class TensorflowElmo extends Serializable

Embeddings from a language model trained on the 1 Billion Word Benchmark.

Note that this is a very computationally expensive module compared to word embedding modules that only perform embedding lookups. The use of an accelerator is recommended.

word_emb: the character-based word representations with shape [batch_size, max_length, 512]. == word_emb

lstm_outputs1: the first LSTM hidden state with shape [batch_size, max_length, 1024]. === lstm_outputs1

lstm_outputs2: the second LSTM hidden state with shape [batch_size, max_length, 1024]. === lstm_outputs2

elmo: the weighted sum of the 3 layers, where the weights are trainable. This tensor has shape [batch_size, max_length, 1024] == elmo

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

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Instance Constructors

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

    tensorflow

    Elmo Model wrapper with TensorFlow Wrapper

    batchSize

    size of batch

    configProtoBytes

    Configuration for TensorFlow session Sources : https://tfhub.dev/google/elmo/3 https://arxiv.org/abs/1802.05365

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  5. def calculateEmbeddings(sentences: Seq[TokenizedSentence], poolingLayer: String): Seq[WordpieceEmbeddingsSentence]

    Calculate the embeddigns for a sequence of Tokens and create WordPieceEmbeddingsSentence objects from them

    Calculate the embeddigns for a sequence of Tokens and create WordPieceEmbeddingsSentence objects from them

    sentences

    A sequence of Tokenized Sentences for which embeddings will be calculated

    poolingLayer

    Define which output layer you want from the model word_emb, lstm_outputs1, lstm_outputs2, elmo. See https://tfhub.dev/google/elmo/3 for reference

    returns

    A Seq of WordpieceEmbeddingsSentence, one element for each input sentence

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  11. def getDimensions: (String) ⇒ Int

    word_emb: the character-based word representations with shape [batch_size, max_length, 512].

    word_emb: the character-based word representations with shape [batch_size, max_length, 512]. == 512

    lstm_outputs1: the first LSTM hidden state with shape [batch_size, max_length, 1024]. === 1024

    lstm_outputs2: the second LSTM hidden state with shape [batch_size, max_length, 1024]. === 1024

    elmo: the weighted sum of the 3 layers, where the weights are trainable. This tensor has shape [batch_size, max_length, 1024] == 1024

    returns

    The dimension of chosen layer

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  18. def tag(batch: Seq[TokenizedSentence], embeddingsKey: String, dimension: Int): Seq[Array[Array[Float]]]

    Tag a seq of TokenizedSentences, will get the embeddings according to key.

    Tag a seq of TokenizedSentences, will get the embeddings according to key.

    batch

    The Tokens for which we calculate embeddings

    embeddingsKey

    Specification of the output embedding for Elmo

    dimension

    Elmo's embeddings dimension: either 512 or 1024

    returns

    The Embeddings Vector. For each Seq Element we have a Sentence, and for each sentence we have an Array for each of its words. Each of its words gets a float array to represent its Embeddings

  19. val tensorflow: TensorflowWrapper
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