class XlmRoBertaForZeroShotClassification extends AnnotatorModel[XlmRoBertaForZeroShotClassification] with HasBatchedAnnotate[XlmRoBertaForZeroShotClassification] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine with HasCandidateLabelsProperties
XlmRoBertaForZeroShotClassification using a ModelForSequenceClassification
trained on NLI
(natural language inference) tasks. Equivalent of XlmRoBertaForZeroShotClassification
models, but these models don't require a hardcoded number of potential classes, they can be
chosen at runtime. It usually means it's slower but it is much more flexible.
Note that the model will loop through all provided labels. So the more labels you have, the longer this process will take.
Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.
Pretrained models can be loaded with pretrained
of the companion object:
val sequenceClassifier = XlmRoBertaForZeroShotClassification .pretrained() .setInputCols("token", "document") .setOutputCol("label")
The default model is "xlm_roberta_large_zero_shot_classifier_xnli_anli"
, if no name is
provided.
For available pretrained models please see the Models Hub.
To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.
Example
import spark.implicits._ import com.johnsnowlabs.nlp.base._ import com.johnsnowlabs.nlp.annotator._ import org.apache.spark.ml.Pipeline val documentAssembler = new DocumentAssembler() .setInputCol("text") .setOutputCol("document") val tokenizer = new Tokenizer() .setInputCols("document") .setOutputCol("token") val sequenceClassifier = XlmRoBertaForZeroShotClassification .pretrained() .setInputCols("token", "document") .setOutputCol("label") .setCaseSensitive(true) val pipeline = new Pipeline().setStages(Array( documentAssembler, tokenizer, sequenceClassifier )) val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text") val result = pipeline.fit(data).transform(data) result.select("label.result").show(false) +------+ |result| +------+ |[pos] | |[neg] | +------+
- See also
XlmRoBertaForZeroShotClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers
- Grouped
- Alphabetic
- By Inheritance
- XlmRoBertaForZeroShotClassification
- HasCandidateLabelsProperties
- HasEngine
- HasClassifierActivationProperties
- HasCaseSensitiveProperties
- WriteSentencePieceModel
- WriteOnnxModel
- WriteTensorflowModel
- HasBatchedAnnotate
- AnnotatorModel
- CanBeLazy
- RawAnnotator
- HasOutputAnnotationCol
- HasInputAnnotationCols
- HasOutputAnnotatorType
- ParamsAndFeaturesWritable
- HasFeatures
- DefaultParamsWritable
- MLWritable
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
Type Members
-
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
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
$[T](param: Param[T]): T
- Attributes
- protected
- Definition Classes
- Params
-
def
$$[T](feature: StructFeature[T]): T
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
$$[K, V](feature: MapFeature[K, V]): Map[K, V]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
$$[T](feature: SetFeature[T]): Set[T]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
$$[T](feature: ArrayFeature[T]): Array[T]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
_transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
val
activation: Param[String]
Whether to enable caching DataFrames or RDDs during the training (Default depends on model).
Whether to enable caching DataFrames or RDDs during the training (Default depends on model).
- Definition Classes
- HasClassifierActivationProperties
-
def
afterAnnotate(dataset: DataFrame): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[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
- batchedAnnotations
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
- XlmRoBertaForZeroShotClassification → HasBatchedAnnotate
-
def
batchProcess(rows: Iterator[_]): Iterator[Row]
- Definition Classes
- HasBatchedAnnotate
-
val
batchSize: IntParam
Size of every batch (Default depends on model).
Size of every batch (Default depends on model).
- Definition Classes
- HasBatchedAnnotate
-
def
beforeAnnotate(dataset: Dataset[_]): Dataset[_]
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
val
candidateLabels: StringArrayParam
Candidate labels for classification, you can set candidateLabels dynamically during the runtime
Candidate labels for classification, you can set candidateLabels dynamically during the runtime
- Definition Classes
- HasCandidateLabelsProperties
-
val
caseSensitive: BooleanParam
Whether to ignore case in index lookups (Default depends on model)
Whether to ignore case in index lookups (Default depends on model)
- Definition Classes
- HasCaseSensitiveProperties
-
final
def
checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
clear(param: Param[_]): XlmRoBertaForZeroShotClassification.this.type
- Definition Classes
- Params
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
val
coalesceSentences: BooleanParam
Instead of 1 class per sentence (if inputCols is sentence) output 1 class per document by averaging probabilities in all sentences (Default:
false
).Instead of 1 class per sentence (if inputCols is sentence) output 1 class per document by averaging probabilities in all sentences (Default:
false
).Due to max sequence length limit in almost all transformer models such as XLM-RoBERTa (512 tokens), this parameter helps feeding all the sentences into the model and averaging all the probabilities for the entire document instead of probabilities per sentence.
