sparknlp_jsl.annotator.NerChunker#

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

Bases: AnnotatorModel

Extracts phrases that fits into a known pattern using the NER tags. Useful for entity groups with neighboring tokens: when there is no pretrained NER model to address certain issues. A Regex needs to be provided to extract the tokens between entities.

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

Parameters:

setRegexParsers: A list of regex patterns to match chunks, for example: [“‹DT›?‹JJ›*‹NN”]

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.base import *
>>> from sparknlp.annotator import *
>>> from sparknlp_jsl.annotator import *
>>> from sparknlp.training import *
>>> from pyspark.ml import Pipeline

>>> document_assembler = DocumentAssembler() \
...    .setInputCol("text") \
...    .setOutputCol("document")
...
>>> sentence_detector = SentenceDetector() \
...    .setInputCol("document") \
...    .setOutputCol("sentence")
...
>>> tokenizer = Tokenizer() \
...    .setInputCols(["sentence"]) \
...    .setOutputCol("token")
...
>>> embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models") \
...    .setInputCols(["sentence", "token"]) \
...    .setOutputCol("embeddings")     ...    .setCaseSensitive(False)
...
>>> ner = MedicalNerModel.pretrained("ner_radiology", "en", "clinical/models") \
...    .setInputCols(["sentence", "token","embeddings"]) \
...    .setOutputCol("ner")     ...    .setCaseSensitive(False)
...
>>> chunker = NerChunker() \
...    .setInputCols(["sentence","ner"]) \
...    .setChunkCol("ner_chunk") \
...    .setOutputCol("chunk")
...    .setRegexParsers(Array("<ImagingFindings>.*<BodyPart>"))
...
...
>>> pipeline = Pipeline(stages=[
...    document_assembler,
...    sentence_detector,
...    tokenizer,
...    embeddings,
...    ner,
...    chunker
...])
>>> result = pipeline.fit.fit(dataset).transform(dataset)

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.
`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`()
`setRegexParsers`(b)	Sets list of regex patterns to match chunks, for example: Array(“‹DT›?‹JJ›*‹NN›”
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`getter_attrs`
`inputCols`
`lazyAnnotator`
`name`
`outputCol`
`params`	Returns all params ordered by name.
`regexParsers`

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.

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

setRegexParsers(b)[source]#

Sets list of regex patterns to match chunks, for example: Array(“‹DT›?‹JJ›*‹NN›”

Parameters:

bList[String]: list of regex patterns to match chunks, for example: Array(“‹DT›?‹JJ›*‹NN›”

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.