class SentenceEmbeddings extends AnnotatorModel[SentenceEmbeddings] with HasSimpleAnnotate[SentenceEmbeddings] with HasEmbeddingsProperties with HasStorageRef
Converts the results from WordEmbeddings, BertEmbeddings, or ElmoEmbeddings into sentence or document embeddings by either summing up or averaging all the word embeddings in a sentence or a document (depending on the inputCols).
This can be configured with setPoolingStrategy
, which either be "AVERAGE"
or "SUM"
.
For more extended examples see the Examples. and the SentenceEmbeddingsTestSpec.
Example
import spark.implicits._ import com.johnsnowlabs.nlp.base.DocumentAssembler import com.johnsnowlabs.nlp.annotators.Tokenizer import com.johnsnowlabs.nlp.embeddings.WordEmbeddingsModel import com.johnsnowlabs.nlp.embeddings.SentenceEmbeddings import com.johnsnowlabs.nlp.EmbeddingsFinisher import org.apache.spark.ml.Pipeline val documentAssembler = new DocumentAssembler() .setInputCol("text") .setOutputCol("document") val tokenizer = new Tokenizer() .setInputCols(Array("document")) .setOutputCol("token") val embeddings = WordEmbeddingsModel.pretrained() .setInputCols("document", "token") .setOutputCol("embeddings") val embeddingsSentence = new SentenceEmbeddings() .setInputCols(Array("document", "embeddings")) .setOutputCol("sentence_embeddings") .setPoolingStrategy("AVERAGE") val embeddingsFinisher = new EmbeddingsFinisher() .setInputCols("sentence_embeddings") .setOutputCols("finished_embeddings") .setOutputAsVector(true) .setCleanAnnotations(false) val pipeline = new Pipeline() .setStages(Array( documentAssembler, tokenizer, embeddings, embeddingsSentence, embeddingsFinisher )) val data = Seq("This is a sentence.").toDF("text") val result = pipeline.fit(data).transform(data) result.selectExpr("explode(finished_embeddings) as result").show(5, 80) +--------------------------------------------------------------------------------+ | result| +--------------------------------------------------------------------------------+ |[-0.22093398869037628,0.25130119919776917,0.41810303926467896,-0.380883991718...| +--------------------------------------------------------------------------------+
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- SentenceEmbeddings
- HasStorageRef
- HasEmbeddingsProperties
- HasProtectedParams
- HasSimpleAnnotate
- AnnotatorModel
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- HasOutputAnnotationCol
- HasInputAnnotationCols
- HasOutputAnnotatorType
- ParamsAndFeaturesWritable
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implicit
class
ProtectedParam[T] extends Param[T]
- Definition Classes
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-
type
AnnotationContent = Seq[Row]
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
type
AnnotatorType = String
- Definition Classes
- HasOutputAnnotatorType
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
$[T](param: Param[T]): T
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def
$$[T](feature: StructFeature[T]): T
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def
$$[K, V](feature: MapFeature[K, V]): Map[K, V]
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def
$$[T](feature: SetFeature[T]): Set[T]
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def
$$[T](feature: ArrayFeature[T]): Array[T]
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final
def
==(arg0: Any): Boolean
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def
_transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
def
afterAnnotate(dataset: DataFrame): DataFrame
- Attributes
- protected
- Definition Classes
- SentenceEmbeddings → AnnotatorModel
-
def
annotate(annotations: Seq[Annotation]): Seq[Annotation]
takes a document and annotations and produces new annotations of this annotator's annotation type
takes a document and annotations and produces new annotations of this annotator's annotation type
- annotations
Annotations that correspond to inputAnnotationCols generated by previous annotators if any
- returns
any number of annotations processed for every input annotation. Not necessary one to one relationship
- Definition Classes
- SentenceEmbeddings → HasSimpleAnnotate
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
beforeAnnotate(dataset: Dataset[_]): Dataset[_]
- Attributes
- protected
- Definition Classes
- SentenceEmbeddings → AnnotatorModel
-
final
def
checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
clear(param: Param[_]): SentenceEmbeddings.this.type
- Definition Classes
- Params
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
copy(extra: ParamMap): SentenceEmbeddings
requirement for annotators copies
requirement for annotators copies
- Definition Classes
- RawAnnotator → Model → Transformer → PipelineStage → Params
-
def
copyValues[T <: Params](to: T, extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
-
def
createDatabaseConnection(database: Name): RocksDBConnection
- Definition Classes
- HasStorageRef
-
final
def
defaultCopy[T <: Params](extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
-
def
dfAnnotate: UserDefinedFunction
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column
- returns
udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation
- Definition Classes
- HasSimpleAnnotate
-
val
dimension: ProtectedParam[Int]
Number of embedding dimensions (Default:
100
)Number of embedding dimensions (Default:
100
)- Definition Classes
- SentenceEmbeddings → HasEmbeddingsProperties
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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def
equals(arg0: Any): Boolean
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def
