ML Classification using GloVe Vectors & Keras | NLP Project in Python with GloVe, TensorFlow & Keras


In this NLP tutorial with Python we’ll use TensorFlow’s Keras to classify text with the help of GloVe Word Embeddings.

GloVe (Global Vectors) is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

The main intuition underlying the model is the simple observation that ratios of word-word co-occurrence probabilities have the potential for encoding some form of meaning.

The training objective of GloVe is to learn word vectors such that their dot product equals the logarithm of the words’ probability of co-occurrence.

We are going to use a 100 dimensional GloVe pre-trained corpus model to represent our words, trained on Twitter data (2B tweets, 27B tokens, 1.2M vocab).

You can access the Jupyter notebook here (login required):

How To Remove StopWords, Punctuation, Emojis and HTML from Strings with Regex:

GloVe Project Page:

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Comment List

  • DecisionForest
    January 6, 2021

    thanks for the great content, subscribed!

  • DecisionForest
    January 6, 2021

    Thank u soo much ,dear.
    Please, make a tutorial on "Character Embedding" for text classification , if possible.

  • DecisionForest
    January 6, 2021

    any idea how to determine max_len because i am having text of size more than 1000 words

  • DecisionForest
    January 6, 2021

    Thanks for the tutorial and that you show the code step-by-step with result, rather than just discussing the code

  • DecisionForest
    January 6, 2021

    Hi nice tutorial, can the Glove Vectors support portuguese words? Thanks you you helped me a lot

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