sparknlp.annotator.SentenceDetectorDLModel#

class sparknlp.annotator.SentenceDetectorDLModel(classname='com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLModel', java_model=None)[source]#

Bases: sparknlp.common.AnnotatorModel

Annotator that detects sentence boundaries using a deep learning approach.

Instantiated Model of the SentenceDetectorDLApproach. Detects sentence boundaries using a deep learning approach.

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

>>> sentenceDL = SentenceDetectorDLModel.pretrained() \
...     .setInputCols(["document"]) \
...     .setOutputCol("sentencesDL")

The default model is "sentence_detector_dl", if no name is provided. For available pretrained models please see the Models Hub.

Each extracted sentence can be returned in an Array or exploded to separate rows, if explodeSentences is set to true.

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

Input Annotation types

Output Annotation type

DOCUMENT

DOCUMENT

Parameters
modelArchitecture

Model architecture (CNN)

explodeSentences

whether to explode each sentence into a different row, for better parallelization. Defaults to false.

customBounds

characters used to explicitly mark sentence bounds, by default []

useCustomBoundsOnly

Only utilize custom bounds in sentence detection, by default False

splitLength

length at which sentences will be forcibly split

minLength

Set the minimum allowed length for each sentence, by default 0

maxLength

Set the maximum allowed length for each sentence, by default 99999

impossiblePenultimates

Impossible penultimates - list of strings which a sentence can’t end with

Examples

In this example, the normal SentenceDetector is compared to the SentenceDetectorDLModel. In a pipeline, SentenceDetectorDLModel can be used as a replacement for the SentenceDetector.

>>> 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("sentences")
>>> sentenceDL = SentenceDetectorDLModel \
...     .pretrained("sentence_detector_dl", "en") \
...     .setInputCols(["document"]) \
...     .setOutputCol("sentencesDL")
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     sentence,
...     sentenceDL
... ])
>>> data = spark.createDataFrame([["""John loves Mary.Mary loves Peter
...     Peter loves Helen .Helen loves John;
...     Total: four people involved."""]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(sentences.result) as sentences").show(truncate=False)
+----------------------------------------------------------+
|sentences                                                 |
+----------------------------------------------------------+
|John loves Mary.Mary loves Peter\n     Peter loves Helen .|
|Helen loves John;                                         |
|Total: four people involved.                              |
+----------------------------------------------------------+
>>> result.selectExpr("explode(sentencesDL.result) as sentencesDL").show(truncate=False)
+----------------------------+
|sentencesDL                 |
+----------------------------+
|John loves Mary.            |
|Mary loves Peter            |
|Peter loves Helen .         |
|Helen loves John;           |
|Total: four people involved.|
+----------------------------+

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.

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.

setCustomBounds(value)

Sets characters used to explicitly mark sentence bounds, by default [].

setExplodeSentences(value)

Sets whether to explode each sentence into a different row, for better parallelization, by default False.

setImpossiblePenultimates(...)

Sets impossible penultimates - list of strings which a sentence can't end with.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setMaxLength(value)

Sets the maximum allowed length for each sentence, by default 99999

setMinLength(value)

Sets minimum allowed length for each sentence, by default 0

setModel(modelArchitecture)

Sets the Model architecture.

setOutputCol(value)

Sets output column name of annotations.

setParamValue(paramName)

Sets the value of a parameter.

setParams()

setSplitLength(value)

Sets length at which sentences will be forcibly split.

setUseCustomBoundsOnly(value)

Sets whether to only utilize custom bounds in sentence detection, by default False.

transform(dataset[, params])

Transforms the input dataset with optional parameters.

write()

Returns an MLWriter instance for this ML instance.

Attributes

customBounds

explodeSentences

getter_attrs

impossiblePenultimates

inputCols

lazyAnnotator

maxLength

minLength

modelArchitecture

name

outputCol

params

Returns all params ordered by name.

splitLength

useCustomBoundsOnly

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

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='sentence_detector_dl', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters
namestr, optional

Name of the pretrained model, by default “sentence_detector_dl”

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
SentenceDetectorDLModel

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.

setCustomBounds(value)[source]#

Sets characters used to explicitly mark sentence bounds, by default [].

Parameters
valueList[str]

Characters used to explicitly mark sentence bounds

setExplodeSentences(value)[source]#

Sets whether to explode each sentence into a different row, for better parallelization, by default False.

Parameters
valuebool

Whether to explode each sentence into a different row

setImpossiblePenultimates(impossible_penultimates)[source]#

Sets impossible penultimates - list of strings which a sentence can’t end with.

Parameters
impossible_penultimatesList[str]

List of strings which a sentence can’t end with

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

setMaxLength(value)[source]#

Sets the maximum allowed length for each sentence, by default 99999

Parameters
valueint

Maximum allowed length for each sentence

setMinLength(value)[source]#

Sets minimum allowed length for each sentence, by default 0

Parameters
valueint

Minimum allowed length for each sentence

setModel(modelArchitecture)[source]#

Sets the Model architecture. Currently only "cnn" is available.

Parameters
model_architecturestr

Model architecture

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

setSplitLength(value)[source]#

Sets length at which sentences will be forcibly split.

Parameters
valueint

Length at which sentences will be forcibly split.

setUseCustomBoundsOnly(value)[source]#

Sets whether to only utilize custom bounds in sentence detection, by default False.

Parameters
valuebool

Whether to only utilize custom bounds

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.