sparknlp_jsl.annotator.IOBTagger#
- class sparknlp_jsl.annotator.IOBTagger(classname='com.johnsnowlabs.nlp.annotators.ner.IOBTagger', java_model=None)[source]#
Bases:
AnnotatorModel
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.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 | +-----+---+-----------+-----------+
Methods
__init__
([classname, java_model])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.
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.
Gets current column names of input annotations.
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.
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.
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
()setScheme
(f)Sets format of tags, either IOB or BIOES
transform
(dataset[, params])Transforms the input dataset with optional parameters.
write
()Returns an MLWriter instance for this ML instance.
Attributes
getter_attrs
inputCols
lazyAnnotator
name
outputCol
Returns all params ordered by name.
scheme
- 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 typeParam
.
- 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.
- 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
- setScheme(f)[source]#
Sets format of tags, either IOB or BIOES
- Parameters:
- pairsstr
Format of tags, either IOB or BIOES
- 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.