Model overview
News topic classification model based on `xlm-roberta-large` and fine-tuned on a news corpus in 4 languages (Croatian, Slovenian, Catalan and Greek), annotated with the top-level IPTC Media Topic NewsCodes labels. The development and evaluation of the model is described in the paper LLM Teacher-Student Framework for Text Classification With No Manually Annotated Data: A Case Study in IPTC News Topic Classification (Kuzman and Ljubešić, 2025). The model can be used for classification into topic labels from the IPTC NewsCodes schema and can be applied to any news text in a language, supported by the `xlm-roberta-large`. Based on a manually-annotated test set (in Croatian, Slovenian, Catalan and Greek), the model achieves macro-F1 score of 0.746, micro-F1 score of 0.734, and accuracy of 0.734, and outperforms the GPT-4o model (version `gpt-4o-2024-05-13`) used in a zero-shot setting. If we use only labels that are predicted with a confidence score equal or higher than 0.90, the model achieves micro-F1 and macro-F1 of 0.80.
Key features
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Limitations & considerations
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Public benchmarks & run configuration
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Sources & traceability
许可证：cc-by-sa-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。