Class

com.johnsnowlabs.ml.tensorflow

TensorflowUSE

Related Doc: package tensorflow

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class TensorflowUSE extends Serializable

The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.

See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/UniversalSentenceEncoderTestSpec.scala for further reference on how to use this API.

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Instance Constructors

  1. new TensorflowUSE(tensorflow: TensorflowWrapper, configProtoBytes: Option[Array[Byte]] = None)

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    tensorflow

    USE Model wrapper with TensorFlow Wrapper

    configProtoBytes

    Configuration for TensorFlow session Sources : https://arxiv.org/abs/1803.11175 https://tfhub.dev/google/universal-sentence-encoder/2

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def calculateEmbeddings(sentences: Seq[Sentence]): Seq[Annotation]

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  6. def clone(): AnyRef

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  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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  13. final def ne(arg0: AnyRef): Boolean

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. final def synchronized[T0](arg0: ⇒ T0): T0

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  17. val tensorflow: TensorflowWrapper

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    USE Model wrapper with TensorFlow Wrapper

  18. def toString(): String

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  19. final def wait(): Unit

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  20. final def wait(arg0: Long, arg1: Int): Unit

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  21. final def wait(arg0: Long): Unit

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