sparknlp.annotator.Tokenizer#

class sparknlp.annotator.Tokenizer[source]#

Bases: sparknlp.common.AnnotatorApproach

Tokenizes raw text in document type columns into TokenizedSentence .

This class represents a non fitted tokenizer. Fitting it will cause the internal RuleFactory to construct the rules for tokenizing from the input configuration.

Identifies tokens with tokenization open standards. A few rules will help customizing it if defaults do not fit user needs.

For extended examples of usage see the Spark NLP Workshop.

Input Annotation types	Output Annotation type
`DOCUMENT`	`TOKEN`

Parameters

targetPattern: Pattern to grab from text as token candidates, by default \S+
prefixPattern: Regex with groups and begins with \A to match target prefix, by default \A([^\s\w\$\.]*)
suffixPattern: Regex with groups and ends with \z to match target suffix, by default ([^\s\w]?)([^\s\w]*)\z
infixPatterns: Regex patterns that match tokens within a single target. groups identify different sub-tokens. multiple defaults
exceptions: Words that won’t be affected by tokenization rules
exceptionsPath: Path to file containing list of exceptions
caseSensitiveExceptions: Whether to care for case sensitiveness in exceptions, by default True
contextChars: Character list used to separate from token boundaries, by default [‘.’, ‘,’, ‘;’, ‘:’, ‘!’, ‘?’, ‘*’, ‘-’, ‘(’, ‘)’, ‘”’, “’”]
splitPattern: Pattern to separate from the inside of tokens. Takes priority over splitChars.
splitChars: Character list used to separate from the inside of tokens
minLength: Set the minimum allowed length for each token, by default 0
maxLength: Set the maximum allowed length for each token, by default 99999

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> data = spark.createDataFrame([["I'd like to say we didn't expect that. Jane's boyfriend."]]).toDF("text")
>>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> tokenizer = Tokenizer().setInputCols(["document"]).setOutputCol("token").fit(data)
>>> pipeline = Pipeline().setStages([documentAssembler, tokenizer]).fit(data)
>>> result = pipeline.transform(data)
>>> result.selectExpr("token.result").show(truncate=False)
+-----------------------------------------------------------------------+
|output                                                                 |
+-----------------------------------------------------------------------+
|[I'd, like, to, say, we, didn't, expect, that, ., Jane's, boyfriend, .]|
+-----------------------------------------------------------------------+

Methods

`__init__`()
`addContextChars`(value)	Adds an additional character to the list used to separate from token boundaries.
`addException`(value)	Adds an additional word that won't be affected by tokenization rules.
`addInfixPattern`(value)	Adds an additional regex pattern that match tokens within a single target.
`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.
`fit`(dataset[, params])	Fits a model to the input dataset with optional parameters.
`fitMultiple`(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
`getCaseSensitiveExceptions`()	Gets whether to care for case sensitiveness in exceptions.
`getContextChars`()	Gets character list used to separate from token boundaries.
`getExceptions`()	Gets words that won't be affected by tokenization rules.
`getInfixPatterns`()	Gets regex patterns that match tokens within a single target.
`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.
`getPrefixPattern`()	Gets regex with groups and begins with `\A` to match target prefix.
`getSplitChars`()	Gets character list used to separate from the inside of tokens.
`getSuffixPattern`()	Gets regex with groups and ends with `\z` to match target suffix.
`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.
`setCaseSensitiveExceptions`(value)	Sets whether to care for case sensitiveness in exceptions, by default True.
`setContextChars`(value)	Sets character list used to separate from token boundaries, by default ['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '"', "'"].
`setExceptions`(value)	Sets words that won't be affected by tokenization rules.
`setExceptionsPath`(path[, read_as, options])	Path to txt file with list of token exceptions
`setInfixPatterns`(value)	Sets regex patterns that match tokens within a single target.
`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 legth for each token, by default 99999.
`setMinLength`(value)	Sets the minimum allowed legth for each token, by default 0.
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setPrefixPattern`(value)	Sets regex with groups and begins with `\A` to match target prefix, by default `\A([^\s\w\$\.]*)`.
`setSplitChars`(value)	Sets character list used to separate from the inside of tokens.
`setSplitPattern`(value)	Sets pattern to separate from the inside of tokens.
`setSuffixPattern`(value)	Sets regex with groups and ends with `\z` to match target suffix, by default `([^\s\w]?)([^\s\w]*)\z`.
`setTargetPattern`(value)	Sets pattern to grab from text as token candidates, by default `\S+`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`caseSensitiveExceptions`
`contextChars`
`exceptions`
`exceptionsPath`
`getter_attrs`
`infixPatterns`
`inputCols`
`lazyAnnotator`
`maxLength`
`minLength`
`name`
`outputCol`
`params`	Returns all params ordered by name.
`prefixPattern`
`splitChars`
`splitPattern`
`suffixPattern`
`targetPattern`

addContextChars(value)[source]#

Adds an additional character to the list used to separate from token boundaries.

