Packages

class NomicEmbeddings extends AnnotatorModel[NomicEmbeddings] with HasBatchedAnnotate[NomicEmbeddings] with WriteTensorflowModel with WriteOnnxModel with WriteOpenvinoModel with HasEmbeddingsProperties with HasStorageRef with HasCaseSensitiveProperties with HasEngine

Sentence embeddings using NomicEmbeddings.

nomic-embed-text-v1 is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks.

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

val embeddings = NomicEmbeddings.pretrained()
  .setInputCols("document")
  .setOutputCol("nomic_embeddings")

The default model is "nomic_small", if no name is provided.

For available pretrained models please see the Models Hub.

For extended examples of usage, see NomicEmbeddingsTestSpec.

Sources :

Nomic Embed: Training a Reproducible Long Context Text Embedder

NomicEmbeddings Github Repository

Paper abstract

This technical report describes the training of nomic-embed-text-v1, the first fully reproducible, open-source, open-weights, opendata, 8192 context length English text embedding model that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short and long-context tasks. We release the training code and model weights under an Apache 2 license. In contrast with other open-source models, we release a training data loader with 235 million curated text pairs that allows for the full replication of nomic-embedtext-v1. You can find code and data to replicate the model at https://github.com/nomicai/contrastors.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.embeddings.NomicEmbeddings
import com.johnsnowlabs.nlp.EmbeddingsFinisher
import org.apache.spark.ml.Pipeline

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

val embeddings = NomicEmbeddings.pretrained("nomic_small", "en")
  .setInputCols("document")
  .setOutputCol("nomic_embeddings")

val embeddingsFinisher = new EmbeddingsFinisher()
  .setInputCols("nomic_embeddings")
  .setOutputCols("finished_embeddings")
  .setOutputAsVector(true)

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

val data = Seq("query: how much protein should a female eat",
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day." +
But, as you can see from this chart, you'll need to increase that if you're expecting or training for a" +
marathon. Check out the chart below to see how much protein you should be eating each day."

).toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(finished_embeddings) as result").show(1, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[[8.0190285E-4, -0.005974853, -0.072875895, 0.007944068, 0.026059335, -0.0080...|
[[0.050514214, 0.010061974, -0.04340176, -0.020937217, 0.05170225, 0.01157857...|
+--------------------------------------------------------------------------------+
See also

Annotators Main Page for a list of transformer based embeddings

Linear Supertypes
HasEngine, HasCaseSensitiveProperties, HasStorageRef, HasEmbeddingsProperties, HasProtectedParams, WriteOpenvinoModel, WriteOnnxModel, WriteTensorflowModel, HasBatchedAnnotate[NomicEmbeddings], AnnotatorModel[NomicEmbeddings], CanBeLazy, RawAnnotator[NomicEmbeddings], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[NomicEmbeddings], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. NomicEmbeddings
  2. HasEngine
  3. HasCaseSensitiveProperties
  4. HasStorageRef
  5. HasEmbeddingsProperties
  6. HasProtectedParams
  7. WriteOpenvinoModel
  8. WriteOnnxModel
  9. WriteTensorflowModel
  10. HasBatchedAnnotate
  11. AnnotatorModel
  12. CanBeLazy
  13. RawAnnotator
  14. HasOutputAnnotationCol
  15. HasInputAnnotationCols
  16. HasOutputAnnotatorType
  17. ParamsAndFeaturesWritable
  18. HasFeatures
  19. DefaultParamsWritable
  20. MLWritable
  21. Model
  22. Transformer
  23. PipelineStage
  24. Logging
  25. Params
  26. Serializable
  27. Serializable
  28. Identifiable
  29. AnyRef
  30. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

    uid

    required uid for storing annotator to disk

Type Members

  1. implicit class ProtectedParam[T] extends Param[T]
    Definition Classes
    HasProtectedParams
  2. 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
  3. 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
    NomicEmbeddings → 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
    NomicEmbeddings → 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. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  16. 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
  17. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  18. final def clear(param: Param[_]): NomicEmbeddings.this.type
    Definition Classes
    Params
  19. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  20. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

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

  21. def copy(extra: ParamMap): NomicEmbeddings

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  22. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  23. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  24. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. val dimension: ProtectedParam[Int]

    Number of embedding dimensions (Default depends on model)

    Number of embedding dimensions (Default depends on model)

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  43. def getCaseSensitive: Boolean

    Definition Classes
    HasCaseSensitiveProperties
  44. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  45. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  46. def getDimension: Int

    Definition Classes
    HasEmbeddingsProperties
  47. def getEngine: String

    Definition Classes
    HasEngine
  48. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  49. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  50. def getMaxSentenceLength: Int

  51. def getModelIfNotSet: Nomic

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  54. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  55. def getStorageRef: String
    Definition Classes
    HasStorageRef
  56. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  57. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  58. def hasParent: Boolean
    Definition Classes
    Model
  59. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  60. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  61. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. val inputAnnotatorTypes: Array[String]

    Annotator reference id.

