This repository contains a few brief experiments with Stanford NLP's GloVe, an unsupervised learning algorithm for obtaining vector representations for words. Similar to Word2Vec, GloVe creates a continuous N-dimensional representation of a word that is learned from its surrounding context words in a training corpus. Trained on a large corpus of text, these co-occurance statistics (an N-dimensional vector embedding) cause semantically similar words to appear near each-other in their resulting N-dimensional embedding space (e.g. "dog" and "cat" may appear nearby a region of other pet related words in the embedding space because the context words that surround both "dog" and "cat" in the training corpus are similar). All three scripts use the GloVe.6B pre-trained word embeddings created from the combined Wikipedia 2014 and Gigaword 5 datasets. They were trained using 6 billion tokens and contains 400,000 unique lowercase words. Trained embeddings are provided in 50, 100, 200, and 300 dimensions (822 MB download).