c

com.johnsnowlabs.ml.tensorflow

TensorflowRoBertaClassification

class TensorflowRoBertaClassification extends Serializable with TensorflowForClassification

Linear Supertypes
TensorflowForClassification, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. TensorflowRoBertaClassification
  2. TensorflowForClassification
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new TensorflowRoBertaClassification(tensorflowWrapper: TensorflowWrapper, sentenceStartTokenId: Int, sentenceEndTokenId: Int, sentencePadTokenId: Int, configProtoBytes: Option[Array[Byte]] = None, tags: Map[String, Int], signatures: Option[Map[String, String]] = None, merges: Map[(String, String), Int], vocabulary: Map[String, Int])

    tensorflowWrapper

    Bert Model wrapper with TensorFlow Wrapper

    sentenceStartTokenId

    Id of sentence start Token

    sentenceEndTokenId

    Id of sentence end Token.

    configProtoBytes

    Configuration for TensorFlow session

    tags

    labels which model was trained with in order

    signatures

    TF v2 signatures in Spark NLP

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val _tfRoBertaSignatures: Map[String, String]
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def calculateSoftmax(scores: Array[Float]): Array[Float]
    Definition Classes
    TensorflowForClassification
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. def encode(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]

    Encode the input sequence to indexes IDs adding padding where necessary

    Encode the input sequence to indexes IDs adding padding where necessary

    Definition Classes
    TensorflowForClassification
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. def findIndexedToken(tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tokenPiece: TokenPiece): Option[IndexedToken]
  13. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  14. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  15. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  16. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  18. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  19. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int]): Seq[Annotation]
    Definition Classes
    TensorflowForClassification
  20. val sentenceEndTokenId: Int
  21. val sentencePadTokenId: Int
  22. val sentenceStartTokenId: Int
  23. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  24. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
  25. val tensorflowWrapper: TensorflowWrapper
  26. def toString(): String
    Definition Classes
    AnyRef → Any
  27. def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
  28. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  31. def wordAndSpanLevelAlignmentWithTokenizer(tokenLogits: Array[Array[Float]], tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tags: Map[String, Int]): Seq[Annotation]

    Word-level and span-level alignment with Tokenizer https://github.com/google-research/bert#tokenization

    Word-level and span-level alignment with Tokenizer https://github.com/google-research/bert#tokenization

    ### Input orig_tokens = ["John", "Johanson", "'s", "house"] labels = ["NNP", "NNP", "POS", "NN"]

    # bert_tokens == ["[CLS]", "john", "johan", "##son", "'", "s", "house", "[SEP]"] # orig_to_tok_map == [1, 2, 4, 6]

    Definition Classes
    TensorflowForClassification

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped