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):
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GloVe Project Page:
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