sparknlp.annotator.SentenceDetectorDLApproach

class sparknlp.annotator.SentenceDetectorDLApproach(classname='com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLApproach')[source]

Bases: sparknlp.common.AnnotatorApproach

Trains an annotator that detects sentence boundaries using a deep learning approach.

Currently, only the CNN model is supported for training, but in the future the architecture of the model can be set with setModel().

For pretrained models see SentenceDetectorDLModel.

Each extracted sentence can be returned in an Array or exploded to separate rows, if explodeSentences is set to True.

Input Annotation types

Output Annotation type

DOCUMENT

DOCUMENT

Parameters
modelArchitecture

Model architecture (CNN)

impossiblePenultimates

Impossible penultimates - list of strings which a sentence can’t end with

validationSplit

Choose the proportion of training dataset to be validated against the model on each

epochsNumber

Number of epochs for the optimization process

outputLogsPath

Path to folder where logs will be saved. If no path is specified, no logs are generated

explodeSentences

Whether to explode each sentence into a different row, for better parallelization. Defaults to False.

References

The default model "cnn" is based on the paper Deep-EOS: General-Purpose Neural Networks for Sentence Boundary Detection (2020, Stefan Schweter, Sajawel Ahmed) using a CNN architecture. We also modified the original implementation a little bit to cover broken sentences and some impossible end of line chars.

Examples

The training process needs data, where each data point is a sentence. In this example the train.txt file has the form of:

...
Slightly more moderate language would make our present situation – namely the lack of progress – a little easier.
His political successors now have great responsibilities to history and to the heritage of values bequeathed to them by Nelson Mandela.
...

where each line is one sentence.

Training can then be started like so:

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> trainingData = spark.read.text("train.txt").toDF("text")
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> sentenceDetector = SentenceDetectorDLApproach() \
...     .setInputCols(["document"]) \
...     .setOutputCol("sentences") \
...     .setEpochsNumber(100)
>>> pipeline = Pipeline().setStages([documentAssembler, sentenceDetector])
>>> model = pipeline.fit(trainingData)

Methods

__init__([classname])

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.

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.

setEpochsNumber(epochs_number)

Sets number of epochs to train.

setExplodeSentences(value)

Sets whether to explode each sentence into a different row, for better parallelization, by default False.

setImpossiblePenultimates(...)

Sets impossible penultimates - list of strings which a sentence can't end with.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setModel(model_architecture)

Sets the Model architecture.

setOutputCol(value)

Sets output column name of annotations.

setOutputLogsPath(output_logs_path)

Sets folder path to save training logs.

setParamValue(paramName)

Sets the value of a parameter.

setValidationSplit(validation_split)

Sets the proportion of training dataset to be validated against the model on each Epoch, by default it is 0.0 and off.

write()

Returns an MLWriter instance for this ML instance.

Attributes

epochsNumber

explodeSentences

getter_attrs

impossiblePenultimates

inputCols

lazyAnnotator

modelArchitecture

name

outputCol

outputLogsPath

params

Returns all params ordered by name.

validationSplit

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.

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.

setEpochsNumber(epochs_number)[source]

Sets number of epochs to train.

Parameters
epochs_numberint

Number of epochs

setExplodeSentences(value)[source]

Sets whether to explode each sentence into a different row, for better parallelization, by default False.

Parameters
valuebool

Whether to explode each sentence into a different row

setImpossiblePenultimates(impossible_penultimates)[source]

Sets impossible penultimates - list of strings which a sentence can’t end with.

Parameters
impossible_penultimatesList[str]

List of strings which a sentence can’t end with

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

setModel(model_architecture)[source]

Sets the Model architecture. Currently only "cnn" is available.

Parameters
model_architecturestr

Model architecture

setOutputCol(value)

Sets output column name of annotations.

Parameters
valuestr

Name of output column

setOutputLogsPath(output_logs_path)[source]

Sets folder path to save training logs.

Parameters
output_logs_pathstr

Folder path to save training logs

setParamValue(paramName)

Sets the value of a parameter.

Parameters
paramNamestr

Name of the parameter

setValidationSplit(validation_split)[source]

Sets the proportion of training dataset to be validated against the model on each Epoch, by default it is 0.0 and off. The value should be between 0.0 and 1.0.

Parameters
validation_splitfloat

Proportion of training dataset to be validated

uid

A unique id for the object.

write()

Returns an MLWriter instance for this ML instance.