-
val
configProtoBytes: IntArrayParam
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with
config_proto.SerializeToString()
-
val
contradictionIdParam: IntParam
- Definition Classes
- HasCandidateLabelsProperties
-
def
copy(extra: ParamMap): XlmRoBertaForZeroShotClassification
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
-
final
def
defaultCopy[T <: Params](extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
-
val
engine: Param[String]
This param is set internally once via loadSavedModel.
This param is set internally once via loadSavedModel. That's why there is no setter
- Definition Classes
- HasEngine
-
val
entailmentIdParam: IntParam
- Definition Classes
- HasCandidateLabelsProperties
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
explainParam(param: Param[_]): String
- Definition Classes
- Params
-
def
explainParams(): String
- Definition Classes
- Params
-
def
extraValidate(structType: StructType): Boolean
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
def
extraValidateMsg: String
Override for additional custom schema checks
Override for additional custom schema checks
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
final
def
extractParamMap(): ParamMap
- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
-
val
features: ArrayBuffer[Feature[_, _, _]]
- Definition Classes
- HasFeatures
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
get[T](feature: StructFeature[T]): Option[T]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
get[T](feature: SetFeature[T]): Option[Set[T]]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
get[T](feature: ArrayFeature[T]): Option[Array[T]]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getActivation: String
- Definition Classes
- HasClassifierActivationProperties
-
def
getBatchSize: Int
Size of every batch.
Size of every batch.
- Definition Classes
- HasBatchedAnnotate
-
def
getCandidateLabels: Array[String]
- Definition Classes
- HasCandidateLabelsProperties
-
def
getCaseSensitive: Boolean
- Definition Classes
- HasCaseSensitiveProperties
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
getClasses: Array[String]
Returns labels used to train this model
- def getCoalesceSentences: Boolean
- def getConfigProtoBytes: Option[Array[Byte]]
-
final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getEngine: String
- Definition Classes
- HasEngine
-
def
getInputCols: Array[String]
- returns
input annotations columns currently used
- Definition Classes
- HasInputAnnotationCols
-
def
getLazyAnnotator: Boolean
- Definition Classes
- CanBeLazy
- def getMaxSentenceLength: Int
- def getModelIfNotSet: XlmRoBertaClassification
-
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 getSignatures: Option[Map[String, String]]
-
final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
- Definition Classes
- Params
-
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[String]
Input Annotator Types: DOCUMENT, TOKEN
Input Annotator Types: DOCUMENT, TOKEN
- Definition Classes
- XlmRoBertaForZeroShotClassification → 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
- Params
-
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
labels: MapFeature[String, Int]
Labels used to decode predicted IDs back to string tags
-
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
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logName: String
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- 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
-
val
maxSentenceLength: IntParam
Max sentence length to process (Default:
128
) -
def
msgHelper(schema: StructType): String
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
val
multilabel: BooleanParam
Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0).
Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class
- Definition Classes
- HasClassifierActivationProperties
-
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
- Definition Classes
- XlmRoBertaForZeroShotClassification → ParamsAndFeaturesWritable
-
val
optionalInputAnnotatorTypes: Array[String]
- Definition Classes
- HasInputAnnotationCols
-
val
outputAnnotatorType: AnnotatorType
Output Annotator Types: CATEGORY
Output Annotator Types: CATEGORY
- Definition Classes
- XlmRoBertaForZeroShotClassification → HasOutputAnnotatorType
-
final
val
outputCol: Param[String]
- Attributes
- protected
- Definition Classes
- HasOutputAnnotationCol
-
lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
var
parent: Estimator[XlmRoBertaForZeroShotClassification]
- Definition Classes
- Model
-
def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
def
set[T](feature: StructFeature[T], value: T): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[K, V](feature: MapFeature[K, V], value: Map[K, V]): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: SetFeature[T], value: Set[T]): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: ArrayFeature[T], value: Array[T]): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
set(paramPair: ParamPair[_]): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): XlmRoBertaForZeroShotClassification.this.type
- Definition Classes
- Params
-
def
setActivation(value: String): XlmRoBertaForZeroShotClassification.this.type
- Definition Classes
- HasClassifierActivationProperties
-
def
setBatchSize(size: Int): XlmRoBertaForZeroShotClassification.this.type
Size of every batch.
Size of every batch.
