Please select the classification you wish to use:



Current Supported Languages per Task

Language \ Task Policy Content Binary Classifier (for CAP) CAP Manifesto Sentiment (3) Emotions (6) NER
Coverage % 6.12% 13.27% 32.65% 7.14% 7.14% 2.04%
Armenian
Bulgarian
Croatian
Czech
Danish
Dutch
English
Estonian
Finnish
French
Georgian
German
Greek
Hebrew
Hungarian
Icelandic
Italian
Japanese
Korean
Latvian
Lithuanian
Norwegian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Somali
Spanish
Swedish
Turkish


The research was supported by the Ministry of Innovation and Technology NRDI Office within the RRF-2.3.1-21-2022-00004 Artificial Intelligence National Laboratory project and received additional funding from the European Union's Horizon 2020 program under grant agreement no 101008468. We also thank the Babel Machine project and HUN-REN Cloud (Héder et al. 2022; https://science-cloud.hu) for their support. We used the machine learning service of the Slices RI infrastructure (https://www.slices-ri.eu/)


HOW TO CITE: If you use the Babel Machine for your work or research, please cite this paper:

Sebők, M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2024). Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393241259434