Hands-On Machine Learning for Algorithmic Trading

GloVe – global vectors for word representation

GloVe is an unsupervised algorithm developed at the Stanford NLP lab that learns vector representations for words from aggregated global word-word co-occurrence statistics (see references). Vectors pretrained on the following web-scale sources are available:

• Common Crawl with 42B or 840B tokens and a vocabulary or 1.9M or 2.2M tokens
• Wikipedia 2014 + Gigaword 5 with 6B tokens and a vocabulary of 400K tokens
• Twitter using 2B tweets, 27B tokens and a vocabulary of 1.2M tokens

We can use gensim to convert and load the vector text files into the KeyedVector object:

from gensim.models import Word2vec, KeyedVectors from gensim.scripts.glove2Word2vec import glove2Word2vecglove2Word2vec(glove_input_file=glove_file, ...