sparknlp.annotator.SentimentDLModel#
- class sparknlp.annotator.SentimentDLModel(classname='com.johnsnowlabs.nlp.annotators.classifier.dl.SentimentDLModel', java_model=None)[source]#
Bases:
sparknlp.common.AnnotatorModel
,sparknlp.common.HasStorageRef
SentimentDL, an annotator for multi-class sentiment analysis.
In natural language processing, sentiment analysis is the task of classifying the affective state or subjective view of a text. A common example is if either a product review or tweet can be interpreted positively or negatively.
This is the instantiated model of the
SentimentDLApproach
. For training your own model, please see the documentation of that class.Pretrained models can be loaded with
pretrained()
of the companion object:>>> sentiment = SentimentDLModel.pretrained() \ ... .setInputCols(["sentence_embeddings"]) \ ... .setOutputCol("sentiment")
The default model is
"sentimentdl_use_imdb"
, if no name is provided. It is english sentiment analysis trained on the IMDB dataset. For available pretrained models please see the Models Hub.For extended examples of usage, see the Spark NLP Workshop.
Input Annotation types
Output Annotation type
SENTENCE_EMBEDDINGS
CATEGORY
- Parameters
- configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
- threshold
The minimum threshold for the final result otheriwse it will be neutral, by default 0.6
- thresholdLabel
In case the score is less than threshold, what should be the label. Default is neutral, by default “neutral”
- classes
Tags used to trained this SentimentDLModel
Examples
>>> import sparknlp >>> from sparknlp.base import * >>> from sparknlp.annotator import * >>> from pyspark.ml import Pipeline >>> documentAssembler = DocumentAssembler() \ ... .setInputCol("text") \ ... .setOutputCol("document") >>> useEmbeddings = UniversalSentenceEncoder.pretrained() \ ... .setInputCols(["document"]) \ ... .setOutputCol("sentence_embeddings") >>> sentiment = SentimentDLModel.pretrained("sentimentdl_use_twitter") \ ... .setInputCols(["sentence_embeddings"]) \ ... .setThreshold(0.7) \ ... .setOutputCol("sentiment") >>> pipeline = Pipeline().setStages([ ... documentAssembler, ... useEmbeddings, ... sentiment ... ]) >>> data = spark.createDataFrame([ ... ["Wow, the new video is awesome!"], ... ["bruh what a damn waste of time"] ... ]).toDF("text") >>> result = pipeline.fit(data).transform(data) >>> result.select("text", "sentiment.result").show(truncate=False) +------------------------------+----------+ |text |result | +------------------------------+----------+ |Wow, the new video is awesome!|[positive]| |bruh what a damn waste of time|[negative]| +------------------------------+----------+
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.
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.
Sets configProto from tensorflow, serialized into byte array.
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.
setThreshold
(v)Sets the minimum threshold for the final result otheriwse it will be neutral, by default 0.6.
Sets what the label should be, if the score is less than threshold, by default "neutral".
transform
(dataset[, params])Transforms the input dataset with optional parameters.
write
()Returns an MLWriter instance for this ML instance.
Attributes
classes
configProtoBytes
getter_attrs
inputCols
lazyAnnotator
name
outputCol
Returns all params ordered by name.
storageRef
threshold
thresholdLabel
- 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
- 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='sentimentdl_use_imdb', lang='en', remote_loc=None)[source]#
Downloads and loads a pretrained model.
- Parameters
- namestr, optional
Name of the pretrained model, by default “sentimentdl_use_imdb”
- 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
- SentimentDLModel
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.
- setConfigProtoBytes(b)[source]#
Sets configProto from tensorflow, serialized into byte array.
- Parameters
- bList[int]
ConfigProto from tensorflow, serialized into byte array
- 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
- setThreshold(v)[source]#
Sets the minimum threshold for the final result otheriwse it will be neutral, by default 0.6.
- Parameters
- vfloat
Minimum threshold for the final result
- setThresholdLabel(p)[source]#
Sets what the label should be, if the score is less than threshold, by default “neutral”.
- Parameters
- pstr
The label, if the score is less than threshold
- 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.