`sparknlp_jsl.annotator.chunk2_token`#

Module Contents#

Classes#

Chunk2Token

Converts CHUNK type annotations into TOKEN type

class Chunk2Token(classname='com.johnsnowlabs.nlp.annotators.Chunk2Token', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

Converts CHUNK type annotations into TOKEN type

Input Annotation types	Output Annotation type
`CHUNK`	`TOKEN`

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> chunkAssembler = sentenceDetector = SentenceDetector().setInputCols("document").setOutputCol("sentence")
>>> tokenizer = Tokenizer().setInputCols("sentence").setOutputCol("token")
>>> nGramGenerator = NGramGenerator().setInputCols("token").setOutputCol("ngrams") \
...     .setDelimiter("_").setN(2).setEnableCumulative(False)
>>> chunk2Token = Chunk2Token().setInputCols("ngrams").setOutputCol("ngram_tokens")
>>> data = spark.createDataFrame([["A 63-year-old man presents to the hospital."]]).toDF("text")
>>> pipeline = Pipeline() \
...     .setStages([documentAssembler, chunkAssembler, tokenizer, nGramGenerator, chunk2Token]).fit(data)
>>> result = pipeline.transform(data)
>>> result.selectExpr("ngram_tokens").show(truncate=False)
+----------------------------------------------------------------+
|result                                                          |
+----------------------------------------------------------------+
|{token, 0, 12, A_63-year-old, {sentence -> 0, chunk -> 0}, []}  |
|{token, 2, 16, 63-year-old_man, {sentence -> 0, chunk -> 1}, []}|
|{token, 14, 25, man_presents, {sentence -> 0, chunk -> 2}, []}  |
|{token, 18, 28, presents_to, {sentence -> 0, chunk -> 3}, []}   |
|{token, 27, 32, to_the, {sentence -> 0, chunk -> 4}, []}        |
|{token, 30, 41, the_hospital, {sentence -> 0, chunk -> 5}, []}  |
|{token, 34, 42, hospital_., {sentence -> 0, chunk -> 6}, []}    |
+----------------------------------------------------------------+

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = Chunk2Token#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

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()#

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.

sparknlp_jsl.annotator.chunk2_token#

Module Contents#

Classes#

`sparknlp_jsl.annotator.chunk2_token`#