sparknlp.annotator.WordSegmenterApproach

class sparknlp.annotator.WordSegmenterApproach[source]

Bases: sparknlp.common.AnnotatorApproach

Trains a WordSegmenter which tokenizes non-english or non-whitespace separated texts.

Many languages are not whitespace separated and their sentences are a concatenation of many symbols, like Korean, Japanese or Chinese. Without understanding the language, splitting the words into their corresponding tokens is impossible. The WordSegmenter is trained to understand these languages and split them into semantically correct parts.

For instantiated/pretrained models, see WordSegmenterModel.

To train your own model, a training dataset consisting of Part-Of-Speech tags is required. The data has to be loaded into a dataframe, where the column is an Annotation of type POS. This can be set with setPosColumn().

Tip: The helper class POS might be useful to read training data into data frames.

For extended examples of usage, see the Spark NLP Workshop.

Input Annotation types

Output Annotation type

DOCUMENT

TOKEN

Parameters
posCol

column of Array of POS tags that match tokens

nIterations

Number of iterations in training, converges to better accuracy, by default 5

frequencyThreshold

How many times at least a tag on a word to be marked as frequent, by default 5

ambiguityThreshold

How much percentage of total amount of words are covered to be marked as frequent, by default 0.97

Examples

In this example, "chinese_train.utf8" is in the form of:

|LL |RR |LL |RR |LL |RR

and is loaded with the POS class to create a dataframe of POS type Annotations.

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> wordSegmenter = WordSegmenterApproach() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token") \
...     .setPosColumn("tags") \
...     .setNIterations(5)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     wordSegmenter
... ])
>>> trainingDataSet = POS().readDataset(
...     spark,
...     "src/test/resources/word-segmenter/chinese_train.utf8"
... )
>>> pipelineModel = pipeline.fit(trainingDataSet)

Methods

__init__()

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.

getAmbiguityThreshold()

Sets How much percentage of total amount of words are covered to be marked as frequent.

getFrequencyThreshold()

Sets How many times at least a tag on a word to be marked as frequent.

getInputCols()

Gets current column names of input annotations.

getLazyAnnotator()

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getNIterations()

Gets number of iterations in training, converges to better accuracy.

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.

setAmbiguityThreshold(value)

Sets the percentage of total amount of words are covered to be marked as frequent, by default 0.97.

setFrequencyThreshold(value)

Sets how many times at least a tag on a word to be marked as frequent, by default 5.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setNIterations(value)

Sets number of iterations in training, converges to better accuracy, by default 5.

setOutputCol(value)

Sets output column name of annotations.

setParamValue(paramName)

Sets the value of a parameter.

setPosColumn(value)

Sets column name for array of POS tags that match tokens.

write()

Returns an MLWriter instance for this ML instance.

Attributes

ambiguityThreshold

frequencyThreshold

getter_attrs

inputCols

lazyAnnotator

nIterations

name

outputCol

params

Returns all params ordered by name.

posCol

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.

getAmbiguityThreshold()[source]

Sets How much percentage of total amount of words are covered to be marked as frequent.

Returns
float

Percentage of total amount of words are covered to be marked as frequent

getFrequencyThreshold()[source]

Sets How many times at least a tag on a word to be marked as frequent.

Returns
int

Frequency threshold to be marked as frequent

getInputCols()

Gets current column names of input annotations.

getLazyAnnotator()

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getNIterations()[source]

Gets number of iterations in training, converges to better accuracy.

Returns
int

Number of iterations

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.

setAmbiguityThreshold(value)[source]

Sets the percentage of total amount of words are covered to be marked as frequent, by default 0.97.

Parameters
valuefloat

Percentage of total amount of words are covered to be marked as frequent

setFrequencyThreshold(value)[source]

Sets how many times at least a tag on a word to be marked as frequent, by default 5.

Parameters
valueint

Frequency threshold to be marked as frequent

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

setNIterations(value)[source]

Sets number of iterations in training, converges to better accuracy, by default 5.

Parameters
valueint

Number of iterations

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

setPosColumn(value)[source]

Sets column name for array of POS tags that match tokens.

Parameters
valuestr

Name of the column

uid

A unique id for the object.

write()

Returns an MLWriter instance for this ML instance.