Parameters

valuestr: Additional context character

addException(value)[source]#

Adds an additional word that won’t be affected by tokenization rules.

Parameters

valuestr: Additional word that won’t be affected by tokenization rules

addInfixPattern(value)[source]#

Adds an additional regex pattern that match tokens within a single target. Groups identify different sub-tokens.

Parameters

valuestr: Regex pattern that match tokens within a single target

addSplitChars(value)[source]#

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

fit(dataset, params=None)#

Fits a model to 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. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

fitted model(s)

New in version 1.3.0.

fitMultiple(dataset, paramMaps)#

Fits a model to the input dataset for each param map in paramMaps.

Parameters

dataset – input dataset, which is an instance of pyspark.sql.DataFrame.
paramMaps – A Sequence of param maps.

Returns

A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

New in version 2.3.0.

getCaseSensitiveExceptions()[source]#

Gets whether to care for case sensitiveness in exceptions.

Returns

bool: Whether to care for case sensitiveness in exceptions

getContextChars()[source]#

Gets character list used to separate from token boundaries.

Returns

List[str]: Character list used to separate from token boundaries

getExceptions()[source]#

Gets words that won’t be affected by tokenization rules.

Returns

List[str]: Words that won’t be affected by tokenization rules

getInfixPatterns()[source]#

Gets regex patterns that match tokens within a single target. Groups identify different sub-tokens.

Returns

List[str]: The infix patterns

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

getPrefixPattern()[source]#

Gets regex with groups and begins with \A to match target prefix.

Returns

str: The prefix pattern

getSplitChars()[source]#

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

Returns

List[str]: Character list used to separate from the inside of tokens

getSuffixPattern()[source]#

Gets regex with groups and ends with \z to match target suffix.

Returns

str: The suffix pattern

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.

setCaseSensitiveExceptions(value)[source]#

Sets whether to care for case sensitiveness in exceptions, by default True.

Parameters

valuebool: Whether to care for case sensitiveness in exceptions

setContextChars(value)[source]#

Sets character list used to separate from token boundaries, by default [‘.’, ‘,’, ‘;’, ‘:’, ‘!’, ‘?’, ‘*’, ‘-’, ‘(’, ‘)’, ‘”’, “’”].

Parameters

valueList[str]: Character list used to separate from token boundaries

setExceptions(value)[source]#

Sets words that won’t be affected by tokenization rules.

Parameters

valueList[str]: Words that won’t be affected by tokenization rules

setExceptionsPath(path, read_as='TEXT', options={'format': 'text'})[source]#

Path to txt file with list of token exceptions

Parameters

pathstr: Path to the source file
read_asstr, optional: How to read the file, by default ReadAs.TEXT
optionsdict, optional: Options to read the resource, by default {“format”: “text”}

setInfixPatterns(value)[source]#

Sets regex patterns that match tokens within a single target. Groups identify different sub-tokens.

Parameters

valueList[str]: Regex patterns that match tokens within a single target

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 legth for each token, by default 99999.

Parameters

valueint: Maximum allowed legth for each token

setMinLength(value)[source]#

Sets the minimum allowed legth for each token, by default 0.

Parameters

valueint: Minimum allowed legth for each token

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

setPrefixPattern(value)[source]#

Sets regex with groups and begins with \A to match target prefix, by default \A([^\s\w\$\.]*).

Parameters

valuestr: Regex with groups and begins with \A to match target prefix

setSplitChars(value)[source]#

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)[source]#

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

Parameters

valuestr: Pattern used to separate from the inside of tokens

setSuffixPattern(value)[source]#

Sets regex with groups and ends with \z to match target suffix, by default ([^\s\w]?)([^\s\w]*)\z.

Parameters

valuestr: Regex with groups and ends with \z to match target suffix

setTargetPattern(value)[source]#

Sets pattern to grab from text as token candidates, by default \S+.

Parameters

valuestr: Pattern to grab from text as token candidates

uid#: A unique id for the object.

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

sparknlp.annotator.Token2Chunk

sparknlp.annotator.TokenizerModel