--- dataset_info: - config_name: all_languages_highlevel features: - name: text dtype: string - name: label dtype: string - name: lang dtype: string - name: id dtype: string splits: - name: train num_bytes: 710259 num_examples: 6430 - name: validation num_bytes: 179231 num_examples: 1608 - name: test num_bytes: 222985 num_examples: 2010 download_size: 601522 dataset_size: 1112475 - config_name: all_languages_lowlevel features: - name: text dtype: string - name: labels sequence: string - name: lang dtype: string - name: id dtype: string splits: - name: train num_bytes: 826954 num_examples: 6430 - name: validation num_bytes: 207973 num_examples: 1608 - name: test num_bytes: 260637 num_examples: 2010 download_size: 614714 dataset_size: 1295564 - config_name: high_resources_highlevel features: - name: text dtype: string - name: label dtype: string - name: lang dtype: string - name: id dtype: string splits: - name: train num_bytes: 569544 num_examples: 5353 - name: validation num_bytes: 142698 num_examples: 1339 download_size: 359821 dataset_size: 712242 - config_name: high_resources_lowlevel features: - name: text dtype: string - name: labels sequence: string - name: lang dtype: string - name: id dtype: string splits: - name: train num_bytes: 668111 num_examples: 5353 - name: validation num_bytes: 166962 num_examples: 1339 download_size: 368829 dataset_size: 835073 - config_name: only_english_highlevel features: - name: text dtype: string - name: label dtype: string - name: lang dtype: string - name: id dtype: string splits: - name: train num_bytes: 162298 num_examples: 1747 - name: validation num_bytes: 40922 num_examples: 437 download_size: 88620 dataset_size: 203220 - config_name: only_english_lowlevel features: - name: text dtype: string - name: labels sequence: string - name: lang dtype: string - name: id dtype: string splits: - name: train num_bytes: 191533 num_examples: 1747 - name: validation num_bytes: 48286 num_examples: 437 download_size: 91808 dataset_size: 239819 configs: - config_name: all_languages_highlevel data_files: - split: train path: all_languages_highlevel/train-* - split: validation path: all_languages_highlevel/validation-* - split: test path: all_languages_highlevel/test-* - config_name: all_languages_lowlevel data_files: - split: train path: all_languages_lowlevel/train-* - split: validation path: all_languages_lowlevel/validation-* - split: test path: all_languages_lowlevel/test-* - config_name: high_resources_highlevel data_files: - split: train path: high_resources_highlevel/train-* - split: validation path: high_resources_highlevel/validation-* - config_name: high_resources_lowlevel data_files: - split: train path: high_resources_lowlevel/train-* - split: validation path: high_resources_lowlevel/validation-* - config_name: only_english_highlevel data_files: - split: train path: only_english_highlevel/train-* - split: validation path: only_english_highlevel/validation-* - config_name: only_english_lowlevel data_files: - split: train path: only_english_lowlevel/train-* - split: validation path: only_english_lowlevel/validation-* task_categories: - text-classification language: - en - es - pl - hu - el - da - tr - ja - sv - fi - 'no' - ru - it - he - is tags: - finance size_categories: - 1K MultiFin – a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families. The dataset consists of hierarchical label structure providing two classification tasks: multi-label and multi-class. ## Dataset Details ### Dataset Description The MULTIFIN dataset is a multilingual corpus, consisting of real-world article headlines covering 15 languages. The corpus is annotated using hierarchical label structure, providing two classification tasks: multi-class and multi-label classification. - **Curated by:** Rasmus Jørgensen, Oliver Brandt, Mareike Hartmann, Xiang Dai, Christian Igel, and Desmond Elliott. - **Language(s) (NLP):** English, Spanish, Polish, Hungarian, Greek, Danish, Turkish, Japanese, Swedish, Finnish, Norwegian, Russian, Italian, Hebrew, Icelandic. - **License:** [More Information Needed] ### Dataset Sources [optional] - **Repository:** https://github.com/RasmusKaer/MultiFin - **Paper:** https://aclanthology.org/2023.findings-eacl.66/ ## Dataset Structure [More Information Needed] ## Citation **BibTeX:** @inproceedings{jorgensen-etal-2023-multifin, title = "{M}ulti{F}in: A Dataset for Multilingual Financial {NLP}", author = "J{\o}rgensen, Rasmus and Brandt, Oliver and Hartmann, Mareike and Dai, Xiang and Igel, Christian and Elliott, Desmond", editor = "Vlachos, Andreas and Augenstein, Isabelle", booktitle = "Findings of the Association for Computational Linguistics: EACL 2023", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.findings-eacl.66", doi = "10.18653/v1/2023.findings-eacl.66", pages = "894--909", abstract = "Financial information is generated and distributed across the world, resulting in a vast amount of domain-specific multilingual data. Multilingual models adapted to the financial domain would ease deployment when an organization needs to work with multiple languages on a regular basis. For the development and evaluation of such models, there is a need for multilingual financial language processing datasets. We describe MultiFin {--} a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families. The dataset consists of hierarchical label structure providing two classification tasks: multi-label and multi-class. We develop our annotation schema based on a real-world application and annotate our dataset using both {`}label by native-speaker{'} and {`}translate-then-label{'} approaches. The evaluation of several popular multilingual models, e.g., mBERT, XLM-R, and mT5, show that although decent accuracy can be achieved in high-resource languages, there is substantial room for improvement in low-resource languages.", }