`sparknlp_jsl.annotator.doc2_chunk_internal`#

Contains classes for Doc2Chunk.

Module Contents#

Classes#

Doc2ChunkInternal

Converts DOCUMENT type annotations into CHUNK type with the

class Doc2ChunkInternal#

Bases: sparknlp.internal.AnnotatorTransformer, sparknlp_jsl.common.AnnotatorProperties

Converts DOCUMENT type annotations into CHUNK type with the contents of a chunkCol.

Chunk text must be contained within input DOCUMENT. May be either StringType or ArrayType[StringType] (using setIsArray). Useful for annotators that require a CHUNK type input.

For more extended examples on document pre-processing see the Spark NLP Workshop.

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

Parameters:

chunkCol – Column that contains the string. Must be part of DOCUMENT
startCol – Column that has a reference of where the chunk begins
startColByTokenIndex – Whether start column is prepended by whitespace tokens
isArray – Whether the chunkCol is an array of strings, by default False
failOnMissing – Whether to fail the job if a chunk is not found within document. Return empty otherwise
lowerCase – Whether to lower case for matching case

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.common import *
>>> from sparknlp.annotator import *
>>> from sparknlp.training import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> chunkAssembler = Doc2Chunk() \
...     .setInputCols("document") \
...     .setChunkCol("target") \
...     .setOutputCol("chunk") \
...     .setIsArray(True)
>>> data = spark.createDataFrame([[
...     "Spark NLP is an open-source text processing library for advanced natural language processing.",
...     ["Spark NLP", "text processing library", "natural language processing"]
... ]]).toDF("text", "target")
>>> pipeline = Pipeline().setStages([documentAssembler, chunkAssembler]).fit(data)
>>> result = pipeline.transform(data)
>>> result.selectExpr("chunk.result", "chunk.annotatorType").show(truncate=False)
+-----------------------------------------------------------------+---------------------+
|result                                                           |annotatorType        |
+-----------------------------------------------------------------+---------------------+
|[Spark NLP, text processing library, natural language processing]|[chunk, chunk, chunk]|
+-----------------------------------------------------------------+---------------------+

See also

Chunk2Doc: for converting CHUNK annotations to DOCUMENT

chunkCol#

failOnMissing#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

isArray#

kwargs#

lazyAnnotator#

lowerCase#

name = 'Doc2Chunk'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = None#

outputCol#

startCol#

startColByTokenIndex#

uid#

clear(param: pyspark.ml.param.Param) → None#: Clears a param from the param map if it has been explicitly set.

copy(extra: pyspark.ml._typing.ParamMap | None = None) → JP#

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 (dict, optional) – Extra parameters to copy to the new instance
Returns:: Copy of this instance
Return type:: JavaParams

explainParam(param: str | Param) → str#: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str#: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: pyspark.ml._typing.ParamMap | None = None) → pyspark.ml._typing.ParamMap#

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 (dict, optional) – extra param values
Returns:: merged param map
Return type:: dict

getInputCols()#: Gets current column names of input annotations.

getLazyAnnotator()#: Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param: str) → Any#
getOrDefault(param: Param[T]) → T: 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: str) → Param#: Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

hasDefault(param: str | Param[Any]) → bool#: Checks whether a param has a default value.

hasParam(paramName: str) → bool#: Tests whether this instance contains a param with a given (string) name.

inputColsValidation(value)#

isDefined(param: str | Param[Any]) → bool#: Checks whether a param is explicitly set by user or has a default value.

isSet(param: str | Param[Any]) → bool#: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL#: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read()#: Returns an MLReader instance for this class.

save(path: str) → None#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: Param, value: Any) → None#: Sets a parameter in the embedded param map.

setChunkCol(value)#

Sets column that contains the string. Must be part of DOCUMENT.

Parameters:: value (str) – Name of the Chunk Column

setFailOnMissing(value)#

Sets whether to fail the job if a chunk is not found within document. Return empty otherwise.

Parameters:: value (bool) – Whether to fail job on missing chunks

setInputCols(*value)#

Sets column names of input annotations.

Parameters:: *value (List[str]) – Input columns for the annotator

setIsArray(value)#

Sets whether the chunkCol is an array of strings.

Parameters:: value (bool) – Whether the chunkCol is an array of strings

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:: value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline

setLowerCase(value)#

Sets whether to lower case for matching case.

Parameters:: value (bool) – Name of the Id Column

setOutputCol(value)#

Sets output column name of annotations.

Parameters:: value (str) – Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

setParams()#

Sets params for this Doc2ChunkInternal.

Returns:: Doc2ChunkInternal with params set
Return type:: Doc2ChunkInternal

setStartCol(value)#

Sets column that has a reference of where chunk begins.

Parameters:: value (str) – Name of the reference column

setStartColByTokenIndex(value)#

Sets whether start column is prepended by whitespace tokens.

Parameters:: value (bool) – whether start column is prepended by whitespace tokens

transform(dataset: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = None) → pyspark.sql.dataframe.DataFrame#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

dataset (pyspark.sql.DataFrame) – input dataset
params (dict, optional) – an optional param map that overrides embedded params.

Returns:

transformed dataset

Return type:

pyspark.sql.DataFrame

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

sparknlp_jsl.annotator.doc2_chunk_internal#

Module Contents#

Classes#

`sparknlp_jsl.annotator.doc2_chunk_internal`#