NodeDoc2Vec Learner

Learner

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

Input Ports

  1. Port Type: Data
    Table containing Document or String columns.

Output Ports

  1. Port Type: Word Vector Model
    Trained Doc2Vec Model.

  1. Calculate Document Distance using Word Vectors

    deeplearning machine learning word2vec doc2vec word vectors embeddings
    This example shows how to train a Word Vector model as well as some properties of the resulting vectors.
    shima0011 > 04_Analytics > 14_Deep_Learning > 01_DL4J > 06_Calculate_Document_Distance_Using_Word_Vectors

  2. Calculate Document Distance using Word Vectors

    deeplearning machine learning word2vec doc2vec word vectors embeddings
    This example shows how to train a Word Vector model as well as some properties of the resulting vectors.
    knime > Examples > 04_Analytics > 14_Deep_Learning > 01_DL4J > 06_Calculate_Document_Distance_Using_Word_Vectors

  3. Simple Document Classification using Word Vectors

    deeplearning machine learning word2vec doc2vec word vectors embeddings classification
    This example shows how to transform a document into a vector using a word vector model and using these vectors for class…
    shima0011 > 04_Analytics > 14_Deep_Learning > 01_DL4J > 07_Simple_Document_Classification_Using_Word_Vectors

  4. Simple Document Classification using Word Vectors

    deeplearning machine learning word2vec doc2vec word vectors embeddings classification
    This example shows how to transform a document into a vector using a word vector model and using these vectors for class…
    knime > Examples > 04_Analytics > 14_Deep_Learning > 01_DL4J > 07_Simple_Document_Classification_Using_Word_Vectors