sparknlp.annotator.Doc2VecModel#
- class sparknlp.annotator.Doc2VecModel(classname='com.johnsnowlabs.nlp.embeddings.Doc2VecModel', java_model=None)[source]#
Bases:
sparknlp.common.AnnotatorModel
,sparknlp.common.HasStorageRef
,sparknlp.common.HasEmbeddingsProperties
Word2Vec model that creates vector representations of words in a text corpus.
The algorithm first constructs a vocabulary from the corpus and then learns vector representation of words in the vocabulary. The vector representation can be used as features in natural language processing and machine learning algorithms.
We use Word2Vec implemented in Spark ML. It uses skip-gram model in our implementation and a hierarchical softmax method to train the model. The variable names in the implementation match the original C implementation.
This is the instantiated model of the
Doc2VecApproach
. For training your own model, please see the documentation of that class.Pretrained models can be loaded with
pretrained()
of the companion object:>>> embeddings = Doc2VecModel.pretrained() \ ... .setInputCols(["token"]) \ ... .setOutputCol("embeddings")
The default model is “doc2vec_gigaword_300”, if no name is provided.
Input Annotation types
Output Annotation type
TOKEN
SENTENCE_EMBEDDINGS
- Parameters
- vectorSize
The dimension of codes after transforming from words (> 0) , by default 100
References
For the original C implementation, see https://code.google.com/p/word2vec/
For the research paper, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality.
Examples
>>> 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 = Doc2VecModel.pretrained() \ ... .setInputCols(["token"]) \ ... .setOutputCol("embeddings") >>> embeddingsFinisher = EmbeddingsFinisher() \ ... .setInputCols(["embeddings"]) \ ... .setOutputCols("finished_embeddings") \ ... .setOutputAsVector(True) >>> pipeline = Pipeline().setStages([ ... documentAssembler, ... tokenizer, ... embeddings, ... embeddingsFinisher ... ]) >>> data = spark.createDataFrame([["This is a sentence."]]).toDF("text") >>> result = pipeline.fit(data).transform(data) >>> result.selectExpr("explode(finished_embeddings) as result").show(1, 80) +--------------------------------------------------------------------------------+ | result| +--------------------------------------------------------------------------------+ |[0.06222493574023247,0.011579325422644615,0.009919632226228714,0.109361454844...| +--------------------------------------------------------------------------------+
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 embeddings dimension.
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).
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.
setDimension
(value)Sets embeddings dimension.
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.
setVectorSize
(vectorSize)Sets vector size (default: 100).
transform
(dataset[, params])Transforms the input dataset with optional parameters.
write
()Returns an MLWriter instance for this ML instance.
Attributes
dimension
getter_attrs
inputCols
lazyAnnotator
name
outputCol
Returns all params ordered by name.
storageRef
vectorSize
- 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
- 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).
- property params#
Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
- static pretrained(name='doc2vec_gigaword_300', lang='en', remote_loc=None)[source]#
Downloads and loads a pretrained model.
- Parameters
- namestr, optional
Name of the pretrained model, by default “doc2vec_wiki”
- 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
- Doc2VecModel
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.
- setDimension(value)#
Sets embeddings dimension.
- Parameters
- valueint
Embeddings dimension
- 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.