attrs

inputCols

lazyAnnotator

name

outputCol

params

Returns all params ordered by name.

storageRef

threshold

clear(param)

Clears a param from the param map if it has been explicitly set.

copy(extra=None)

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extra – Extra parameters to copy to the new instance

Returns

Copy of this instance

explainParam(param)

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters

extra – extra param values

Returns

merged param map

getInputCols()

Gets current column names of input annotations.

getLazyAnnotator()

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol()

Gets output column name of annotations.

getParam(paramName)

Gets a param by its name.

getParamValue(paramName)

Gets the value of a parameter.

Parameters
paramNamestr

Name of the parameter

getStorageRef()

Gets unique reference name for identification.

Returns
str

Unique reference name for identification

hasDefault(param)

Checks whether a param has a default value.

hasParam(paramName)

Tests whether this instance contains a param with a given (string) name.

isDefined(param)

Checks whether a param is explicitly set by user or has a default value.

isSet(param)

Checks whether a param is explicitly set by user.

classmethod load(path)

Reads an ML instance from the input path, a shortcut of read().load(path).

property params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

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

Downloads and loads a pretrained model.

Parameters
namestr, optional

Name of the pretrained model, by default “multiclassifierdl_use_toxic”

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
MultiClassifierDLModel

The restored model

classmethod read()

Returns an MLReader instance for this class.

save(path)

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)

Sets a parameter in the embedded param map.

setConfigProtoBytes(b)[source]

Sets configProto from tensorflow, serialized into byte array.

Parameters
bList[str]

ConfigProto from tensorflow, serialized into byte array

setInputCols(*value)

Sets column names of input annotations.

Parameters
*valuestr

Input columns for the annotator

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters
valuebool

Whether Annotator should be evaluated lazily in a RecursivePipeline

setOutputCol(value)

Sets output column name of annotations.

Parameters
valuestr

Name of output column

setParamValue(paramName)

Sets the value of a parameter.

Parameters
paramNamestr

Name of the parameter

setStorageRef(value)

Sets unique reference name for identification.

Parameters
valuestr

Unique reference name for identification

setThreshold(v)[source]

Sets minimum threshold for each label to be accepted, by default 0.5.

Parameters
vfloat

The minimum threshold for each label to be accepted, by default 0.5

transform(dataset, params=None)

Transforms the input dataset with optional parameters.

Parameters
  • dataset – input dataset, which is an instance of pyspark.sql.DataFrame

  • params – an optional param map that overrides embedded params.

Returns

transformed dataset

New in version 1.3.0.

uid

A unique id for the object.

write()

Returns an MLWriter instance for this ML instance.