sparknlp_jsl.annotator.ChunkMapperModel#
- class sparknlp_jsl.annotator.ChunkMapperModel(classname='com.johnsnowlabs.nlp.annotators.chunker.ChunkMapperModel', java_model=None)[source]#
Bases:
AnnotatorModel
The chunk mapper Approach load a JsonDictionary that have the relations to be mapped in the ChunkMapperModel
Input Annotation types
Output Annotation type
CHUNK
LABEL_DEPENDENCY
- Parameters:
- dictionary
Dictionary path where is the json that contains the mappinmgs columns
- rel
Relation that we going to use to map the chunk
- lowerCase
Parameter to decide if we going to use the chunk mapper or not
mappings
[{labeled_dependency, 22, 35, Analgesic, {chunk -> 0, relation -> action, confidence -> 0.56995, all_relations -> Antipyretic, entity -> Warfarina Lusa, sentence -> 0}, []}, {labeled_dependency, 41, 50, NONE, {entity -> amlodipine, sentence -> 0, chunk -> 1, confidence -> 0.9989}, []}, {labeled_dependency, 55, 56, NONE, {entity -> MG, sentence -> 0, chunk -> 2, confidence -> 0.9123}, []}]
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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 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.
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).
pretrained
([name, lang, remote_loc])Downloads and loads a pretrained model.
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.
Whether to decide if we want to send multi-chunk tokens or only single token chunks Parameters ---------- mc : bool "Whether to decide if we want to send multi-chunk tokens or only single token chunks
setInputCols
(*value)Sets column names of input annotations.
setLazyAnnotator
(value)Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
setLowerCase
(lc)Set if we want to save the keys of the dictionary in lower case or not Parameters ---------- lc : bool Parameter that select if you want to use the keys in lower case or not
Whether to decide if we want to send multi-chunk tokens or only single token chunks Parameters ---------- mc : bool "Whether to decide if we want to send multi-chunk tokens or only single token chunks
setOutputCol
(value)Sets output column name of annotations.
setParamValue
(paramName)Sets the value of a parameter.
setParams
()setRel
(r)setRels
(rs)transform
(dataset[, params])Transforms the input dataset with optional parameters.
write
()Returns an MLWriter instance for this ML instance.
Attributes
allowMultiTokenChunk
dictionary
getter_attrs
inputCols
lazyAnnotator
lowerCase
multivaluesRelations
name
outputCol
Returns all params ordered by name.
rel
rels
- 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
- 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
- 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
.
- static pretrained(name='', lang='en', remote_loc='clinical/models')[source]#
Downloads and loads a pretrained model.
- Parameters:
- namestr, optional
Name of the pretrained model.
- langstr, optional
Language of the pretrained model, by default “en”
- remote_locstr, optional
Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.
- Returns:
- ChunkMapperModel
The restored model
- 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.
- setAllowMultiTokenChunk(mc)[source]#
Whether to decide if we want to send multi-chunk tokens or only single token chunks Parameters ———- mc : bool
“Whether to decide if we want to send multi-chunk tokens or only single token chunks
- 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
- setLowerCase(lc)[source]#
Set if we want to save the keys of the dictionary in lower case or not Parameters ———- lc : bool
Parameter that select if you want to use the keys in lower case or not
- setMultivaluesRelations(mc)[source]#
Whether to decide if we want to send multi-chunk tokens or only single token chunks Parameters ———-
- mcbool
“Whether to decide if we want to send multi-chunk tokens or only single token chunks
- 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
- 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.