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()
andWordEmbeddingsModel.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.
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.
Gets whether to ignore case in tokens for embeddings matching.
Gets embeddings dimension.
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 path to file.
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.
Sets cache size for items retrieved from storage.
setStoragePath
(path, read_as)Sets path to file.
setStorageRef
(value)Sets unique reference name for identification.
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
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 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)’.
- 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.