class TensorflowCamemBert extends Serializable
The CamemBERT model was proposed in CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook’s RoBERTa model released in 2019. It is a model trained on 138GB of French text.
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TensorflowCamemBert(tensorflow: TensorflowWrapper, spp: SentencePieceWrapper, configProtoBytes: Option[Array[Byte]] = None, signatures: Option[Map[String, String]] = None)
- tensorflow
Albert Model wrapper with TensorFlowWrapper
- spp
Albert SentencePiece model with SentencePieceWrapper
- configProtoBytes
Configuration for TensorFlow session
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- val _tfCamemBertSignatures: Map[String, String]
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- def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean): Seq[WordpieceEmbeddingsSentence]
- def prepareBatchInputs(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]
- val spp: SentencePieceWrapper
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- def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
- val tensorflow: TensorflowWrapper
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- def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
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