| --- |
| 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 |
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| 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 |
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| 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<n<10K |
| --- |
| |
| # MultiFin |
|
|
| <!-- Provide a quick summary of the dataset. --> |
|
|
| 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 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 |
|
|
| <!-- Provide the basic links for the dataset. --> |
|
|
| - **Repository:** https://github.com/RasmusKaer/MultiFin |
| - **Paper:** https://aclanthology.org/2023.findings-eacl.66/ |
|
|
|
|
| ## Dataset Structure |
|
|
| <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> |
|
|
| The dataset consists of 10,048 headlines in 15 languages annotated with 23 topic labels for LOW-LEVEL and 6 HIGH-LEVEL topics for multi-class. |
|
|
| The dataset has been further stratified into two subsets: |
| 1. **only_english**: that contains only English training data. |
| 2. **high_resources:** a subset that contains 5 high-resource languages (i.e., English, Turkish, Danish, Spanish, Poland). |
|
|
|
|
| ## Citation |
|
|
| <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
|
|
| **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.", |
| } |
| ``` |
|
|
|
|
|
|
|
|
|
|
