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.
This model for Serbian ASR is based on the facebook/wav2vec2-xls-r-300m model and was fine-tuned with 58 hours of audio and transcripts from Južne vesti, programme '15 minuta'. For more info on the dataset creation see this repo. Tested with `transformers==4.18.0`, `torch==1.11.0`, and `SoundFile==0.10.3.post1`.
This model for Croatian ASR is based on the facebook/wav2vec2-xls-r-300m model and was fine-tuned with 300 hours of recordings and transcripts from the ASR Croatian parliament dataset ParlaSpeech-HR v1.0. Notice: ParlaSpeech corpora are currently in the process of enrichment with new features. Follow our progress here: http://clarinsi.github.io/parlaspeech Nikola Ljubešić, Danijel Koržinek, Peter Rupnik, Ivo-Pavao Jazbec. ParlaSpeech-HR -- a freely available ASR dataset for Croatian bootstrapped from the ParlaMint corpus. http://www.lrec-conf.org/proceedings/lrec2022/workshops/ParlaCLARINIII/pdf/2022.parlaclariniii-1.16.pdf
Claude Opus 4.1是Anthropic旗舰模型的更新版本，在编码、推理和代理任务方面提供了改进的性能。它在sw -bench验证上达到74.5%，并在多文件代码重构、调试精度和面向细节的推理方面显示出显著的进步。该模型支持最多64K令牌的扩展思维，并针对涉及研究、数据分析和工具辅助推理的任务进行了优化。
Claude Opus 4.1是Anthropic旗舰模型的更新版本，在编码、推理和代理任务方面提供了改进的性能。它在sw -bench验证上达到74.5%，并在多文件代码重构、调试精度和面向细节的推理方面显示出显著的进步。该模型支持最多64K令牌的扩展思维，并针对涉及研究、数据分析和工具辅助推理的任务进行了优化。
