Uzbek 65 million web corpus Word2Vec model

Abstract

Word2Vec model developed from 65 million Uzbek web corpus (10.5281/zenodo.19462612). The model can be used for efficient text representation via Word embeddings (vector representations). A simple Python script for loading and using the model. Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. The Word2Vec implementation used to produce the model, which can later be used for the model, was Gensim.

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