Detekce strojově přeložených textů pomocí strojového učení
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Vysoká škola báňská – Technická univerzita Ostrava
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This thesis aims to describe and design a classifier for text data that can detect machine-translated texts using Google Translate and DeepL. This involves creating a custom dataset on which the models, of different architectures and modifications, will be taught and tested.
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Machine learning, Neural Network, Transformer, LSTM, GRU, BERT, NLP, Semantic analysis, Dataset, Model