sparknlp_jsl.annotator.metadata_annotation_converter#

Classes#

class MetadataAnnotationConverter(classname='com.johnsnowlabs.annotator.MetadataAnnotationConverter', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

Converts metadata fields in annotations into actual begin, end, or result values.

MetadataAnnotationConverter enables users to override fields in Spark NLP annotations using values from their metadata dictionary. This is especially useful when metadata contains normalized values, corrected character offsets, or alternative representations of the entity or phrase.

The transformation is handled on the Scala side and is compatible with Spark NLP pipelines.

Input Annotation Type	Output Annotation Type
ANY	ANY

Parameters:

• inputType (str) – Type of the input annotation (e.g., “chunk”, “token”).

• resultField (str) – Name of the metadata key to override the result value.

• beginField (str) – Name of the metadata key to override the begin offset.

• endField (str) – Name of the metadata key to override the end offset.

Examples

>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from sparknlp_jsl.annotator import MetadataAnnotationConverter
>>> from pyspark.ml import Pipeline

>>> document_assembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> chunker = TextMatcher().setInputCols("document").setOutputCol("chunk").setEntities(["known location"])
>>> metadata_converter = MetadataAnnotationConverter() \
...     .setInputAnnotatorType("chunk") \
...     .setInputCols("chunk") \
...     .setOutputCol("converted_chunk") \
...     .setResultField("normalized") \
...     .setBeginField("char_start") \
...     .setEndField("char_end")

>>> pipeline = Pipeline(stages=[document_assembler, chunker, metadata_converter])
>>> data = spark.createDataFrame([["This is a known location."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(converted_chunk) as chunk").select("chunk.result", "chunk.begin", "chunk.end").show(truncate=False)
+--------------+-----+---+
|result        |begin|end|
+--------------+-----+---+
|known location|10   |24 |
+--------------+-----+---+

beginField#

endField#

getter_attrs = []#

inputAnnotatorTypes = [True]#

inputCols#

inputType#

lazyAnnotator#

name = 'MetadataAnnotationConverter'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = [True]#

outputCol#

resultField#

skipLPInputColsValidation = True#

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.

setBeginField(value)#

Sets the metadata key that will override the begin offset in the annotation.

Parameters:

value (str) – Key in the metadata map used to override the begin field.

setEndField(value)#

Sets the metadata key that will override the end offset in the annotation.

Parameters:

value (str) – Key in the metadata map used to override the end field.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

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

setInputType(value)#

Sets the value of inputType.

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

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

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()#

setResultField(value)#

Sets the metadata key that will override the result field in the annotation.

Parameters:

value (str) – Key in the metadata map used to override the result field.

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.