{'fraction_non_alphanumeric': 0.03345724907063197, 'fraction_numerical': 0.016728624535315983, 'mean_word_length': 5.195402298850575, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Artificial text detection in Russian language: a BERT-based Approach

This paper describes our solution for the RuATD (Russian Artificial Text Detection) competition held within the Dialog 2022 conference. Our approach is based on the idea of transfer learning, using pre-trained RuRoBERTa, RuBERT, RuGPT3, RuGPT2 models. The final solution included Byte-level Byte-Pair Encoding tokenization, and a fine-tuned model RuRoBERTa model. The system got Accuracy metric value of 0.65 and took first place in the multiclass classification task.