Packages

class StarCoderTransformer extends AnnotatorModel[StarCoderTransformer] with HasBatchedAnnotate[StarCoderTransformer] with ParamsAndFeaturesWritable with WriteOnnxModel with WriteOpenvinoModel with HasGeneratorProperties with HasEngine

StarCoder2: The Versatile Code Companion.

StarCoder2 is a Transformer model designed specifically for code generation and understanding. With 13 billion parameters, it builds upon the advancements of its predecessors and is trained on a diverse dataset that includes multiple programming languages. This extensive training allows StarCoder2 to support a wide array of coding tasks, from code completion to generation.

StarCoder2 was developed to assist developers in writing and understanding code more efficiently, making it a valuable tool for various software development and data science tasks.

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

val starcoder2 = StarCoder2Transformer.pretrained()
  .setInputCols("document")
  .setOutputCol("generation")

The default model is "StarCoder2-3B", if no name is provided. For available pretrained models please see the Models Hub.

For extended examples of usage, see StarCoder2TestSpec.

References:

Paper Abstract:

The BigCode project,1 an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership with Software Heritage (SWH),2 we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4× larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks.

We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

Note:

This is a computationally intensive module, especially for larger code sequences. 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.StarCoder2Transformer
import org.apache.spark.ml.Pipeline

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

val starcoder2 = StarCoder2Transformer.pretrained("starcoder2")
  .setInputCols(Array("documents"))
  .setMinOutputLength(10)
  .setMaxOutputLength(50)
  .setDoSample(false)
  .setTopK(50)
  .setNoRepeatNgramSize(3)
  .setOutputCol("generation")

val pipeline = new Pipeline().setStages(Array(documentAssembler, starcoder2))

val data = Seq(
  "def add(a, b):"
).toDF("text")
val result = pipeline.fit(data).transform(data)

results.select("generation.result").show(truncate = false)
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result                                                                                                                                                                                              |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[def add(a, b): return a + b]                                                                                                                                                                       |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Linear Supertypes
HasEngine, HasGeneratorProperties, WriteOpenvinoModel, WriteOnnxModel, HasBatchedAnnotate[StarCoderTransformer], AnnotatorModel[StarCoderTransformer], CanBeLazy, RawAnnotator[StarCoderTransformer], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[StarCoderTransformer], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. StarCoderTransformer
  2. HasEngine
  3. HasGeneratorProperties
  4. WriteOpenvinoModel
  5. WriteOnnxModel
  6. HasBatchedAnnotate
  7. AnnotatorModel
  8. CanBeLazy
  9. RawAnnotator
  10. HasOutputAnnotationCol
  11. HasInputAnnotationCols
  12. HasOutputAnnotatorType
  13. ParamsAndFeaturesWritable
  14. HasFeatures
  15. DefaultParamsWritable
  16. MLWritable
  17. Model
  18. Transformer
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Serializable
  24. Identifiable
  25. AnyRef
  26. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new StarCoderTransformer()
  2. new StarCoderTransformer(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 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
    StarCoderTransformer → HasBatchedAnnotate
  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. val beamSize: IntParam

    Beam size for the beam search algorithm (Default: 4)

    Beam size for the beam search algorithm (Default: 4)

    Definition Classes
    HasGeneratorProperties
  16. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  17. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  18. final def clear(param: Param[_]): StarCoderTransformer.this.type
    Definition Classes
    Params
  19. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  20. def copy(extra: ParamMap): StarCoderTransformer

    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)

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

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  42. def getBeamSize: Int

    Definition Classes
    HasGeneratorProperties
  43. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  44. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  45. def getDoSample: Boolean

    Definition Classes
    HasGeneratorProperties
  46. def getEngine: String

    Definition Classes
    HasEngine
  47. def getGenerationConfig: GenerationConfig
  48. def getIgnoreTokenIds: Array[Int]

  49. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  50. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  51. def getMaxOutputLength: Int

    Definition Classes
    HasGeneratorProperties
  52. def getMinOutputLength: Int

    Definition Classes
    HasGeneratorProperties
  53. def getModelIfNotSet: StarCoder

  54. def getNReturnSequences: Int

    Definition Classes
    HasGeneratorProperties
  55. def getNoRepeatNgramSize: Int

    Definition Classes
    HasGeneratorProperties
  56. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  57. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

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

    Definition Classes
    HasGeneratorProperties
  60. def getRepetitionPenalty: Double

    Definition Classes
    HasGeneratorProperties
  61. def getStopTokenIds: Array[Int]

    Definition Classes
    HasGeneratorProperties
  62. def getTask: Option[String]

    Definition Classes
    HasGeneratorProperties
  63. def getTemperature: Double

    Definition Classes
    HasGeneratorProperties
  64. def getTopK: Int

    Definition Classes
    HasGeneratorProperties
  65. def getTopP: Double

    Definition Classes
    HasGeneratorProperties
  66. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  67. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  68. def hasParent: Boolean
    Definition Classes
    Model
  69. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  70. var ignoreTokenIds: IntArrayParam

    A list of token ids which are ignored in the decoder's output (Default: Array())

  71. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  72. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : DOCUMENT

    Input annotator type : DOCUMENT

    Definition Classes
    StarCoderTransformer → HasInputAnnotationCols
  74. 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
  75. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  76. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  77. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  78. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  79. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  80. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  81. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  88. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  91. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. val maxInputLength: IntParam

    max length of the input sequence (Default: 0)

    max length of the input sequence (Default: 0)

    Definition Classes
    HasGeneratorProperties
  93. val maxOutputLength: IntParam

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

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

    Definition Classes
    HasGeneratorProperties
  94. val merges: MapFeature[(String, String), Int]

    Holding merges.txt coming from RoBERTa model

  95. val minOutputLength: IntParam

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

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

    Definition Classes
    HasGeneratorProperties
  96. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  97. val nReturnSequences: IntParam

    The number of sequences to return from the beam search.

