sparknlp.annotator.WordEmbeddings#

class sparknlp.annotator.WordEmbeddings[source]#

Bases: sparknlp.common.AnnotatorApproach, sparknlp.common.HasEmbeddingsProperties, sparknlp.common.HasStorage

Word Embeddings lookup annotator that maps tokens to vectors.

For instantiated/pretrained models, see WordEmbeddingsModel.

A custom token lookup dictionary for embeddings can be set with setStoragePath(). Each line of the provided file needs to have a token, followed by their vector representation, delimited by a spaces:

...
are 0.39658191506190343 0.630968081620067 0.5393722253731201 0.8428180123359783
were 0.7535235923631415 0.9699218875629833 0.10397182122983872 0.11833962569383116
stress 0.0492683418305907 0.9415954572751959 0.47624463167525755 0.16790967216778263
induced 0.1535748762292387 0.33498936903209897 0.9235178224122094 0.1158772920395934
...

If a token is not found in the dictionary, then the result will be a zero vector of the same dimension. Statistics about the rate of converted tokens, can be retrieved with WordEmbeddingsModel.withCoverageColumn() and WordEmbeddingsModel.overallCoverage().

For extended examples of usage, see the Spark NLP Workshop.

Input Annotation types	Output Annotation type
`DOCUMENT, TOKEN`	`WORD_EMBEDDINGS`

Parameters

writeBufferSize: Buffer size limit before dumping to disk storage while writing, by default 10000
readCacheSize: Cache size for items retrieved from storage. Increase for performance but higher memory consumption

See also

SentenceEmbeddings: to combine embeddings into a sentence-level representation

Examples

In this example, the file random_embeddings_dim4.txt has the form of the content above.

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> embeddings = WordEmbeddings() \
...     .setStoragePath("src/test/resources/random_embeddings_dim4.txt", ReadAs.TEXT) \
...     .setStorageRef("glove_4d") \
...     .setDimension(4) \
...     .setInputCols(["document", "token"]) \
...     .setOutputCol("embeddings")
>>> embeddingsFinisher = EmbeddingsFinisher() \
...     .setInputCols(["embeddings"]) \
...     .setOutputCols("finished_embeddings") \
...     .setOutputAsVector(True) \
...     .setCleanAnnotations(False)
>>> pipeline = Pipeline() \
...     .setStages([
...       documentAssembler,
...       tokenizer,
...       embeddings,
...       embeddingsFinisher
...     ])
>>> data = spark.createDataFrame([["The patient was diagnosed with diabetes."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(finished_embeddings) as result").show(truncate=False)
+----------------------------------------------------------------------------------+
|result                                                                            |
+----------------------------------------------------------------------------------+
|[0.9439099431037903,0.4707513153553009,0.806300163269043,0.16176554560661316]     |
|[0.7966810464859009,0.5551124811172485,0.8861005902290344,0.28284206986427307]    |
|[0.025029370561242104,0.35177749395370483,0.052506182342767715,0.1887107789516449]|
|[0.08617766946554184,0.8399239182472229,0.5395117998123169,0.7864698767662048]    |
|[0.6599600911140442,0.16109347343444824,0.6041093468666077,0.8913561105728149]    |
|[0.5955275893211365,0.01899011991918087,0.4397728443145752,0.8911281824111938]    |
|[0.9840458631515503,0.7599489092826843,0.9417727589607239,0.8624503016471863]     |
+----------------------------------------------------------------------------------+

Methods

`__init__`()
`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.
`fit`(dataset[, params])	Fits a model to the input dataset with optional parameters.
`fitMultiple`(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
`getCaseSensitive`()	Gets whether to ignore case in tokens for embeddings matching.
`getDimension`()	Gets embeddings dimension.
`getIncludeStorage`()	Gets 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.
`getOutputCol`()	Gets output column name of annotations.
`getParam`(paramName)	Gets a param by its name.
`getParamValue`(paramName)	Gets the value of a parameter.
`getStoragePath`()	Gets path to file.
`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).
`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.
`setCaseSensitive`(value)	Sets whether to ignore case in tokens for embeddings matching.
`setDimension`(value)	Sets embeddings dimension.
`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.
`setReadCacheSize`(v)	Sets cache size for items retrieved from storage.
`setStoragePath`(path, read_as)	Sets path to file.
`setStorageRef`(value)	Sets unique reference name for identification.
`setWriteBufferSize`(v)	Sets buffer size limit before dumping to disk storage while writing, by default 10000.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`caseSensitive`
`dimension`
`getter_attrs`
`includeStorage`
`inputCols`
`lazyAnnotator`
`name`
`outputCol`
`params`	Returns all params ordered by name.
`readCacheSize`
`storagePath`
`storageRef`
`writeBufferSize`

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

fit(dataset, params=None)#

Fits a model to 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. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

fitted model(s)

New in version 1.3.0.

fitMultiple(dataset, paramMaps)#

Fits a model to the input dataset for each param map in paramMaps.

Parameters

dataset – input dataset, which is an instance of pyspark.sql.DataFrame.
paramMaps – A Sequence of param maps.

Returns

A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

New in version 2.3.0.

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.

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

getStoragePath()#

Gets path to file.

Returns

str: path to file

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 type Param.

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.

setCaseSensitive(value)#

Sets whether to ignore case in tokens for embeddings matching.

Parameters

valuebool: Whether to ignore case in tokens for embeddings matching

setDimension(value)#

Sets embeddings dimension.

Parameters

valueint: Embeddings dimension

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

setReadCacheSize(v)[source]#

Sets cache size for items retrieved from storage. Increase for performance but higher memory consumption.

Parameters

vint: Cache size for items retrieved from storage

setStoragePath(path, read_as)#

Sets path to file.

Parameters

pathstr: Path to file
read_asstr: How to interpret the file

Notes

See ReadAs for reading options.

setStorageRef(value)#

Sets unique reference name for identification.

Parameters

valuestr: Unique reference name for identification

setWriteBufferSize(v)[source]#

Sets buffer size limit before dumping to disk storage while writing, by default 10000.

Parameters

vint: Buffer size limit

uid#: A unique id for the object.

write()#: Returns an MLWriter instance for this ML instance.

sparknlp.annotator.Word2VecModel

sparknlp.annotator.WordEmbeddingsModel