`sparknlp_jsl.annotator.chunker.chunk_sentence_splitter`#

Module Contents#

Classes#

ChunkSentenceSplitter

An annotator that splits a document into sentences based on provided chunks.

class ChunkSentenceSplitter(classname='com.johnsnowlabs.nlp.annotators.chunker.ChunkSentenceSplitter', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

An annotator that splits a document into sentences based on provided chunks. The first piece of the document is treated as a header, and subsequent chunks are labeled with their associated entities.

This annotator is particularly useful when identifying titles and subtitles using Named Entity Recognition (NER), followed by a paragraph-level split.

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

Parameters:

groupBySentences (bool) – Whether to split a document into paragraphs by grouping chunks by sentences. If set to False, it assumes a single document annotation for all chunks. Set to True if you want to group chunks by sentences, and the input column of your chunk annotator is generated by a sentence detector. Default: True
insertChunk (bool) – Whether to include the chunk in the resulting sentences or not. When insertChunk is set to True, the chunk will be added to the generated sentences. If set to False, the chunk will be omitted from the sentences. Default: True
defaultEntity (str) – Defining the default name for the entity that represents content between the beginning of the document and the first chunk. Default: ‘introduction’

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()

defaultEntity#

getter_attrs = []#

groupBySentences#

inputAnnotatorTypes#

inputCols#

insertChunk#

lazyAnnotator#

name = 'ChunkSentenceSplitter'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

skipLPInputColsValidation = True#

uid#

clear(param: pyspark.ml.param.Param) → None#: Clears a param from the param map if it has been explicitly set.

copy(extra: pyspark.ml._typing.ParamMap | None = None) → JP#

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 (dict, optional) – Extra parameters to copy to the new instance
Returns:: Copy of this instance
Return type:: JavaParams

explainParam(param: str | Param) → str#: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str#: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: pyspark.ml._typing.ParamMap | None = None) → pyspark.ml._typing.ParamMap#

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 (dict, optional) – extra param values
Returns:: merged param map
Return type:: dict

getInputCols()#: Gets current column names of input annotations.

getLazyAnnotator()#: Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param: str) → Any#
getOrDefault(param: Param[T]) → T: 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: str) → Param#: Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

hasDefault(param: str | Param[Any]) → bool#: Checks whether a param has a default value.

hasParam(paramName: str) → bool#: Tests whether this instance contains a param with a given (string) name.

inputColsValidation(value)#

isDefined(param: str | Param[Any]) → bool#: Checks whether a param is explicitly set by user or has a default value.

isSet(param: str | Param[Any]) → bool#: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL#: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read()#: Returns an MLReader instance for this class.

save(path: str) → None#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: Param, value: Any) → None#: Sets a parameter in the embedded param map.

setDefaultEntity(value)#

Sets defaultEntity, defining the default name for the entity that represents content between the beginning of the document and the first chunk.

Parameters:: value (str) – Defining the default name for the entity that represents content between the beginning of the document and the first chunk.

setForceInputTypeValidation(etfm)#

setGroupBySentences(value)#

Sets the groupBySentences property, which determines whether to split a document into paragraphs by grouping chunks by sentences. If set to False, it assumes a single document annotation for all chunks. Set to True if you want to group chunks by sentences, and the input column of your chunk annotator is generated by a sentence detector. Default: True

Parameters:: value (Boolean) – Whether to split a document into paragraphs by grouping chunks by sentence. Default: True

setInputCols(*value)#

Sets column names of input annotations.

Parameters:: *value (List[str]) – Input columns for the annotator

setInsertChunk(value)#

Sets the value for the insertChunk parameter, determining whether to include the chunk in the resulting sentences or not. When insertChunk is set to True, the chunk will be added to the generated sentences. If set to False, the chunk will be omitted from the sentences.

Parameters:: value (Boolean) – Whether to insert the chunk in the sentences or not. Default: True

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:: value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline

setOutputCol(value)#

Sets output column name of annotations.

Parameters:: value (str) – Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

setParams()#

transform(dataset: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = None) → pyspark.sql.dataframe.DataFrame#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

dataset (pyspark.sql.DataFrame) – input dataset
params (dict, optional) – an optional param map that overrides embedded params.

Returns:

transformed dataset

Return type:

pyspark.sql.DataFrame

write() → JavaMLWriter#: Returns an MLWriter instance for this ML instance.

sparknlp_jsl.annotator.chunker.chunk_sentence_splitter#

Module Contents#

Classes#

`sparknlp_jsl.annotator.chunker.chunk_sentence_splitter`#