Packages

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

Token-level embeddings using BERT. BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture.

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

val embeddings = BertEmbeddings.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("bert_embeddings")

The default model is "small_bert_L2_768", if no name is provided.

For available pretrained models please see the Models Hub.

For extended examples of usage, see the Examples and the BertEmbeddingsTestSpec. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669.

Sources :

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/google-research/bert

Paper abstract

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

Example

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

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

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val embeddings = BertEmbeddings.pretrained("small_bert_L2_128", "en")
  .setInputCols("token", "document")
  .setOutputCol("bert_embeddings")

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

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

val data = Seq("This is a sentence.").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[-2.3497989177703857,0.480538547039032,-0.3238905668258667,-1.612930893898010...|
|[-2.1357314586639404,0.32984697818756104,-0.6032363176345825,-1.6791689395904...|
|[-1.8244884014129639,-0.27088963985443115,-1.059438943862915,-0.9817547798156...|
|[-1.1648050546646118,-0.4725411534309387,-0.5938255786895752,-1.5780693292617...|
|[-0.9125322699546814,0.4563939869403839,-0.3975459933280945,-1.81611204147338...|
+--------------------------------------------------------------------------------+
See also

BertSentenceEmbeddings for sentence-level embeddings

BertForTokenClassification For BertEmbeddings with a token classification layer on top

Annotators Main Page for a list of transformer based embeddings

Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. BertEmbeddings
  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

Parameters

A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.

  1. val batchSize: IntParam

    Size of every batch (Default depends on model).

    Size of every batch (Default depends on model).

    Definition Classes
    HasBatchedAnnotate
  2. 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
  3. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

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

  4. val dimension: ProtectedParam[Int]

    Number of embedding dimensions (Default depends on model)

    Number of embedding dimensions (Default depends on model)

    Definition Classes
    HasEmbeddingsProperties
  5. 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
  6. val maxSentenceLength: IntParam

    Max sentence length to process (Default: 128)

  7. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  8. val storageRef: Param[String]

    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  9. val vocabulary: MapFeature[String, Int]

    Vocabulary used to encode the words to ids with WordPieceEncoder

Annotator types

Required input and expected output annotator types

  1. val inputAnnotatorTypes: Array[String]

    Input Annotator Types: DOCUMENT, TOKEN

    Input Annotator Types: DOCUMENT, TOKEN

    Definition Classes
    BertEmbeddingsHasInputAnnotationCols
  2. val outputAnnotatorType: AnnotatorType

    Output Annotator Types: WORD_EMBEDDINGS

    Output Annotator Types: WORD_EMBEDDINGS

    Definition Classes
    BertEmbeddingsHasOutputAnnotatorType

Members

  1. implicit class ProtectedParam[T] extends Param[T]
    Definition Classes
    HasProtectedParams
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType
  1. 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
    BertEmbeddingsHasBatchedAnnotate
  2. def batchProcess(rows: Iterator[_]): Iterator[Row]
    Definition Classes
    HasBatchedAnnotate
  3. final def clear(param: Param[_]): BertEmbeddings.this.type
    Definition Classes
    Params
  4. def copy(extra: ParamMap): BertEmbeddings

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  5. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  6. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  7. def explainParams(): String
    Definition Classes
    Params
  8. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  9. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  10. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  11. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  12. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  13. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  14. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  15. def getModelIfNotSet: Bert
  16. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  17. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  18. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  19. def getStorageRef: String
    Definition Classes
    HasStorageRef
  20. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  21. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  22. def hasParent: Boolean
    Definition Classes
    Model
  23. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  24. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  25. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  26. def onWrite(path: String, spark: SparkSession): Unit
  27. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  28. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  29. var parent: Estimator[BertEmbeddings]
    Definition Classes
    Model
  30. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  31. def set[T](param: ProtectedParam[T], value: T): BertEmbeddings.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
  32. final def set[T](param: Param[T], value: T): BertEmbeddings.this.type
    Definition Classes
    Params
  33. final def setInputCols(value: String*): BertEmbeddings.this.type
    Definition Classes
    HasInputAnnotationCols
  34. def setInputCols(value: Array[String]): BertEmbeddings.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  35. def setLazyAnnotator(value: Boolean): BertEmbeddings.this.type
    Definition Classes
    CanBeLazy
  36. final def setOutputCol(value: String): BertEmbeddings.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  37. def setParent(parent: Estimator[BertEmbeddings]): BertEmbeddings
    Definition Classes
    Model
  38. def setStorageRef(value: String): BertEmbeddings.this.type
    Definition Classes
    HasStorageRef
  39. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  40. def tokenizeWithAlignment(tokens: Seq[TokenizedSentence]): Seq[WordpieceTokenizedSentence]
  41. 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
  42. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  43. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  44. 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
  45. val uid: String
    Definition Classes
    BertEmbeddings → Identifiable
  46. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  47. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  48. def writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOnnxModel
  49. def writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOnnxModel
  50. def writeOpenvinoModel(path: String, spark: SparkSession, openvinoWrapper: OpenvinoWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOpenvinoModel
  51. def writeOpenvinoModels(path: String, spark: SparkSession, ovWrappersWithNames: Seq[(OpenvinoWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOpenvinoModel
  52. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  53. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  54. 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

Parameter setters

  1. def sentenceEndTokenId: Int

  2. def sentenceStartTokenId: Int

  3. def setBatchSize(size: Int): BertEmbeddings.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  4. def setCaseSensitive(value: Boolean): BertEmbeddings.this.type

    Whether to lowercase tokens or not

    Whether to lowercase tokens or not

    Definition Classes
    BertEmbeddingsHasCaseSensitiveProperties
  5. def setConfigProtoBytes(bytes: Array[Int]): BertEmbeddings.this.type

  6. def setDimension(value: Int): BertEmbeddings.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
    BertEmbeddingsHasEmbeddingsProperties
  7. def setMaxSentenceLength(value: Int): BertEmbeddings.this.type

  8. def setModelIfNotSet(spark: SparkSession, tensorflowWrapper: Option[TensorflowWrapper], onnxWrapper: Option[OnnxWrapper], openvinoWrapper: Option[OpenvinoWrapper]): BertEmbeddings

  9. def setSignatures(value: Map[String, String]): BertEmbeddings.this.type

  10. def setVocabulary(value: Map[String, Int]): BertEmbeddings.this.type

Parameter getters

  1. def getBatchSize: Int

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  2. def getCaseSensitive: Boolean

    Definition Classes
    HasCaseSensitiveProperties
  3. def getConfigProtoBytes: Option[Array[Byte]]

  4. def getDimension: Int

    Definition Classes
    HasEmbeddingsProperties
  5. def getEngine: String

    Definition Classes
    HasEngine
  6. def getMaxSentenceLength: Int

  7. def getSignatures: Option[Map[String, String]]