class T5Transformer extends AnnotatorModel[T5Transformer] with HasBatchedAnnotate[T5Transformer] with ParamsAndFeaturesWritable with WriteTensorflowModel with WriteSentencePieceModel

T5: the Text-To-Text Transfer Transformer

T5 reconsiders all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. The text-to-text framework is able to use the same model, loss function, and hyper-parameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). T5 can even apply to regression tasks by training it to predict the string representation of a number instead of the number itself.

Pretrained models can be loaded with pretrained of the companion object:

val t5 = T5Transformer.pretrained()
  .setTask("summarize:")
  .setInputCols("document")
  .setOutputCol("summaries")

The default model is "t5_small", if no name is provided. For available pretrained models please see the Models Hub.

For extended examples of usage, see the Spark NLP Workshop and the T5TestSpec.

Sources:

Paper Abstract:

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new Colossal Clean Crawled Corpus, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.

Note:

This is a very computationally expensive module especially on larger sequence. The use of an accelerator such as GPU is recommended.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.seq2seq.T5Transformer
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("documents")

val t5 = T5Transformer.pretrained("t5_small")
  .setTask("summarize:")
  .setInputCols(Array("documents"))
  .setMaxOutputLength(200)
  .setOutputCol("summaries")

val pipeline = new Pipeline().setStages(Array(documentAssembler, t5))

val data = Seq(
  "Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a " +
    "downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness" +
    " of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this " +
    "paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework " +
    "that converts all text-based language problems into a text-to-text format. Our systematic study compares " +
    "pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens " +
    "of language understanding tasks. By combining the insights from our exploration with scale and our new " +
    "Colossal Clean Crawled Corpus, we achieve state-of-the-art results on many benchmarks covering " +
    "summarization, question answering, text classification, and more. To facilitate future work on transfer " +
    "learning for NLP, we release our data set, pre-trained models, and code."
).toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("summaries.result").show(false)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result                                                                                                                                                                                                        |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[transfer learning has emerged as a powerful technique in natural language processing (NLP) the effectiveness of transfer learning has given rise to a diversity of approaches, methodologies, and practice .]|
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Linear Supertypes
WriteSentencePieceModel, WriteTensorflowModel, HasBatchedAnnotate[T5Transformer], AnnotatorModel[T5Transformer], CanBeLazy, RawAnnotator[T5Transformer], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[T5Transformer], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. T5Transformer
  2. WriteSentencePieceModel
  3. WriteTensorflowModel
  4. HasBatchedAnnotate
  5. AnnotatorModel
  6. CanBeLazy
  7. RawAnnotator
  8. HasOutputAnnotationCol
  9. HasInputAnnotationCols
  10. HasOutputAnnotatorType
  11. ParamsAndFeaturesWritable
  12. HasFeatures
  13. DefaultParamsWritable
  14. MLWritable
  15. Model
  16. Transformer
  17. PipelineStage
  18. Logging
  19. Params
  20. Serializable
  21. Serializable
  22. Identifiable
  23. AnyRef
  24. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new T5Transformer()
  2. new T5Transformer(uid: String)

    uid

    required uid for storing annotator to disk

Type Members

  1. 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
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  12. 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 in batches that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every batch of input annotations. Not necessary one to one relationship IMPORTANT: !MUST! return sequences of equal lengths !! IMPORTANT: !MUST! return sentences that belong to the same original row !! (challenging)

    Definition Classes
    T5TransformerHasBatchedAnnotate
  13. def batchProcess(rows: Iterator[_]): Iterator[Row]
    Definition Classes
    HasBatchedAnnotate
  14. val batchSize: IntParam

    Size of every batch (Default depends on model).

    Size of every batch (Default depends on model).

    Definition Classes
    HasBatchedAnnotate
  15. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  16. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  17. final def clear(param: Param[_]): T5Transformer.this.type
    Definition Classes
    Params
  18. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  19. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  20. def copy(extra: ParamMap): T5Transformer

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  21. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  22. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  23. val doSample: BooleanParam

    Whether or not to use sampling, use greedy decoding otherwise (Default: false)

  24. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  25. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  26. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  27. def explainParams(): String
    Definition Classes
    Params
  28. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  29. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  30. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  31. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  32. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  33. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  34. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  35. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  36. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  37. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  38. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  39. def getBatchSize: Int

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  40. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  41. def getConfigProtoBytes: Option[Array[Byte]]

  42. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  43. def getDoSample: Boolean

  44. def getIgnoreTokenIds: Array[Int]

  45. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  46. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  47. def getMaxOutputLength: Int

  48. def getMinOutputLength: Int

  49. def getModelIfNotSet: TensorflowT5

  50. def getNoRepeatNgramSize: Int

  51. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  52. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  53. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  54. def getRandomSeed: Option[Long]

  55. def getRepetitionPenalty: Double

  56. def getTemperature: Double

  57. def getTopK: Int

  58. def getTopP: Double

  59. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  60. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  61. def hasParent: Boolean
    Definition Classes
    Model
  62. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  63. var ignoreTokenIds: IntArrayParam

