sparknlp_jsl.annotator.ner.iob_tagger#

Classes#

class IOBTagger(classname='com.johnsnowlabs.nlp.annotators.ner.IOBTagger', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

Merges token tags and NER labels from chunks in the specified format. For example output columns as inputs from

Input Annotation types	Output Annotation type
TOKEN, CHUNK	NAMED_ENTITY

Parameters:

Scheme – Format of tags, either IOB or BIOES

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.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 = NerConverter().setInputCols(["sentence", "token", "ner"]).setOutputCol("ner_chunk")
...
>>> iobTagger = IOBTagger().setInputCols(["token", "ner_chunk"]).setOutputCol("ner_label")
>>> pipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, embeddings, nerModel, nerConverter, iobTagger])
...
>>> result.selectExpr("explode(ner_label) as a")     ...   .selectExpr("a.begin","a.end","a.result as chunk","a.metadata.word as word")     ...   .where("chunk!='O'").show(5, False)
+-----+---+-----------+-----------+
|begin|end|chunk      |word       |
+-----+---+-----------+-----------+
|5    |15 |B-Age      |63-year-old|
|17   |19 |B-Gender   |man        |
|64   |72 |B-Modifier |recurrent  |
|98   |107|B-Diagnosis|cellulitis |
|110  |119|B-Diagnosis|pneumonias |
+-----+---+-----------+-----------+

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = IOBTaggerModel#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

scheme#

skipLPInputColsValidation = True#

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 (dict, optional) – Extra parameters to copy to the new instance

Returns:

Copy of this instance

Return type:

JavaParams

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 (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)#

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:

paramName (str) – 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.

inputColsValidation(value)#

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).

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.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

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

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()#

setScheme(scheme)#

Sets format of tags, either IOB or BIOES

Parameters:

scheme (str) – Format of tags, either IOB or BIOES

transform(dataset, params=None)#

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()#

Returns an MLWriter instance for this ML instance.