sparknlp.annotator.ChunkTokenizerModel

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

Bases: sparknlp.annotator.TokenizerModel

Instantiated model of the ChunkTokenizer.

This is the instantiated model of the ChunkTokenizer. For training your own model, please see the documentation of that class.

Input Annotation types

Output Annotation type

CHUNK

TOKEN

Parameters
None

Methods

__init__([classname, java_model])

Initialize this instance with a Java model object.

addSplitChars(value)

Adds an additional character to separate from the inside of tokens.

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.

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()

setSplitChars(value)

Sets character list used to separate from the inside of tokens.

setSplitPattern(value)

Sets pattern to separate from the inside of tokens.

transform(dataset[, params])

Transforms the input dataset with optional parameters.

write()

Returns an MLWriter instance for this ML instance.

Attributes

caseSensitiveExceptions

exceptions

getter_attrs

inputCols

lazyAnnotator

name

outputCol

params

Returns all params ordered by name.

rules

splitChars

splitPattern

targetPattern

addSplitChars(value)

Adds an additional character to separate from the inside of tokens.

Parameters
valuestr

Additional character to separate from the inside of tokens

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='token_rules', lang='en', remote_loc=None)

Downloads and loads a pretrained model.

Parameters
namestr, optional

Name of the pretrained model, by default “token_rules”

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
TokenizerModel

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.

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

setSplitChars(value)

Sets character list used to separate from the inside of tokens.

Parameters
valueList[str]

Character list used to separate from the inside of tokens

setSplitPattern(value)

Sets pattern to separate from the inside of tokens. Takes priority over splitChars.

Parameters
valuestr

Pattern used to separate from the inside of tokens

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.