explainParam(param: Param[_]): String
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def
explainParams(): String
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def
extraValidate(structType: StructType): Boolean
- Attributes
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- RawAnnotator
-
def
extraValidateMsg: String
Override for additional custom schema checks
Override for additional custom schema checks
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- protected
- Definition Classes
- RawAnnotator
-
final
def
extractParamMap(): ParamMap
- Definition Classes
- Params
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final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
-
val
features: ArrayBuffer[Feature[_, _, _]]
- Definition Classes
- HasFeatures
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def
finalize(): Unit
- Attributes
- protected[lang]
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- @throws( classOf[java.lang.Throwable] )
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def
get[T](feature: StructFeature[T]): Option[T]
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def
get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
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def
get[T](feature: SetFeature[T]): Option[Set[T]]
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def
get[T](feature: ArrayFeature[T]): Option[Array[T]]
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final
def
get[T](param: Param[T]): Option[T]
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final
def
getClass(): Class[_]
- Definition Classes
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- Annotations
- @native()
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final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getDimension: Int
Number of embedding dimensions (Default:
100
)Number of embedding dimensions (Default:
100
)- Definition Classes
- SentenceEmbeddings → HasEmbeddingsProperties
-
def
getInputCols: Array[String]
- returns
input annotations columns currently used
- Definition Classes
- HasInputAnnotationCols
-
def
getLazyAnnotator: Boolean
- Definition Classes
- CanBeLazy
-
final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
-
final
def
getOutputCol: String
Gets annotation column name going to generate
Gets annotation column name going to generate
- Definition Classes
- HasOutputAnnotationCol
-
def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
-
def
getStorageRef: String
- Definition Classes
- HasStorageRef
-
final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
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def
hasParam(paramName: String): Boolean
- Definition Classes
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def
hasParent: Boolean
- Definition Classes
- Model
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
val
inputAnnotatorTypes: Array[AnnotatorType]
Input annotator type : DOCUMENT, WORD_EMBEDDINGS
Input annotator type : DOCUMENT, WORD_EMBEDDINGS
- Definition Classes
- SentenceEmbeddings → HasInputAnnotationCols
-
final
val
inputCols: StringArrayParam
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
isDefined(param: Param[_]): Boolean
- Definition Classes
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
isSet(param: Param[_]): Boolean
- Definition Classes
- Params
-
def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
val
lazyAnnotator: BooleanParam
- Definition Classes
- CanBeLazy
-
def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String): Unit
- Attributes
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- Logging
-
def
logError(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
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- Definition Classes
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def
logError(msg: ⇒ String): Unit
- Attributes
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- Logging
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
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- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String): Unit
- Attributes
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- Definition Classes
- Logging
-
def
logName: String
- Attributes
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- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
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- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
msgHelper(schema: StructType): String
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
onWrite(path: String, spark: SparkSession): Unit
- Attributes
- protected
- Definition Classes
- ParamsAndFeaturesWritable
-
val
optionalInputAnnotatorTypes: Array[String]
- Definition Classes
- HasInputAnnotationCols
-
val
outputAnnotatorType: AnnotatorType
Output annotator type : SENTENCE_EMBEDDINGS
Output annotator type : SENTENCE_EMBEDDINGS
- Definition Classes
- SentenceEmbeddings → HasOutputAnnotatorType
-
final
val
outputCol: Param[String]
- Attributes
- protected
- Definition Classes
- HasOutputAnnotationCol
-
lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
var
parent: Estimator[SentenceEmbeddings]
- Definition Classes
- Model
-
val
poolingStrategy: Param[String]
Choose how you would like to aggregate Word Embeddings to Sentence Embeddings (Default:
"AVERAGE"
).Choose how you would like to aggregate Word Embeddings to Sentence Embeddings (Default:
"AVERAGE"
). Can either be"AVERAGE"
or"SUM"
. -
def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
def
set[T](param: ProtectedParam[T], value: T): SentenceEmbeddings.this.type
Sets the value for a protected Param.
