sparknlp_jsl.annotator.NerConverterInternal#

class sparknlp_jsl.annotator.NerConverterInternal[source]#

Bases: AnnotatorModel

Converts a IOB or IOB2 representation of NER to a user-friendly one, by associating the tokens of recognized entities and their label. Chunks with no associated entity (tagged “O”) are filtered.

Input Annotation types	Output Annotation type
`DOCUMENT, TOKEN, NAMED_ENTITY`	`CHUNK`

Parameters:

whiteList: If defined, list of entities to process. The rest will be ignored. Do not include IOB prefix on labels
blackList: If defined, list of entities to ignore. The rest will be proccessed. Do not include IOB prefix on labels
preservePosition: Whether to preserve the original position of the tokens in the original document or use the modified tokens
greedyMode: Whether to ignore B tags for contiguous tokens of same entity same
threshold: Confidence threshold to filter the chunk entities.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.common import *
>>> from sparknlp.annotator import *
>>> from sparknlp.training import *
>>> import sparknlp_jsl
>>> from sparknlp_jsl.base import *
>>> from sparknlp_jsl.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
>>> data = spark.createDataFrame([["A 63-year-old man presents to the hospital ..."]]).toDF("text")
>>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> sentenceDetector = SentenceDetector().setInputCols(["document"]).setOutputCol("sentence")
>>> tokenizer = Tokenizer().setInputCols(["sentence"]).setOutputCol("token")
>>> embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models").setOutputCol("embs")
>>> nerModel = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models").setInputCols(["sentence", "token", "embs"]).setOutputCol("ner")
>>> nerConverter = NerConverterInternal().setInputCols(["sentence", "token", "ner"]).setOutputCol("ner_chunk")
...
>>> pipeline = Pipeline(stages=[
...     documentAssembler,
...     sentenceDetector,
...     tokenizer,
...     embeddings,
...     nerModel,
...     nerConverter])

Methods

`__init__`()	Initialize this instance with a Java model object.
`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.
`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.
`setBlackList`(entities)	If defined, list of entities to ignore.
`setGreedyMode`(p)	Sets whether to ignore B tags for contiguous tokens of same entity same
`setInputCols`(*value)	Sets column names of input annotations.
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setParams`()
`setPreservePosition`(p)	Sets whether to preserve the original position of the tokens in the original document or use the modified tokens
`setReplaceDictResource`(path[, read_as, options])	Sets replace dictionary pairs
`setReplaceLabels`(labels)	Sets custom relation labels
`setThreshold`(p)	Sets confidence threshold to filter the chunk entities.
`setWhiteList`(entities)	If defined, list of entities to process.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`blackList`
`getter_attrs`
`greedyMode`
`inputCols`
`lazyAnnotator`
`name`
`outputCol`
`params`	Returns all params ordered by name.
`preservePosition`
`replaceDictResource`
`replaceLabels`
`threshold`
`whiteList`

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

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.

setBlackList(entities)[source]#

If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

Parameters:

entitieslist: If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

setGreedyMode(p)[source]#

Sets whether to ignore B tags for contiguous tokens of same entity same

Parameters:

pbool: Whether to ignore B tags for contiguous tokens of same entity same

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

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

setPreservePosition(p)[source]#

Sets whether to preserve the original position of the tokens in the original document or use the modified tokens

Parameters:

pbool: Whether to preserve the original position of the tokens in the original document or use the modified tokens

setReplaceDictResource(path, read_as='TEXT', options=None)[source]#

Sets replace dictionary pairs

Parameters:

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

setReplaceLabels(labels)[source]#

Sets custom relation labels

Parameters:

labelsdict[str, str]: Dictionary which maps old to new labels

setThreshold(p)[source]#

Sets confidence threshold to filter the chunk entities.

Parameters:

pfloat: Confidence threshold to filter the chunk entities.

setWhiteList(entities)[source]#

If defined, list of entities to process. The rest will be ignored. Do not include IOB prefix on labels

Parameters:

entitieslist: If defined, list of entities to process. The rest will be ignored. Do not include IOB prefix on labels

transform(dataset, params=None)#

Transforms 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.

Returns:

transformed dataset

New in version 1.3.0.

uid#: A unique id for the object.

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