sparknlp.annotator.SentenceDetector

class sparknlp.annotator.SentenceDetector[source]

Bases: sparknlp.common.AnnotatorModel, sparknlp.annotator.SentenceDetectorParams

Annotator that detects sentence boundaries using any provided approach.

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
useAbbreviations

whether to apply abbreviations at sentence detection, by default True

customBounds

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

useCustomBoundsOnly

Only utilize custom bounds in sentence detection, by default False

explodeSentences

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

detectLists

whether detect lists during sentence detection, by default True

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")
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     sentence
... ])
>>> data = spark.createDataFrame([["This is my first sentence. This my second. How about a third?"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(sentence) as sentences").show(truncate=False)
+------------------------------------------------------------------+
|sentences                                                         |
+------------------------------------------------------------------+
|[document, 0, 25, This is my first sentence., [sentence -> 0], []]|
|[document, 27, 41, This my second., [sentence -> 1], []]          |
|[document, 43, 60, How about a third?, [sentence -> 2], []]       |
+------------------------------------------------------------------+

Methods

__init__()

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).

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 [].

setDetectLists(value)

Sets whether detect lists during sentence detection, by default True

setExplodeSentences(value)

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

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

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.

setUseAbbreviations(value)

Sets whether to apply abbreviations at sentence detection, by default True

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

detectLists

explodeSentences

getter_attrs

inputCols

lazyAnnotator

maxLength

minLength

name

outputCol

params

Returns all params ordered by name.

splitLength

useAbbreviations

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.

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

setDetectLists(value)[source]

Sets whether detect lists during sentence detection, by default True

Parameters
valuebool

Whether detect lists during sentence detection

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

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

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.

setUseAbbreviations(value)[source]

Sets whether to apply abbreviations at sentence detection, by default True

Parameters
valuebool

Whether to apply abbreviations at sentence detection

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.