A.3.1 Classification
KinopoiskSentimentClassification . In a sentiment classification dataset given a film review, one has to predict whether it is Positive, Neutral, or Negative (3 classes in total). The data was taken from the original dataset Blinov et al. ( 2013 ) 30 30 30 https://huggingface.co/datasets/blinoff/kinopoisk , which contains reviews from July 2004 to November 2012. In the preprocessing phase, we removed all mentions of the final rating from the review texts and balanced the set, leaving only 4,500 samples of each class. The resulting dataset was split into three parts (train, validation, and test), with the class balance preserved.

GeoReviewClassification A classification dataset, where given a review text one has to predict its rating ranging from 1 to 5 (five classes in total). The set is based on the Yandex Maps 31 31 31 https://yandex.ru/maps reviews 32 32 32 https://github.com/yandex/geo-reviews-dataset-2023 . The original dataset was balanced and split into three parts (train, validation, and test).

HeadlineClassification . In this dataset, the model needs to determine which news category the article title belongs to. The dataset was built based on ParaPhraserPlus Gudkov et al. ( 2020 ) and contained 10,000 examples for each category, divided into train/validation/test splits of 6000, 2000, and 2000, respectively. A total of 6 classes are used: sports, incidents, politics, science, culture and economics. First, categories that contained at least 10,000 examples were selected. Other categories were discarded due to overlap between categories. For this purpose, we trained a classifier over SBERT {}_{\textnormal{large-ru}} embeddings.

RuReviewsClassification A sentiment classification dataset where top-ranked goods from a major e-commerce site were provided, and user-ranked scores were used as class labels on a 5-point scale. The data was sourced from the original dataset RuReviews 33 33 33 https://github.com/sismetanin/rureviews?tab=readme-ov-file , which contains reviews in the “Women’s Clothes and Accessories” category. During the preprocessing stage, duplicates were removed, and the dataset was balanced, resulting in only 25,000 samples for each class. The resulting dataset was divided into three parts (train, validation, and test) while maintaining class balance.

RuSciBenchGRTNI/OECDClassification .
This is a dataset for the classification of scientific text headings. Each article has its OECD and GRNTI headings, with 29 OECD headings and 28 GRNTI headings in the dataset (e.g., Mathematics, Biological Sciences, Economics and Business, etc.). The data was sourced from the original dataset RuSciBench 34 34 34 https://huggingface.co/datasets/mlsa-iai-msu-lab/ru_sci_bench . During preprocessing, duplicates were removed, the title and abstract were combined, and the set was balanced, leaving only the same number of samples for each class. The resulting dataset was then divided into test and training parts.

InappropriatnessClassification . The dataset aims to predict whether the message is inappropriate or not in the form of binary classification. The data is based on the Inappropriate Messages dataset (version 3) 35 35 35 https://github.com/s-nlp/inappropriate-sensitive-topics/blob/main/Version3/Inappapropriate_messages.csv Babakov et al. ( 2021 ) . We binarized the inappropriateness scores using the 0.5 threshold. The resulting dataset was balanced and split into three parts (train, validation, and test), with the class balance preserved.