- Definition Classes
- HasBatchedAnnotate
-
def
setCandidateLabels(value: Array[String]): XlmRoBertaForZeroShotClassification.this.type
- Definition Classes
- HasCandidateLabelsProperties
-
def
setCaseSensitive(value: Boolean): XlmRoBertaForZeroShotClassification.this.type
Whether to lowercase tokens or not (Default:
true
).Whether to lowercase tokens or not (Default:
true
).- Definition Classes
- XlmRoBertaForZeroShotClassification → HasCaseSensitiveProperties
- def setCoalesceSentences(value: Boolean): XlmRoBertaForZeroShotClassification.this.type
- def setConfigProtoBytes(bytes: Array[Int]): XlmRoBertaForZeroShotClassification.this.type
-
def
setDefault[T](feature: StructFeature[T], value: () ⇒ T): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
setDefault(paramPairs: ParamPair[_]*): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): XlmRoBertaForZeroShotClassification.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
-
final
def
setInputCols(value: String*): XlmRoBertaForZeroShotClassification.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setInputCols(value: Array[String]): XlmRoBertaForZeroShotClassification.this.type
Overrides required annotators column if different than default
Overrides required annotators column if different than default
- Definition Classes
- HasInputAnnotationCols
- def setLabels(value: Map[String, Int]): XlmRoBertaForZeroShotClassification.this.type
-
def
setLazyAnnotator(value: Boolean): XlmRoBertaForZeroShotClassification.this.type
- Definition Classes
- CanBeLazy
- def setMaxSentenceLength(value: Int): XlmRoBertaForZeroShotClassification.this.type
- def setModelIfNotSet(spark: SparkSession, tensorflowWrapper: Option[TensorflowWrapper], onnxWrapper: Option[OnnxWrapper], spp: SentencePieceWrapper): XlmRoBertaForZeroShotClassification
-
def
setMultilabel(value: Boolean): XlmRoBertaForZeroShotClassification.this.type
Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0).
Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class
- Definition Classes
- HasClassifierActivationProperties
-
final
def
setOutputCol(value: String): XlmRoBertaForZeroShotClassification.this.type
Overrides annotation column name when transforming
Overrides annotation column name when transforming
- Definition Classes
- HasOutputAnnotationCol
-
def
setParent(parent: Estimator[XlmRoBertaForZeroShotClassification]): XlmRoBertaForZeroShotClassification
- Definition Classes
- Model
- def setSignatures(value: Map[String, String]): XlmRoBertaForZeroShotClassification.this.type
-
def
setThreshold(threshold: Float): XlmRoBertaForZeroShotClassification.this.type
Choose the threshold to determine which logits are considered to be positive or negative.
Choose the threshold to determine which logits are considered to be positive or negative. (Default:
0.5f
). The value should be between 0.0 and 1.0. Changing the threshold value will affect the resulting labels and can be used to adjust the balance between precision and recall in the classification process.- Definition Classes
- HasClassifierActivationProperties
-
val
signatures: MapFeature[String, String]
It contains TF model signatures for the laded saved model
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
val
threshold: FloatParam
Choose the threshold to determine which logits are considered to be positive or negative.
Choose the threshold to determine which logits are considered to be positive or negative. (Default:
0.5f
). The value should be between 0.0 and 1.0. Changing the threshold value will affect the resulting labels and can be used to adjust the balance between precision and recall in the classification process.- Definition Classes
- HasClassifierActivationProperties
-
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
- XlmRoBertaForZeroShotClassification → 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
-
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
write: MLWriter
- Definition Classes
- ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
-
def
writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
- Definition Classes
- WriteOnnxModel
-
def
writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
- Definition Classes
- WriteOnnxModel
-
def
writeSentencePieceModel(path: String, spark: SparkSession, spp: SentencePieceWrapper, suffix: String, filename: String): Unit
- Definition Classes
- WriteSentencePieceModel
-
def
writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
- Definition Classes
- WriteTensorflowModel
-
def
writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
- Definition Classes
- WriteTensorflowModel
-
def
writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None, savedSignatures: Option[Map[String, String]] = None): Unit
- Definition Classes
- WriteTensorflowModel
Inherited from HasCandidateLabelsProperties
Inherited from HasEngine
Inherited from HasClassifierActivationProperties
Inherited from HasCaseSensitiveProperties
Inherited from WriteSentencePieceModel
Inherited from WriteOnnxModel
Inherited from WriteTensorflowModel
Inherited from HasBatchedAnnotate[XlmRoBertaForZeroShotClassification]
Inherited from AnnotatorModel[XlmRoBertaForZeroShotClassification]
Inherited from CanBeLazy
Inherited from RawAnnotator[XlmRoBertaForZeroShotClassification]
Inherited from HasOutputAnnotationCol
Inherited from HasInputAnnotationCols
Inherited from HasOutputAnnotatorType
Inherited from ParamsAndFeaturesWritable
Inherited from HasFeatures
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from Model[XlmRoBertaForZeroShotClassification]
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