sparknlp_jsl.annotator.classification.few_shot_assertion_classifier#

Classes#

class FewShotAssertionClassifierModel(classname='com.johnsnowlabs.nlp.annotators.classification.LargeFewShotClassifierModel', java_model=None)#

Bases: sparknlp_jsl.annotator.classification.large_few_shot_classifier.LargeFewShotClassifierModel

FewShotAssertionClassifierModel does assertion classification using can run large (LLMS based) few shot classifiers based on the SetFit approach.

Input Annotation types	Output Annotation type
DOCUMENT, CHUNK	ASSERTION

batchSize

Batch size

caseSensitive

Whether the classifier is senstivive to text casing

maxSentenceLength

The maximum length of the input text

>>> document_assembler = sparknlp.DocumentAssembler()    ...     .setInputCol("text")    ...     .setOutputCol("document")
...
>>> sentence_detector = SentenceDetector()    ...    .setInputCol("document")    ...    .setOutputCol("sentence")
...
>>> tokenizer = Tokenizer()    ...    .setInputCols(["sentence"])    ...    .setOutputCol("token")
...
>>> embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")    ...    .setInputCols(["sentence", "token"])    ...    .setOutputCol("embeddings")     ...    .setCaseSensitive(False)
...
>>> ner = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models")     ...    .setInputCols(["sentence", "token", "embeddings"])     ...    .setOutputCol("ner")
...
>>> ner_converter = NerConverter()    ...    .setInputCols(["sentence", "token", "ner"])    ...    .setWhiteList("Disease_Syndrome_Disorder", "Hypertension")    ...    .setOutputCol("ner_chunk")
...
>>> few_shot_assertion_classifier = FewShotAssertionClassifierModel().pretrained()    ...     .setInputCols(["sentence", "ner_chunk"])    ...     .setOutputCol("assertion")
...
>>> data = spark.createDataFrame(
...     [["Includes hypertension and chronic obstructive pulmonary disease."]]
...     ).toDF("text")
...
>>> results = sparknlp.base.Pipeline()     ...     .setStages([
...         document_assembler, sentence_detector, tokenizer, embeddings, ner, ner_converter,
...         few_shot_assertion_classifier])     ...     .fit(data)     ...     .transform(data)     ...
>>> results    ...     .selectExpr("assertion.result", "assertion.metadata.chunk", "assertion.metadata.confidence")    ...     .show()

result	chunk	confidence
present absent	hypertension arteriovenous malformations	1.0 1.0

batchSize#

caseSensitive#

getter_attrs = []#

hasDifferentiableHead#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

maxSentenceLength#

max_length_limit = 512#

modelArchitecture#

name = FewShotAssertionClassifierModel#

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

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 labels used to train this model

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)#

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).

static loadSavedModel(folder, spark_session, model_architecture, has_differentiable_head=False)#

Loads a locally saved model.

Parameters:

• folder (str) – Folder of the saved model

• spark_session (pyspark.sql.SparkSession) – The current SparkSession

• model_architecture (str) – The model architecture of the underlying sentence embeddings model, e.g. MPNet or Bert

• has_differentiable_head (bool) – A flag indicating whether the classifier is differentiable

Returns:

The restored model

Return type:

LargeFewShotClassifierModel

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

Downloads and loads a pretrained model.

Parameters:

• name (str, optional) – Name of the pretrained model, by default “assertion_fewshotclassifier”

• lang (str, optional) – Language of the pretrained model, by default “en”

• remote_loc (str, optional) – Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:

The restored model

Return type:

FewShotAssertionClassifierModel

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.

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

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

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()#

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.