    Annotator reference id. Used to identify elements in metadata or to refer to this annotator type

    Definition Classes
    NomicEmbeddings → HasInputAnnotationCols
  63. 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
  64. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  65. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  66. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  67. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  68. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  69. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  70. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  77. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. val maxSentenceLength: IntParam

    Max sentence length to process (Default: 128)

  82. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  83. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  84. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  85. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  86. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    NomicEmbeddings → ParamsAndFeaturesWritable
  87. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  88. val outputAnnotatorType: AnnotatorType
    Definition Classes
    NomicEmbeddings → HasOutputAnnotatorType
  89. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  90. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  91. var parent: Estimator[NomicEmbeddings]
    Definition Classes
    Model
  92. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  93. def sentenceEndTokenId: Int

  94. def sentenceStartTokenId: Int
  95. def set[T](param: ProtectedParam[T], value: T): NomicEmbeddings.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
  96. def set[T](feature: StructFeature[T], value: T): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  97. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  98. def set[T](feature: SetFeature[T], value: Set[T]): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  99. def set[T](feature: ArrayFeature[T], value: Array[T]): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  100. final def set(paramPair: ParamPair[_]): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    Params
  101. final def set(param: String, value: Any): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    Params
  102. final def set[T](param: Param[T], value: T): NomicEmbeddings.this.type
    Definition Classes
    Params
  103. def setBatchSize(size: Int): NomicEmbeddings.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  104. def setCaseSensitive(value: Boolean): NomicEmbeddings.this.type

    Whether to lowercase tokens or not

    Whether to lowercase tokens or not

    Definition Classes
    NomicEmbeddings → HasCaseSensitiveProperties
  105. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  106. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  107. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  108. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  109. final def setDefault(paramPairs: ParamPair[_]*): NomicEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    Params
  110. final def setDefault[T](param: Param[T], value: T): NomicEmbeddings.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  111. def setDimension(value: Int): NomicEmbeddings.this.type

    Set Embeddings dimensions for the BERT model Only possible to set this when the first time is saved dimension is not changeable, it comes from BERT config file

    Set Embeddings dimensions for the BERT model Only possible to set this when the first time is saved dimension is not changeable, it comes from BERT config file

    Definition Classes
    NomicEmbeddings → HasEmbeddingsProperties
  112. final def setInputCols(value: String*): NomicEmbeddings.this.type
    Definition Classes
    HasInputAnnotationCols
  113. def setInputCols(value: Array[String]): NomicEmbeddings.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  114. def setLazyAnnotator(value: Boolean): NomicEmbeddings.this.type
    Definition Classes
    CanBeLazy
  115. def setMaxSentenceLength(value: Int): NomicEmbeddings.this.type

  116. def setModelIfNotSet(spark: SparkSession, onnxWrapper: Option[OnnxWrapper], openvinoWrapper: Option[OpenvinoWrapper]): NomicEmbeddings

  117. final def setOutputCol(value: String): NomicEmbeddings.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  118. def setParent(parent: Estimator[NomicEmbeddings]): NomicEmbeddings
    Definition Classes
    Model
  119. def setStorageRef(value: String): NomicEmbeddings.this.type
    Definition Classes
    HasStorageRef
  120. def setVocabulary(value: Map[String, Int]): NomicEmbeddings.this.type

  121. val storageRef: Param[String]

    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  122. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  123. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  124. def tokenize(sentences: Seq[Annotation]): Seq[WordpieceTokenizedSentence]
  125. 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
  126. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  127. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  128. 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
  129. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  130. val uid: String
    Definition Classes
    NomicEmbeddings → Identifiable
  131. 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
  132. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  133. val vocabulary: MapFeature[String, Int]

    Vocabulary used to encode the words to ids with WordPieceEncoder

  134. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  135. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  136. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  137. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  138. def wrapEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  139. def wrapSentenceEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  140. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  141. def writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOnnxModel
  142. def writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOnnxModel
  143. def writeOpenvinoModel(path: String, spark: SparkSession, openvinoWrapper: OpenvinoWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOpenvinoModel
  144. def writeOpenvinoModels(path: String, spark: SparkSession, ovWrappersWithNames: Seq[(OpenvinoWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOpenvinoModel
  145. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  146. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  147. 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 HasEngine

Inherited from HasCaseSensitiveProperties

Inherited from HasStorageRef

Inherited from HasEmbeddingsProperties

Inherited from HasProtectedParams

Inherited from WriteOpenvinoModel

Inherited from WriteOnnxModel

Inherited from WriteTensorflowModel

Inherited from HasBatchedAnnotate[NomicEmbeddings]

Inherited from AnnotatorModel[NomicEmbeddings]

Inherited from CanBeLazy

Inherited from RawAnnotator[NomicEmbeddings]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[NomicEmbeddings]

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