    A list of token ids which are ignored in the decoder's output

  64. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  65. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : DOCUMENT

    Input annotator type : DOCUMENT

    Definition Classes
    T5TransformerHasInputAnnotationCols
  67. 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
  68. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  69. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  70. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  71. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  72. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  73. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  74. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  81. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. val maxOutputLength: IntParam

    Maximum length of the sequence to be generated (Default: 20)

  86. val minOutputLength: IntParam

    Minimum length of the sequence to be generated (Default: 0)

  87. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  88. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  89. val noRepeatNgramSize: IntParam

    If set to int > 0, all ngrams of that size can only occur once (Default: 0)

  90. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  91. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  92. def onWrite(path: String, spark: SparkSession): Unit
  93. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  94. val outputAnnotatorType: String

    Output annotator type : DOCUMENT

    Output annotator type : DOCUMENT

    Definition Classes
    T5TransformerHasOutputAnnotatorType
  95. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  96. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  97. var parent: Estimator[T5Transformer]
    Definition Classes
    Model
  98. var randomSeed: Option[Long]

    Optional Random seed for the model.

    Optional Random seed for the model. Needs to be of type Long.

  99. val repetitionPenalty: DoubleParam

    The parameter for repetition penalty (Default: 1.0).

    The parameter for repetition penalty (Default: 1.0). 1.0 means no penalty. See this paper for more details.

  100. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  101. def set[T](feature: StructFeature[T], value: T): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def set[T](feature: SetFeature[T], value: Set[T]): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  104. def set[T](feature: ArrayFeature[T], value: Array[T]): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  105. final def set(paramPair: ParamPair[_]): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  106. final def set(param: String, value: Any): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  107. final def set[T](param: Param[T], value: T): T5Transformer.this.type
    Definition Classes
    Params
  108. def setBatchSize(size: Int): T5Transformer.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  109. def setConfigProtoBytes(bytes: Array[Int]): T5Transformer.this.type

  110. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  111. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  112. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  113. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  114. final def setDefault(paramPairs: ParamPair[_]*): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  115. final def setDefault[T](param: Param[T], value: T): T5Transformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  116. def setDoSample(value: Boolean): T5Transformer.this.type

  117. def setIgnoreTokenIds(tokenIds: Array[Int]): T5Transformer.this.type

  118. final def setInputCols(value: String*): T5Transformer.this.type
    Definition Classes
    HasInputAnnotationCols
  119. def setInputCols(value: Array[String]): T5Transformer.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  120. def setLazyAnnotator(value: Boolean): T5Transformer.this.type
    Definition Classes
    CanBeLazy
  121. def setMaxOutputLength(value: Int): T5Transformer.this.type

  122. def setMinOutputLength(value: Int): T5Transformer.this.type

  123. def setModelIfNotSet(spark: SparkSession, tfWrapper: TensorflowWrapper, spp: SentencePieceWrapper): T5Transformer.this.type

  124. def setNoRepeatNgramSize(value: Int): T5Transformer.this.type

  125. final def setOutputCol(value: String): T5Transformer.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  126. def setParent(parent: Estimator[T5Transformer]): T5Transformer
    Definition Classes
    Model
  127. def setRandomSeed(value: Long): T5Transformer.this.type

  128. def setRepetitionPenalty(value: Double): T5Transformer.this.type

  129. def setTask(value: String): T5Transformer.this.type

  130. def setTemperature(value: Double): T5Transformer.this.type

  131. def setTopK(value: Int): T5Transformer.this.type

  132. def setTopP(value: Double): T5Transformer.this.type

  133. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  134. val task: Param[String]

    Set transformer task, e.g.

    Set transformer task, e.g. "summarize:" (Default: ""). The T5 task needs to be in the format "task:".

  135. val temperature: DoubleParam

    The value used to module the next token probabilities (Default: 1.0)

  136. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  137. val topK: IntParam

    The number of highest probability vocabulary tokens to keep for top-k-filtering (Default: 50)

  138. val topP: DoubleParam

    If set to float < 1.0, only the most probable tokens with probabilities that add up to topP or higher are kept for generation (Default: 1.0)

  139. 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
  140. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  141. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  142. 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
  143. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  144. val uid: String
    Definition Classes
    T5Transformer → Identifiable
  145. 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
  146. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  147. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  148. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  149. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  150. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  151. def writeSentencePieceModel(path: String, spark: SparkSession, spp: SentencePieceWrapper, suffix: String, filename: String): Unit
    Definition Classes
    WriteSentencePieceModel
  152. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  153. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  154. 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 WriteSentencePieceModel

Inherited from WriteTensorflowModel

Inherited from AnnotatorModel[T5Transformer]

Inherited from CanBeLazy

Inherited from RawAnnotator[T5Transformer]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[T5Transformer]

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.

Members

Parameter setters

Parameter getters