sparknlp_jsl.annotator.ChunkSentenceSplitter#

class sparknlp_jsl.annotator.ChunkSentenceSplitter(classname='com.johnsnowlabs.nlp.annotators.chunker.ChunkSentenceSplitter', java_model=None)[source]#

Bases: AnnotatorModel

Split the document using the chunks that you provided,and put in the metadata the chunk entity. The first piece of documento to the first chunk will have the entity as header.

Use the identifier or field as a entity.

Input Annotation types	Output Annotation type
`DOCUMENT, CHUNK`	`DOCUMENT`

Parameters:

inputType: The type of the entity that you want to filter by default sentence_embeddings.Possible values document|token|wordpiece|word_embeddings|sentence_embeddings|category|date|sentiment|pos|chunk|named_entity|regex|dependency|labeled_dependency|language|keyword
Examples
——–
>>> document = DocumentAssembler().setInputCol(“text”).setOutputCol(“document”)
**>>> regexMatcher = RegexMatcher().setExternalRules(“../src/test/resources/chunker/title_regex.txt”, “,”) **
**… .setInputCols(“document”) **
**… .setOutputCol(“chunks”) **
>>> chunkSentenceSplitter = ChunkSentenceSplitter().setInputCols(“chunks”,”document”).setOutputCol(“paragraphs”)
>>> pipeline = Pipeline().setStages([documentAssembler,regexMatcher,chunkSentenceSplitter])
>>> result = pipeline.fit(data).transform(data).select(“paragraphs”)
>>> result.show()

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).
`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.
`setDefaultEntity`(value)	Sets the key in the metadata dictionary that you want to filter (by default 'entity')
`setGroupBySentences`(value)	Sets the groupBySentences that allow split the paragraphs grouping the chunks by sentences.
`setInputCols`(*value)	Sets column names of input annotations.
`setInsertChunk`(value)	Whether to insert the chunk in the paragraph or not.
`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`()
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`defaultEntity`
`getter_attrs`
`groupBySentences`
`inputCols`
`insertChunk`
`lazyAnnotator`
`name`
`outputCol`
`params`	Returns all params ordered by name.

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.

setDefaultEntity(value)[source]#

Sets the key in the metadata dictionary that you want to filter (by default ‘entity’)

Parameters:

valuestr: The key in the metadata dictionary that you want to filter (by default ‘entity’)

setGroupBySentences(value)[source]#

Sets the groupBySentences that allow split the paragraphs grouping the chunks by sentences.: If is false we assume that we have 1 document annotations and all chunks are for this document. Use false if the input column of your chunk annotator was a sentenceDetector column. Use true when we have a sentence detector as input column or when the document have many sentences per row

Parameters:

valueBoolean: Allow split the paragraphs grouping the chunks by sentences

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*valuestr: Input columns for the annotator

setInsertChunk(value)[source]#

Whether to insert the chunk in the paragraph or not.

Parameters:

valueBoolean: Whether to insert the chunk in the paragraph or not.

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

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.