sparknlp_jsl.annotator.assertion.fewshot_assertion_sentence_converter
#
Module Contents#
Classes#
- class FewShotAssertionSentenceConverter#
Bases:
sparknlp.internal.AnnotatorTransformer
Input Annotation types
Output Annotation type
TOKEN
CHUNK
- getter_attrs = []#
- inputAnnotatorTypes#
- inputCols#
- kwargs#
- name = 'FewShotAssertionSentenceConverter'#
- outputAnnotatorType#
- outputCol#
- scopeWindow#
- 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
- 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.
- 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.
- 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).
- 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.
- setParamValue(paramName)#
Sets the value of a parameter.
- Parameters:
paramName (str) – Name of the parameter
- setParams()#
- setScopeWindow(value: list)#
Sets the scope of the window of the assertion expression.
- Parameters:
value (list) – Left and right offset if the scope window. Offsets must be non-negative values.
- 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 datasetparams (dict, optional) – an optional param map that overrides embedded params.
- Returns:
transformed dataset
- Return type:
- write() JavaMLWriter #
Returns an MLWriter instance for this ML instance.