The article deals with the concept of building an automated system for the harvesting of apple crops. This system is a robotic complex mounted on a tractor cart, including an industrial robot and a packaging system with a container for fruit collection. The robot is equipped with a vacuum gripper and a vision system. A generator for power supply, a vacuum pump for the gripper and an equipment control system are also installed on the cart. The developed automated system will have a high degree of reliability that meets the requirements of operation in the field.

This article discusses modern methods of extrapolating pre-trained transformers aimed at improving their ability to process long and short text sequences in Russian in the financial sector. Particular attention is paid to the task of classifying texts that reflect broker analysts’ expectations regarding market movements (expectations of growth, decline, or uncertainty of change). To solve this problem, the application of lightweight language models ruBERTtiny1 and ruBERT-tiny2 is investigated, which are adapted to work effectively with large amounts of input data while maintaining prediction quality. The paper analyzes various approaches to expanding the contextual window of models, including extrapolation methods, and considers the impact of tokenization, vectorization, and embedding strategies on the final classification results. Additionally, the paper discusses the peculiarities of using transformers in conditions of increased market volatility and changing news flows, which allows for a more in-depth assessment of the stability of the proposed solutions. Furthermore, a formula for calculating a leading indicator for stock markets is proposed and discussed, demonstrating the practical significance of using transformer models in the analysis of financial texts and the formation of analytical metrics. The presented results highlight the promising application of compact transformers in predictive financial analytics tasks.

The paper discusses tokenization as a key step in textual data processing, especially in the financial domain. Current tokenization techniques are analyzed with examples from recent research and their impact on the performance of NLP models. The study shows that word-based tokenization algorithms (BPE, WordPiece, Unigram) have become the standard for language models due to their flexibility and text compression efficiency. We discuss the limitations of input sequence length in language models (BPE and WordPiece show a tendency to over-partition, Unigram requires complex training, and symbolic tokenisation creates excessively long sequences) and methods to overcome these limitations, including text partitioning, hierarchical processing and extrapolation of pre-trained models with Transformer architecture to handle long input data. For financial data, it is recommended to use domain-specific tokenizers or additional training on specialized systems, which is confirmed by the successful experience of BloomberGPT. Special attention is paid to the problem of processing long texts. Three solution approaches are proposed: text partitioning; hierarchical processing; extrapolation of transformer models. In conclusion, the importance of tokenization for financial analytics is emphasized, where the quality of text processing directly affects decision-making. The development of tokenization methods continues in parallel with the improvement of NLP models, which makes this stage of text processing a critical component of modern analytical systems.