Sets the value for a protected Param.
If the parameter was already set, it will not be set again. Default values do not count as a set value and can be overridden.
- T
Type of the parameter
- param
Protected parameter to set
- value
Value for the parameter
- returns
This object
- Definition Classes
- HasProtectedParams
-
def
set[T](feature: StructFeature[T], value: T): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[K, V](feature: MapFeature[K, V], value: Map[K, V]): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: SetFeature[T], value: Set[T]): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: ArrayFeature[T], value: Array[T]): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
set(paramPair: ParamPair[_]): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): SentenceEmbeddings.this.type
- Definition Classes
- Params
-
def
setDefault[T](feature: StructFeature[T], value: () ⇒ T): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
setDefault(paramPairs: ParamPair[_]*): SentenceEmbeddings.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): SentenceEmbeddings.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
-
def
setDimension(value: Int): SentenceEmbeddings.this.type
- Definition Classes
- HasEmbeddingsProperties
-
final
def
setInputCols(value: String*): SentenceEmbeddings.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setInputCols(value: Array[String]): SentenceEmbeddings.this.type
Overrides required annotators column if different than default
Overrides required annotators column if different than default
- Definition Classes
- HasInputAnnotationCols
-
def
setLazyAnnotator(value: Boolean): SentenceEmbeddings.this.type
- Definition Classes
- CanBeLazy
-
final
def
setOutputCol(value: String): SentenceEmbeddings.this.type
Overrides annotation column name when transforming
Overrides annotation column name when transforming
- Definition Classes
- HasOutputAnnotationCol
-
def
setParent(parent: Estimator[SentenceEmbeddings]): SentenceEmbeddings
- Definition Classes
- Model
-
def
setPoolingStrategy(strategy: String): SentenceEmbeddings.this.type
Choose how you would like to aggregate Word Embeddings to Sentence Embeddings (Default:
"AVERAGE"
).Choose how you would like to aggregate Word Embeddings to Sentence Embeddings (Default:
"AVERAGE"
). Can either be"AVERAGE"
or"SUM"
. -
def
setStorageRef(value: String): SentenceEmbeddings.this.type
- Definition Classes
- HasStorageRef
-
val
storageRef: Param[String]
Unique identifier for storage (Default:
this.uid
)Unique identifier for storage (Default:
this.uid
)- Definition Classes
- HasStorageRef
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
final
def
transform(dataset: Dataset[_]): DataFrame
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
- dataset
Dataset[Row]
- Definition Classes
- AnnotatorModel → Transformer
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
final
def
transformSchema(schema: StructType): StructType
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
- Definition Classes
- RawAnnotator → PipelineStage
-
def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
-
val
uid: String
- Definition Classes
- SentenceEmbeddings → Identifiable
-
def
validate(schema: StructType): Boolean
takes a Dataset and checks to see if all the required annotation types are present.
takes a Dataset and checks to see if all the required annotation types are present.
- schema
to be validated
- returns
True if all the required types are present, else false
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
def
validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
- Definition Classes
- HasStorageRef
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
wrapColumnMetadata(col: Column): Column
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
def
wrapEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
- Attributes
- protected
- Definition Classes
- HasEmbeddingsProperties
-
def
wrapSentenceEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
- Attributes
- protected
- Definition Classes
- HasEmbeddingsProperties
-
def
write: MLWriter
- Definition Classes
- ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
Inherited from HasStorageRef
Inherited from HasEmbeddingsProperties
Inherited from HasProtectedParams
Inherited from HasSimpleAnnotate[SentenceEmbeddings]
Inherited from AnnotatorModel[SentenceEmbeddings]
Inherited from CanBeLazy
Inherited from RawAnnotator[SentenceEmbeddings]
Inherited from HasOutputAnnotationCol
Inherited from HasInputAnnotationCols
Inherited from HasOutputAnnotatorType
Inherited from ParamsAndFeaturesWritable
Inherited from HasFeatures
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from Model[SentenceEmbeddings]
Inherited from Transformer
Inherited from PipelineStage
Inherited from Logging
Inherited from Params
Inherited from Serializable
Inherited from Serializable
Inherited from Identifiable
Inherited from AnyRef
Inherited from Any
Parameters
A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.
Annotator types
Required input and expected output annotator types