Introduction to transformers: a break through in NLP
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Sabrina Queipo [1] ; Antonio Garcia-Cabot [2]
;
Eva Garcia-Lopez
[2]
;
David de-Fitero Domínguez
[2]
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[1] Massachusetts Institute of Technology
Massachusetts Institute of Technology
City of Cambridge, Estados Unidos
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[2] Universidad de Alcalá
Universidad de Alcalá
Alcalá de Henares, España
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- Localización: ATICA2022: Aplicación de Tecnologías de la Información y Comunicaciones Avanzadas y Accesibilidad / coord. por Luis Bengochea Martínez, Paola Ingavélez Guerra, José Ramón Hilera González , 2023, ISBN 9788419745538, págs. 142-147
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Transformers are amongst the most powerful classes of NLP models invented to date. From machine translation to speech recognition systems, models such as BERT and GPT-2 are achieving state-of-the-art performance both in terms of evaluation score (BLEU score) and training time. What’s novel about transformers? Like many other scientific breakthroughs, transformers are the synthesis of several ideas, including transfer learning, attention, and scaling up neural networks. This paper describes the key mechanisms behind their success. In addition, variants of the transformer architecture are described, as well as an overview to the Hugging Face ecosystem. This paper aims to introduce the theory of transformers, essential for students to start deploying and training their own models for practical applications
