sparknlp_jsl.annotator.EntityChunkEmbeddings#

class sparknlp_jsl.annotator.EntityChunkEmbeddings(classname='com.johnsnowlabs.nlp.annotators.embeddings.EntityChunkEmbeddings', java_model=None)[source]#

Bases: BertSentenceEmbeddings

Weighted average embeddings of multiple named entities chunk annotations

Input Annotation types

Output Annotation type

DEPENDENCY, CHUNK

SENTENCE_EMBEDDINGS

Input Annotation types	Output Annotation type
`DEPENDENCY, CHUNK`	`SENTENCE_EMBEDDINGS`

Parameters:

targetEntities: Target entities and their related entities
entityWeights: Relative weights of entities.
maxSyntacticDistance: Maximal syntactic distance between related entities. Default value is 2.

result	drug_embedding”
metformin 125 mg 250 mg coumadin one pill paracetamol	[-0.267413, 0.07614058, -0.5620966, 0.83838946, 0.8911504] [0.22319649, -0.07094894, -0.6885556, 0.79176235, 0.82672405] [-0.10939768, -0.29242, -0.3574444, 0.3981813, 0.79609615]

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.
`explainParams`()	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.
`getBatchSize`()	Gets current batch size.
`getCaseSensitive`()	Gets whether to ignore case in tokens for embeddings matching.
`getDimension`()	Gets embeddings dimension.
`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.
`getOutputCol`()	Gets output column name of annotations.
`getParam`(paramName)	Gets a param by its name.
`getParamValue`(paramName)	Gets the value of a parameter.
`getStorageRef`()	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).
`loadSavedModel`(folder, spark_session)	Loads a locally saved model.
`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.
`setBatchSize`(v)	Sets batch size.
`setCaseSensitive`(value)	Sets whether to ignore case in tokens for embeddings matching.
`setConfigProtoBytes`(b)	Sets configProto from tensorflow, serialized into byte array.
`setDimension`(value)	Sets embeddings dimension.
`setEntityWeights`([weights])	Sets the relative weights of the embeddings of specific entities. By default the dictionary is empty and
`setInputCols`(*value)	Sets column names of input annotations.
`setIsLong`(value)	Sets whether to use Long type instead of Int type for inputs buffer.
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setMaxSentenceLength`(value)	Sets max sentence length to process.
`setMaxSyntacticDistance`(distance)	Sets the maximal syntactic distance between related entities. Default value is 2. Parameters ---------- distance : int Maximal syntactic distance.
`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.
`setTargetEntities`([entities])	Sets the target entities and maps them to their related entities.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`batchSize`
`caseSensitive`
`configProtoBytes`
`dimension`
`entityWeights`
`getter_attrs`
`inputCols`
`isLong`
`lazyAnnotator`
`maxSentenceLength`
`maxSyntacticDistance`
`name`
`outputCol`
`params`	Returns all params ordered by name.
`storageRef`
`targetEntities`

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

getBatchSize()#

Gets current batch size.

Returns:

int: Current batch size

getCaseSensitive()#

Gets whether to ignore case in tokens for embeddings matching.

Returns:

bool: Whether to ignore case in tokens for embeddings matching

getDimension()#: Gets embeddings dimension.

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).

static loadSavedModel(folder, spark_session)#

Loads a locally saved model.

Parameters:

folderstr: Folder of the saved model
spark_sessionpyspark.sql.SparkSession: The current SparkSession

Returns:

BertSentenceEmbeddings: The restored model

property params#: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

static pretrained(name='sbiobert_base_cased_mli', lang='en', remote_loc='clinical/models')[source]#

Downloads and loads a pretrained model.

Parameters:

namestr, optional: Name of the pretrained model, by default “sent_small_bert_L2_768”
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:

BertSentenceEmbeddings: 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.

setBatchSize(v)#

Sets batch size.

Parameters:

vint: Batch size

setCaseSensitive(value)#

Sets whether to ignore case in tokens for embeddings matching.

Parameters:

valuebool: Whether to ignore case in tokens for embeddings matching

setConfigProtoBytes(b)#

Sets configProto from tensorflow, serialized into byte array.

Parameters:

bList[int]: ConfigProto from tensorflow, serialized into byte array

setDimension(value)#

Sets embeddings dimension.

Parameters:

valueint: Embeddings dimension

setEntityWeights(weights=None)[source]#

Sets the relative weights of the embeddings of specific entities. By default the dictionary is empty and: all entities have equal weights. If non-empty and some entity is not in it, then its weight is set to 0.

Parameters:

weights:dict[str, float]: Dictionary with the relative weighs of entities. The notation TARGET_ENTITY:RELATED_ENTITY can be used to specify the weight of a entity which is related to specific target entity (e.g. “DRUG:SYMPTOM”: 0.3). Entity names are case-insensitive.

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*valuestr: Input columns for the annotator

setIsLong(value)#

Sets whether to use Long type instead of Int type for inputs buffer.

Some Bert models require Long instead of Int.

Parameters:

valuebool: Whether to use Long type instead of Int type for inputs buffer

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

valuebool: Whether Annotator should be evaluated lazily in a RecursivePipeline

setMaxSentenceLength(value)#

Sets max sentence length to process.

Parameters:

valueint: Max sentence length to process

setMaxSyntacticDistance(distance)[source]#: Sets the maximal syntactic distance between related entities. Default value is 2. Parameters ———- distance : int

Maximal syntactic distance

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

setTargetEntities(entities=None)[source]#

Sets the target entities and maps them to their related entities. A target entity with an empty list of related entities means all other entities are assumed to be related to it.

Parameters:

entities: dict[str, list[str]]: Dictionary with target and related entities (TARGET: [RELATED1, RELATED2,…]). If the list of related entities is empty, then all non-target entities are considered. Entity names are case insensitive.

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.