`sparknlp_jsl.annotator.ner.zero_shot_ner`#

Module Contents#

Classes#

ZeroShotNerModel

Zero shot named entity recognition based on RoBertaForQuestionAnswering.

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

Bases: sparknlp.annotator.classifier_dl.RoBertaForQuestionAnswering, sparknlp_jsl.common.HasEngine

Zero shot named entity recognition based on RoBertaForQuestionAnswering.

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

Parameters:

entityDefinitions –
A dictionary with definitions of named entities. The keys of dictionary are the entity labels and the values are lists of questions. For example:
```
>>>    {
>>>        "CITY": ["Which city?", "Which town?"],
>>>        "NAME": ["What is her name?", "What is his name?"]
>>>    }
```
predictionThreshold – Minimal confidence score to encode an entity (Default: 0.01f)
ignoreEntities – A list of entity labels which are discarded from the output.

Examples

>>> document_assembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> sentence_detector = annotators.SentenceDetector()\
...     .setInputCols(["document"])\
...     .setOutputCol("sentence")
>>> tokenizer = annotators.Tokenizer()\
...     .setInputCols(["sentence"])\
...     .setOutputCol("token")
>>> zero_shot_ner = ZeroShotNerModel()\
...     .load("/models/sparknlp/zero_shot_ner")\
...     .setEntityDefinitions(
...         {
...             "NAME": ["What is his name?", "What is my name?", "What is her name?"],
...             "CITY": ["Which city?", "Which is the city?"]
...         })\
...     .setInputCols(["sentence", "token"])\
...     .setOutputCol("zero_shot_ner")\
>>> data = spark.createDataFrame(
...         [["My name is Clara, I live in New York and Hellen lives in Paris."]]
...     ).toDF("text")
>>> Pipeline() \
...     .setStages([document_assembler, sentence_detector, tokenizer, zero_shot_ner]) \
...     .fit(data) \
...     .transform(data) \
...     .selectExpr("document", "explode(zero_shot_ner) AS entity")\
...     .select(
...         "document.result",
...         "entity.result",
...         "entity.metadata.word",
...         "entity.metadata.confidence",
...         "entity.metadata.question")\
...     .show(truncate=False)
    +-----------------------------------------------------------------+------+------+----------+------------------+
    |result                                                           |result|word  |confidence|question          |
    +-----------------------------------------------------------------+------+------+----------+------------------+
    |[My name is Clara, I live in New York and Hellen lives in Paris.]|B-CITY|Paris |0.5328949 |Which is the city?|
    |[My name is Clara, I live in New York and Hellen lives in Paris.]|B-NAME|Clara |0.9360068 |What is my name?  |
    |[My name is Clara, I live in New York and Hellen lives in Paris.]|B-CITY|New   |0.83294415|Which city?       |
    |[My name is Clara, I live in New York and Hellen lives in Paris.]|I-CITY|York  |0.83294415|Which city?       |
    |[My name is Clara, I live in New York and Hellen lives in Paris.]|B-NAME|Hellen|0.45366877|What is her name? |
    +-----------------------------------------------------------------+------+------+----------+------------------+

batchSize#

caseSensitive#

coalesceSentences#

configProtoBytes#

engine#

getter_attrs = []#

ignoreEntities#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

maxSentenceLength#

max_length_limit = 512#

name = 'ZeroShotNerModel'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'named_entity'#

outputCol#

predictionThreshold#

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

getCaseSensitive()#

Gets whether to ignore case in tokens for embeddings matching.

Returns:: Whether to ignore case in tokens for embeddings matching
Return type:: bool

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

getEngine()#

Returns:: Deep Learning engine used for this model”
Return type:: str

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

getLazyAnnotator()#: Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getMaxSentenceLength()#

Gets max sentence of the model.

Returns:: Max sentence length to process
Return type:: int

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.

static load(path: str)#

Load a pre-trained ZeroShotNerModel from a local path.

Parameters:: path (str) – Path to the pre-trained model.
Returns:: A pre-trained ZeroShotNerModel.
Return type:: ZeroShotNerModel

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:

RoBertaForQuestionAnswering

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

Download a pre-trained ZeroShotNerModel.

Parameters:

name (str) – Name of the pre-trained model, by default “zero_shot_ner_roberta”
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 ZeroShotNerModel.

Return type:

ZeroShotNerModel

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

setCaseSensitive(value)#

Sets whether to ignore case in tokens for embeddings matching.

Parameters:: value (bool) – Whether to ignore case in tokens for embeddings matching

setConfigProtoBytes(b)#

Sets configProto from tensorflow, serialized into byte array.

Parameters:: b (List[int]) – ConfigProto from tensorflow, serialized into byte array

setEntityDefinitions(definitions: dict)#

Set entity definitions.

Parameters:: definitions (dict[str, list[str]]) –

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

setMaxSentenceLength(value)#

Sets max sentence length to process.

Note that a maximum limit exists depending on the model. If you are working with long single sequences, consider splitting up the input first with another annotator e.g. SentenceDetector.

Parameters:: value (int) – Max sentence length to process

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.zero_shot_ner#

Module Contents#

Classes#

`sparknlp_jsl.annotator.ner.zero_shot_ner`#