`sparknlp_jsl.annotator.ner.pretrained_zero_shot_ner_chunker`#

Module Contents#

Classes#

PretrainedZeroShotNERChunker

A fine-tuned zero-shot named-entity recognition (NER) model. Performs NER on arbitrary text

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

Bases: sparknlp_jsl.annotator.ner.pretrained_zero_shot_ner.PretrainedZeroShotNER

A fine-tuned zero-shot named-entity recognition (NER) model. Performs NER on arbitrary text

without task-specific labeled training.

In contrast to PretrainedZeroShotNER this annotator directly outputs NER chunks instead of aligning them to provided tokens.

PretrainedZeroShotNER

Input Annotation types: DOCUMENT Output Annotation type: CHUNK

Input Annotation types	Output Annotation type
`DOCUMENT`	`CHUNK`

labels

A list of labels descriving the entities. For example:

>>>    ["person", "location"]

predictionThreshold

Minimal confidence score to encode an entity (Default: 0.5)

>>> text = "Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player."
>>> testData = spark.createDataFrame([[text]], ["text"])
>>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> sentenceDetector = SentenceDetector().setInputCols(["document"]).setOutputCol("sentence")
>>> ner = PretrainedZeroShotNERChunker.pretrained().setInputCols(["sentence"]).setOutputCol("ner_chunk").setLabels(["person", "award", "date", "competitions", "teams"])
>>> pipeline = Pipeline().setStages([documentAssembler, sentenceDetector, ner])
>>> results = pipeline.fit(testData).transform(testData)
>>> results.selectExpr("explode(ner_chunk)").show(1000, truncate=False)
+--------------------------------------------------------------------------------------------------------------------------------------------------+
|col                                                                                                                                               |
+--------------------------------------------------------------------------------------------------------------------------------------------------+
|{chunk, 2, 37, Cristiano Ronaldo dos Santos Aveiro, {sentence -> 0, entity -> person, confidence -> 0.9144007, ner_source -> ner_chunk}, []}      |
|{chunk, 93, 109, 5 February

1985, {sentence -> 1, entity -> date, confidence -> 0.99999976, ner_source -> ner_chunk}, []} |

|{chunk, 196, 213, Saudi Pro

League, {sentence -> 1, entity -> competitions, confidence -> 0.9926515, ner_source -> ner_chunk}, []} |

|{chunk, 219, 227, Al Nassr, {sentence -> 1, entity -> teams, confidence -> 0.99384415, ner_source -> ner_chunk}, []} | |{chunk, 321, 328, Ronaldo, {sentence -> 2, entity -> person, confidence -> 0.999997, ner_source -> ner_chunk}, []} | |{chunk, 342, 353, Ballon d’Or, {sentence -> 2, entity -> award, confidence -> 0.95896983, ner_source -> ner_chunk}, []} | |{chunk, 385, 422, UEFA Men’s Player of the Year

Awards, {sentence -> 2, entity -> award, confidence -> 0.9687164, ner_source -> ner_chunk}, []}|

|{chunk, 433, 454, European Golden Shoes, {sentence -> 2, entity -> award, confidence -> 0.999326, ner_source -> ner_chunk}, []} | +————————————————————————————————————————————————–+

batchSize#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

labels#

lazyAnnotator#

name = 'PretrainedZeroShotNERChunker'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'chunk'#

outputCol#

predictionThreshold#

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

getBatchSize()#

Gets current batch size.

Returns:: Current batch size
Return type:: int

getInputCols()#: Gets current column names of input annotations.

getLabels()#: Returns the list of entities which are recognized.

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).

static loadSavedModel(folder, spark_session)#

Loads a locally saved model.

Parameters:

folder (str) – Folder of the saved model
spark_session (pyspark.sql.SparkSession) – The current SparkSession

Returns:

The restored model

Return type:

AutoGGUFModel

static pretrained(name='zeroshot_ner_deid_subentity_merged_medium', lang='en', remote_loc='clinical/models')#

Download a pre-trained PretrainedZeroShotNERChunker.

Parameters:

name (str) – Name of the pre-trained model, by default “zeroshot_ner_deid_subentity_merged_medium”
lang (str) – Language of the pre-trained model, by default “en”
remote_loc (str) – Remote location of the pre-trained model. If None, use the open-source location. Other values are “clinical/models”, “finance/models”, or “legal/models”.

Returns:

A pre-trained PretrainedZeroShotNERChunker.

Return type:

PretrainedZeroShotNERChunker

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.

setBatchSize(v)#

Sets batch size.

Parameters:: v (int) – Batch size

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

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

setLabels(labels: list)#

Set entity descriptions.

Parameters:: labels (list[str]) – entity descriptions

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()#

setPredictionThreshold(threshold: float)#

Sets the minimal confidence score to encode an entity.

Parameters:: threshold (float) – minimal confidence score to encode an entity (default is 0.1)

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.

sparknlp_jsl.annotator.ner.pretrained_zero_shot_ner_chunker#

Module Contents#

Classes#

`sparknlp_jsl.annotator.ner.pretrained_zero_shot_ner_chunker`#