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Nicolay Rusnachenko

nicolay-r
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https://nicolay-r.github.io/

AI & ML interests

Information Retrieval・Medical Multimodal NLP (πŸ–Ό+πŸ“) Research Fellow @BU_Research・software developer http://arekit.io・PhD in NLP

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posted an update about 15 hours ago
πŸ“’ For those who tracking adances in Sentiment Analysis, this post might be relevant for you. So far we arranged RuOpinionNE-2024 competition with final stage that has just been completed several days ago. Here the quick findings we got from the top submissions. πŸŽ‰ πŸ” First, RuOpinonNE-2024 competition πŸ’» on extraction of opinion tuples (opinion source, opinion object, tonality, linguistic expression) from low resource domain news texts written in Russian language. The competition is hosted by codalab platform: https://codalab.lisn.upsaclay.fr/competitions/20244 To asses the advanes, we adopt F1 over sentiment classes which also involves evalution of the spans. πŸ‘ Among 7 participants in total, the top three submissions showcase the following results: πŸ₯‰msuai F1=0.33 🎊 πŸ₯ˆRefalMachine showcase +0.02 F1=0.35 🎊 πŸ†VatolinAlexey showcase +0.06 F1=0.41 🎊 πŸ“ At present, the competition organizers are working on: 1. 🟑 Collecting information about models utilized participants to contribute here with pre-trained models / concepts; 2. 🟑 Wrapping up findings from the submissions in a paper. πŸ”” For more information and further updates, the most complete source that complements codalab is this github: https://github.com/dialogue-evaluation/RuOpinionNE-2024 The RuOpinionNE-2024 are now in post-evaluation stage, so everyone interested in low resouce domain evaluation on opinon extraction are welcome πŸ™Œ
posted an update 2 days ago
πŸ“’ Deligted to share the new version of the bulk-ner which represent a tiny framework that would save you time for deploing NER with any model. πŸ“¦: https://pypi.org/project/bulk-ner/0.25.1/ 🌟: https://github.com/nicolay-r/bulk-ner The direct adaptaion of the LM for NER would result in spending signifcant amount of time on formatting your texts according to the NER-model needs. In particular: 1. Processing CONLL format with B-I-O tags from model outputs 2. Input trimming: long input content might not be completely fitted The 0.25.1 I made a huge steps forward by providing: βœ… Enhanced integration by providing function for casting extracted enties to your type (see picture below) βœ… Enhanced integration with AREkit pipelines βœ… Simpified API for using (Example using DeepPavlov NER models): https://github.com/nicolay-r/bulk-ner/wiki#api πŸ‘ The code for pipeline deployment is taken from the AREkit project: https://github.com/nicolay-r/AREkit
reacted to cutechicken's post with πŸš€ 6 days ago
πŸ”¬ PaperImpact : Scientific Impact Predictor Powered by Deep Learning 🎯 https://huggingface.co/spaces/VIDraft/PaperImpact πŸ“š Overview A cutting-edge AI system that combines transformer architecture with citation pattern analysis to predict research impact. Our model, trained on 120,000+ CS papers, analyzes innovation potential, methodological robustness, and future impact, providing researchers with valuable insights before publication. 🧠 Scientific Foundation BERT-based semantic analysis Citation network pattern learning NDCG optimization & MSE loss Cross-validated prediction engine GPU-accelerated inference πŸ’« Why Researchers Need This Pre-submission impact assessment Research direction optimization Time-saving paper evaluation Competitive edge in academia Trend identification advantage 🎯 Key Features One-click arXiv paper analysis Real-time impact scoring (0-1) 9-tier grading system (AAA-C) Smart input validation Instant visual feedback 🌟 Unique Benefits "Don't wait years to know your paper's impact. Get instant, AI-powered insights to strengthen your research strategy and maximize your academic influence." Perfect for: Research authors PhD students Journal editors Research institutions Grant committees #ResearchImpact #AcademicAI #ScienceMetrics #ResearchExcellence
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commented 2 papers 9 months ago
commented 5 papers 10 months ago