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Doc2Vec Learner

KNIME Labs Deep Learning DL4J Word Embeddings
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This node trains a Doc2Vec (Paragraph Vectors) model on labelled documents. The model will learn sequence representations (vector for each label) as well as word representations (vector for each word), that can be extracted using the Vocabulary Extractor Node. For more information on Word Vectors in general see: http://deeplearning4j.org/word2vec

Node details

Input ports
  1. Type: Table
    Data Table
    Table containing Document or String columns.
Output ports
  1. Type: Word Vector Model
    Word Vector Model
    Trained Doc2Vec Model.

Extension

The Doc2Vec Learner node is part of this extension:

Related workflows & nodes

  1. Calculate Document Distance using Word Vectors
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    First, we read in a dataset containing sentences and assign each document a unique label.…
    knime > Examples > 04_Analytics > 14_Deep_Learning > 01_DL4J > 06_Calculate_Document_Distance_Using_Word_Vectors
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  2. Calculate Document Distance using Word Vectors
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    First, we read in a dataset containing sentences and assign each document a unique label.…
    polathdr > Public > 06_Calculate_Document_Distance_Using_Word_Vectors
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  3. Simple Document Classification using Word Vectors
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    This example shows how to transform a document into a vector using a word vector model an…
    knime > Examples > 04_Analytics > 14_Deep_Learning > 01_DL4J > 07_Simple_Document_Classification_Using_Word_Vectors
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