class TensorflowZeroShotNer extends RoBertaClassification
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Instance Constructors
- new TensorflowZeroShotNer(tensorflowWrapper: Option[TensorflowWrapper], onnxWrapper: Option[OnnxWrapper], sentenceStartTokenId: Int, sentenceEndTokenId: Int, sentencePadTokenId: Int, handleImpossibleAnswer: Boolean = false, configProtoBytes: Option[Array[Byte]] = None, tags: Map[String, Int], signatures: Option[Map[String, String]] = None, merges: Map[(String, String), Int], vocabulary: Map[String, Int])
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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val
_tfRoBertaSignatures: Map[String, String]
- Definition Classes
- RoBertaClassification
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final
def
asInstanceOf[T0]: T0
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def
calculateSigmoid(scores: Array[Float]): Array[Float]
- Definition Classes
- XXXForClassification
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def
calculateSoftmax(scores: Array[Float]): Array[Float]
- Definition Classes
- XXXForClassification
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def
clone(): AnyRef
- Attributes
- protected[lang]
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- Annotations
- @throws( ... ) @native()
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def
computeZeroShotLogitsWithONNX(batch: Seq[Array[Int]], maxSentenceLength: Int): Array[Float]
- Definition Classes
- RoBertaClassification
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def
computeZeroShotLogitsWithTF(batch: Seq[Array[Int]], maxSentenceLength: Int): Array[Float]
- Definition Classes
- RoBertaClassification
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def
constructAnnotationForSequenceClassifier(sentence: Sentence, label: String, meta: Array[(String, String)]): Annotation
- Definition Classes
- XXXForClassification
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def
constructMetaForSequenceClassifier(tags: Map[String, Int], scores: Array[Float]): Array[(String, String)]
- Definition Classes
- XXXForClassification
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val
detectedEngine: String
- Definition Classes
- RoBertaClassification
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def
encode(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]
- Definition Classes
- XXXForClassification
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def
encodeSequence(seq1: Seq[WordpieceTokenizedSentence], seq2: Seq[WordpieceTokenizedSentence], maxSequenceLength: Int): Seq[Array[Int]]
- Definition Classes
- TensorflowZeroShotNer → RoBertaClassification → XXXForClassification
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def
encodeSequenceWithPadding(seq1: Seq[WordpieceTokenizedSentence], seq2: Seq[WordpieceTokenizedSentence], maxSequenceLength: Int): Seq[Array[Int]]
- Definition Classes
- XXXForClassification
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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- protected[lang]
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- @throws( classOf[java.lang.Throwable] )
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def
findIndexedToken(tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tokenPiece: TokenPiece): Option[IndexedToken]
- Definition Classes
- RoBertaClassification → XXXForClassification
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final
def
getClass(): Class[_]
- Definition Classes
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- @native()
- val handleImpossibleAnswer: Boolean
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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val
logger: Logger
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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val
onnxWrapper: Option[OnnxWrapper]
- Definition Classes
- TensorflowZeroShotNer → RoBertaClassification
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def
predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int]): Seq[Annotation]
- Definition Classes
- XXXForClassification
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def
predictSequence(tokenizedSentences: Seq[TokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, coalesceSentences: Boolean, tags: Map[String, Int], activation: String): Seq[Annotation]
- Definition Classes
- XXXForClassification
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def
predictSequenceWithZeroShot(tokenizedSentences: Seq[TokenizedSentence], sentences: Seq[Sentence], candidateLabels: Array[String], entailmentId: Int, contradictionId: Int, batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, coalesceSentences: Boolean, tags: Map[String, Int], activation: String): Seq[Annotation]
- Definition Classes
- XXXForClassification
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def
predictSpan(documents: Seq[Annotation], maxSentenceLength: Int, caseSensitive: Boolean, mergeTokenStrategy: String, engine: String): Seq[Annotation]
- Definition Classes
- TensorflowZeroShotNer → RoBertaClassification → XXXForClassification
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def
predictSpanMultipleChoice(documents: Seq[Annotation], choicesDelimiter: String, maxSentenceLength: Int, caseSensitive: Boolean): Seq[Annotation]
- Definition Classes
- XXXForClassification
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def
scoresToLabelForSequenceClassifier(tags: Map[String, Int], scores: Array[Float]): String
- Definition Classes
- XXXForClassification
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val
sentenceEndTokenId: Int
- Definition Classes
- TensorflowZeroShotNer → RoBertaClassification → XXXForClassification
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val
sentencePadTokenId: Int
- Definition Classes
- TensorflowZeroShotNer → RoBertaClassification → XXXForClassification
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val
sentenceStartTokenId: Int
- Definition Classes
- TensorflowZeroShotNer → RoBertaClassification → XXXForClassification
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val
sigmoidThreshold: Float
- Attributes
- protected
- Definition Classes
- RoBertaClassification → XXXForClassification
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
- Definition Classes
- RoBertaClassification → XXXForClassification
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def
tagSequence(batch: Seq[Array[Int]], activation: String): Array[Array[Float]]
- Definition Classes
- RoBertaClassification → XXXForClassification
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def
tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]])
- Definition Classes
- TensorflowZeroShotNer → RoBertaClassification → XXXForClassification
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def
tagSpanMultipleChoice(batch: Seq[Array[Int]]): Array[Float]
- Definition Classes
- XXXForClassification
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def
tagZeroShotSequence(batch: Seq[Array[Int]], entailmentId: Int, contradictionId: Int, activation: String): Array[Array[Float]]
- Definition Classes
- RoBertaClassification → XXXForClassification
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val
tensorflowWrapper: Option[TensorflowWrapper]
- Definition Classes
- TensorflowZeroShotNer → RoBertaClassification
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def
toString(): String
- Definition Classes
- AnyRef → Any
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def
tokenizeDocument(docs: Seq[Annotation], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
- Definition Classes
- RoBertaClassification → XXXForClassification
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def
tokenizeSeqString(candidateLabels: Seq[String], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
- Definition Classes
- RoBertaClassification → XXXForClassification
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def
tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
- Definition Classes
- RoBertaClassification → XXXForClassification
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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- @throws( ... ) @native()
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def
wordAndSpanLevelAlignmentWithTokenizer(tokenLogits: Array[Array[Float]], tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tags: Map[String, Int]): Seq[Annotation]
- Definition Classes
- XXXForClassification