Spanish is one of the most spoken languages in the globe, but not necessarily Spanish is written and spoken in the same way in different countries. Understanding local language variations can help to improve model performances on regional tasks, both understanding local structures and also improving the message's content. For instance, think about a machine learning engineer who automatizes some language classification task on a particular region or a social scientist trying to understand a regional event with echoes on social media; both can take advantage of dialect-based language models to understand what is happening with more contextual information hence more precision. This manuscript presents and describes a set of regionalized resources for the Spanish language built on four-year Twitter public messages geotagged in 26 Spanish-speaking countries. We introduce word embeddings based on FastText, language models based on BERT, and per-region sample corpora. We also provide a broad comparison among regions covering lexical and semantical similarities; as well as examples of using regional resources on message classification tasks.

Every day for over four years, Ramses woke up in his home in Barquisimeto, Venezuela, turned on his computer, and began labeling images that will help make self-driving cars ubiquitous one day. Through a microtasking platform called Remotasks, he would identify mundane objects that line the streets everywhere -- trees, lampposts, pedestrians, stop signs -- so that autonomous vehicles could learn to notice them, too. Like many Venezuelans, Ramses turned to microtasking when his country plunged into economic turmoil. The gig gave him the opportunity to earn American dollars instead of the local currency, which is subject to extraordinarily high inflation. "I would work Sunday to Sunday," Ramses, who asked to use only his first name for privacy reasons, told Rest of World over WhatsApp.