In recent times, GenAI has sparked fervent excitement, sometimes touted as the panacea for all natural language processing (NLP) tasks. This presentation explores a practical text classification scenario at Malt, highlighting the practical hurdles encountered when employing GenAI (latency, environmental impact, and budgetary constraints). To overcome these obstacles, a smaller, dedicated model emerged as a viable solution. We'll delve into the construction and optimization (quantization, graph optimization) of this multilingual model. Finally we’ll see how GenAI's unparalleled zero-shot capabilities enables its continuous adaptation.

Marc Palyart

Affiliation: Malt

Marc Palyart is a Staff Data Scientist at Malt, the freelancer marketplace, where he leads the search and matching team. With over a decade of data-wizardry under his belt, he's ventured into the depths of academia and scaled the heights of industry where he's had the pleasure of collaborating with some truly remarkable people.

Kateryna Budzyak