Cohere's multilingual embedding model is revolutionizing cross-lingual text classification by enabling sentiment analysis, content moderation, and intent recognition across 100+ languages using training data from just one language. This model simplifies the traditionally complex task of gathering multilingual training data by allowing text to be classified based on the content rather than the language, with examples such as sentiment analysis of customer interactions, content moderation in global communities, and intent recognition in various applications. The model achieves this by translating texts into numeric vectors that map similar content to similar points in vector space, facilitating cross-language understanding. Cohere's approach includes several classification methods like nearest neighbor, nearest centroid, and logistic regression, each offering varying benefits in accuracy and speed. The Cohere multilingual-22-12 model notably outperforms popular alternatives, enhancing performance, particularly in non-English languages. This technology empowers organizations to leverage text classification for improved customer engagement and market insights, illustrating the growing importance of multilingual capabilities in a globally connected world.