We introduce Bielik 7B v0.1, a 7-billion-parameter generative text model for Polish language processing. Trained on curated Polish corpora, this model addresses key challenges in language model development through innovative techniques. These include Weighted Instruction Cross-Entropy Loss, which balances the learning of different instruction types, and Adaptive Learning Rate, which dynamically adjusts the learning rate based on training progress. To evaluate performance, we created the Open PL LLM Leaderboard and Polish MT-Bench, novel frameworks assessing various NLP tasks and conversational abilities. Bielik 7B v0.1 demonstrates significant improvements, achieving a 9 percentage point increase in average score compared to Mistral-7B-v0.1 on the RAG Reader task. It also excels in the Polish MT-Bench, particularly in Reasoning (6.15/10) and Role-playing (7.83/10) categories. This model represents a substantial advancement in Polish language AI, offering a powerful tool for diverse linguistic applications and setting new benchmarks in the field.
Balancing Natural Language Processing Accuracy and Normalisation in Extracting Medical Insights
Sano - Centre for Computational Personalized Medicine（Sano - 计算个性化医学中心）；Jagiellonian University（雅盖隆大学）；AGH University of Krakow（克拉科夫AGH大学）；Voivodeship Rehabilitation Hospital for Children in Ameryka（阿梅里卡省儿童康复医院）；University of Warmia and Mazury（瓦尔米亚和马祖里大学）
Umm... With Transformers? Insights from Filled Pause Use across Four Slavic Parliaments
Jožef Stefan Institute（约瑟夫·施特凡研究所）；TransUnion（环联公司）；University of Zagreb（萨格勒布大学）；University of Ljubljana（卢布尔雅那大学）；Institute of Contemporary History（当代历史研究所）
