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.
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.
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).
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.
setConfigProtoBytes(b)	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.
setThresholdLabel(p)	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
params	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 type Param.

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.