    The number of sequences to return from the beam search.

    Definition Classes
    HasGeneratorProperties
  98. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  99. val noRepeatNgramSize: IntParam

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

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

    Definition Classes
    HasGeneratorProperties
  100. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  101. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  102. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    StarCoderTransformer → ParamsAndFeaturesWritable
  103. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  104. val outputAnnotatorType: String

    Output annotator type : DOCUMENT

    Output annotator type : DOCUMENT

    Definition Classes
    StarCoderTransformer → HasOutputAnnotatorType
  105. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  106. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  107. var parent: Estimator[StarCoderTransformer]
    Definition Classes
    Model
  108. val randomSeed: Option[Long]

    Optional Random seed for the model.

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

    Definition Classes
    HasGeneratorProperties
  109. 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.

    Definition Classes
    HasGeneratorProperties
  110. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  111. def set[T](feature: StructFeature[T], value: T): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  112. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  113. def set[T](feature: SetFeature[T], value: Set[T]): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  114. def set[T](feature: ArrayFeature[T], value: Array[T]): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  115. final def set(paramPair: ParamPair[_]): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  116. final def set(param: String, value: Any): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  117. final def set[T](param: Param[T], value: T): StarCoderTransformer.this.type
    Definition Classes
    Params
  118. def setBatchSize(size: Int): StarCoderTransformer.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  119. def setBeamSize(beamNum: Int): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  120. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  121. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  122. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  123. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  124. final def setDefault(paramPairs: ParamPair[_]*): StarCoderTransformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  125. final def setDefault[T](param: Param[T], value: T): StarCoderTransformer.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  126. def setDoSample(value: Boolean): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  127. def setGenerationConfig(value: GenerationConfig): StarCoderTransformer.this.type
  128. def setIgnoreTokenIds(tokenIds: Array[Int]): StarCoderTransformer.this.type

  129. final def setInputCols(value: String*): StarCoderTransformer.this.type
    Definition Classes
    HasInputAnnotationCols
  130. def setInputCols(value: Array[String]): StarCoderTransformer.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  131. def setLazyAnnotator(value: Boolean): StarCoderTransformer.this.type
    Definition Classes
    CanBeLazy
  132. def setMaxInputLength(value: Int): StarCoderTransformer.this.type
    Definition Classes
    HasGeneratorProperties
  133. def setMaxOutputLength(value: Int): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  134. def setMerges(value: Map[(String, String), Int]): StarCoderTransformer.this.type

  135. def setMinOutputLength(value: Int): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  136. def setModelIfNotSet(spark: SparkSession, onnxWrappers: Option[DecoderWrappers], openvinoWrapper: Option[OpenvinoWrapper]): StarCoderTransformer.this.type

  137. def setNReturnSequences(beamNum: Int): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  138. def setNoRepeatNgramSize(value: Int): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  139. final def setOutputCol(value: String): StarCoderTransformer.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  140. def setParent(parent: Estimator[StarCoderTransformer]): StarCoderTransformer
    Definition Classes
    Model
  141. def setRandomSeed(value: Int): StarCoderTransformer.this.type

  142. def setRandomSeed(value: Long): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  143. def setRepetitionPenalty(value: Double): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  144. def setStopTokenIds(value: Array[Int]): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  145. def setTask(value: String): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  146. def setTemperature(value: Double): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  147. def setTopK(value: Int): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  148. def setTopP(value: Double): StarCoderTransformer.this.type

    Definition Classes
    HasGeneratorProperties
  149. def setVocabulary(value: Map[String, Int]): StarCoderTransformer.this.type

  150. val stopTokenIds: IntArrayParam

    Stop tokens to terminate the generation

    Stop tokens to terminate the generation

    Definition Classes
    HasGeneratorProperties
  151. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  152. val task: Param[String]

    Set transformer task, e.g.

    Set transformer task, e.g. "summarize:" (Default: "").

    Definition Classes
    HasGeneratorProperties
  153. val temperature: DoubleParam

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

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

    Definition Classes
    HasGeneratorProperties
  154. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  155. val topK: IntParam

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

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

    Definition Classes
    HasGeneratorProperties
  156. 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)

    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)

    Definition Classes
    HasGeneratorProperties
  157. 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
  158. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  159. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  160. 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
  161. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  162. val uid: String
    Definition Classes
    StarCoderTransformer → Identifiable
  163. 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
  164. val vocabulary: MapFeature[String, Int]

    Vocabulary used to encode the words to ids with bpeTokenizer.encode

  165. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  166. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  167. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  168. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  169. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  170. def writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOnnxModel
  171. def writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOnnxModel
  172. def writeOpenvinoModel(path: String, spark: SparkSession, openvinoWrapper: OpenvinoWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOpenvinoModel
  173. def writeOpenvinoModels(path: String, spark: SparkSession, ovWrappersWithNames: Seq[(OpenvinoWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOpenvinoModel

Inherited from HasEngine

Inherited from HasGeneratorProperties

Inherited from WriteOpenvinoModel

Inherited from WriteOnnxModel

Inherited from HasBatchedAnnotate[StarCoderTransformer]

Inherited from AnnotatorModel[StarCoderTransformer]

Inherited from CanBeLazy

Inherited from RawAnnotator[StarCoderTransformer]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[StarCoderTransformer]

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