multilingual IPTC news topic classifier EU AI Act Compliance Profile

Multilingual IPTC Media Topic Classifier

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.

Intended use and limitations

For reliable results, the classifier should be applied to documents of sufficient length (the rule of thumb is at least 75 words).

Use example:

from transformers import pipeline

# Load a multi-class classification pipeline - if the model runs on CPU, comment out "device"
classifier = pipeline("text-classification", model="classla/multilingual-IPTC-news-topic-classifier", device=0, max_length=512, truncation=True)

# Example texts to classify
texts = [
    """Slovenian handball team makes it to Paris Olympics semifinal Lille, 8 August - Slovenia defeated Norway 33:2

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Base Model