Using aligned word vectors for instant translations with Python and Rust
Word vectors are generated using a neural network to learn how words are related from a large body of text-like a web crawl, or Wikipedia. Allison uses colors to illustrate how a word might be mapped to numeric values for Red, Green, and Blue. In 2016, Facebook Research released fastText along with a number of pre-trained models that map millions of words to a numeric representation. To build a simple translation tool, we will start by downloading the word vector data published by fastText. We'll index the word vectors with Instant Distance. f.). Finally, using these tools we can convert an input into its word vector and use Instant Distance to find the nearest neighbors to the input. Since the word vectors have all been aligned, the closest word vectors in different languages should be very similar-if not a direct translation.