Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
multi-class-classification
Languages:
Spanish
Size:
1K - 10K
License:
| YAML tags: | |
| annotations_creators: | |
| - automatically-generated | |
| language_creators: | |
| - found | |
| language: | |
| - es | |
| license: | |
| - cc-by-sa-3.0 | |
| multilinguality: | |
| - monolingual | |
| pretty_name: wikicat_esv2 | |
| size_categories: | |
| - unknown | |
| source_datasets: [] | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - multi-class-classification | |
| # WikiCAT_es: Spanish Text Classification dataset | |
| ## Dataset Description | |
| - **Paper:** | |
| - **Point of Contact:** carlos.rodriguez1@bsc.es | |
| **Repository** | |
| ### Dataset Summary | |
| WikiCAT_ca is a Spanish corpus for thematic Text Classification tasks. It is created automatically from Wikipedia and Wikidata sources, and contains 8401 articles from the Viquipedia classified under 12 different categories. | |
| This dataset was developed by BSC TeMU as part of the PlanTL project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus. | |
| ### Supported Tasks and Leaderboards | |
| Text classification, Language Model | |
| ### Languages | |
| ES- Spanish | |
| ## Dataset Structure | |
| ### Data Instances | |
| Two json files, one for each split. | |
| ### Data Fields | |
| We used a simple model with the article text and associated labels, without further metadata. | |
| #### Example: | |
| <pre> | |
| {'sentence': 'La economía de Reunión se ha basado tradicionalmente en la agricultura. La caña de azúcar ha sido el cultivo principal durante más de un siglo, y en algunos años representa el 85% de las exportaciones. El gobierno ha estado impulsando el desarrollo de una industria turística para aliviar el alto desempleo, que representa más del 40% de la fuerza laboral.(...) El PIB total de la isla fue de 18.800 millones de dólares EE.UU. en 2007., 'label': 'Economía'} | |
| </pre> | |
| #### Labels | |
| 'Religión', 'Entretenimiento', 'Música', 'Ciencia_y_Tecnología', 'Política', 'Economía', 'Matemáticas', 'Humanidades', 'Deporte', 'Derecho', 'Historia', 'Filosofía' | |
| ### Data Splits | |
| * hfeval_esv5.json: 1681 label-document pairs | |
| * hftrain_esv5.json: 6716 label-document pairs | |
| ## Dataset Creation | |
| ### Methodology | |
| La páginas de "Categoría" representan los temas. | |
| para cada tema, extraemos las páginas asociadas a ese primer nivel de la jerarquía, y utilizamos el resúmen ("summary") como texto representativo. | |
| ### Curation Rationale | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| The source data are thematic categories in the different Wikipedias | |
| #### Who are the source language producers? | |
| ### Annotations | |
| #### Annotation process | |
| Automatic annotation | |
| #### Who are the annotators? | |
| [N/A] | |
| ### Personal and Sensitive Information | |
| No personal or sensitive information included. | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| We hope this corpus contributes to the development of language models in Spanish. | |
| ### Discussion of Biases | |
| We are aware that this data might contain biases. We have not applied any steps to reduce their impact. | |
| ### Other Known Limitations | |
| [N/A] | |
| ## Additional Information | |
| ### Dataset Curators | |
| Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es). | |
| For further information, send an email to (plantl-gob-es@bsc.es). | |
| This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx). | |
| ### Licensing Information | |
| This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License. | |
| Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022) | |
| ### Contributions | |
| [N/A] | |