sparknlp.annotator.EntityRulerModel#
- class sparknlp.annotator.EntityRulerModel(classname='com.johnsnowlabs.nlp.annotators.er.EntityRulerModel', java_model=None)[source]#
Bases:
sparknlp.common.AnnotatorModel
,sparknlp.common.HasStorageModel
Instantiated model of the EntityRulerApproach. For usage and examples see the documentation of the main class.
Input Annotation types
Output Annotation type
DOCUMENT, TOKEN
CHUNK
Methods
__init__
([classname, java_model])Initialize this instance with a Java model object.
clear
(param)Clears a param from the param map if it has been explicitly set.
copy
([extra])Creates a copy of this instance with the same uid and some extra params.
explainParam
(param)Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap
([extra])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.
Gets whether to ignore case in tokens for embeddings matching.
Gets whether to include indexed storage in trained model.
Gets current column names of input annotations.
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.
Gets output column name of annotations.
getParam
(paramName)Gets a param by its name.
getParamValue
(paramName)Gets the value of a parameter.
Gets unique reference name for identification.
hasDefault
(param)Checks whether a param has a default value.
hasParam
(paramName)Tests whether this instance contains a param with a given (string) name.
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.
load
(path)Reads an ML instance from the input path, a shortcut of read().load(path).
loadStorage
(path, spark, storage_ref)loadStorages
(path, spark, storage_ref, databases)pretrained
(name[, lang, remote_loc])read
()Returns an MLReader instance for this class.
save
(path)Save this ML instance to the given path, a shortcut of 'write().save(path)'.
saveStorage
(path, spark)Saves the current model to storage.
set
(param, value)Sets a parameter in the embedded param map.
setCaseSensitive
(value)Sets whether to ignore case in tokens for embeddings matching.
setIncludeStorage
(value)Sets whether to include indexed storage in trained model.
setInputCols
(*value)Sets column names of input annotations.
setLazyAnnotator
(value)Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
setOutputCol
(value)Sets output column name of annotations.
setParamValue
(paramName)Sets the value of a parameter.
setParams
()setStorageRef
(value)Sets unique reference name for identification.
transform
(dataset[, params])Transforms the input dataset with optional parameters.
write
()Returns an MLWriter instance for this ML instance.
Attributes
caseSensitive
database
getter_attrs
includeStorage
inputCols
lazyAnnotator
name
outputCol
Returns all params ordered by name.
storageRef
- 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 – Extra parameters to copy to the new instance
- Returns
Copy of this instance
- 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 – extra param values
- Returns
merged param map
- getCaseSensitive()#
Gets whether to ignore case in tokens for embeddings matching.
- Returns
- bool
Whether to ignore case in tokens for embeddings matching
- getIncludeStorage()#
Gets whether to include indexed storage in trained model.
- Returns
- bool
Whether to include indexed storage in trained model
- 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
- paramNamestr
Name of the parameter
- getStorageRef()#
Gets unique reference name for identification.
- Returns
- str
Unique reference name for identification
- hasDefault(param)#
Checks whether a param has a default value.
- hasParam(paramName)#
Tests whether this instance contains a param with a given (string) name.
- 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).
- property params#
Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
- 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)’.
- saveStorage(path, spark)#
Saves the current model to storage.
- Parameters
- pathstr
Path for saving the model.
- spark
pyspark.sql.SparkSession
The current SparkSession
- set(param, value)#
Sets a parameter in the embedded param map.
- setCaseSensitive(value)#
Sets whether to ignore case in tokens for embeddings matching.
- Parameters
- valuebool
Whether to ignore case in tokens for embeddings matching
- setIncludeStorage(value)#
Sets whether to include indexed storage in trained model.
- Parameters
- valuebool
Whether to include indexed storage in trained model
- setInputCols(*value)#
Sets column names of input annotations.
- Parameters
- *valuestr
Input columns for the annotator
- setLazyAnnotator(value)#
Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
- Parameters
- valuebool
Whether Annotator should be evaluated lazily in a RecursivePipeline
- setOutputCol(value)#
Sets output column name of annotations.
- Parameters
- valuestr
Name of output column
- setParamValue(paramName)#
Sets the value of a parameter.
- Parameters
- paramNamestr
Name of the parameter
- setStorageRef(value)#
Sets unique reference name for identification.
- Parameters
- valuestr
Unique reference name for identification
- transform(dataset, params=None)#
Transforms the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – an optional param map that overrides embedded params.
- Returns
transformed dataset
New in version 1.3.0.
- uid#
A unique id for the object.
- write()#
Returns an MLWriter instance for this ML instance.