| | --- |
| | license: cc-by-nc-4.0 |
| | language: |
| | - fr |
| | pipeline_tag: text-classification |
| | widget: |
| | - text: >- |
| | * ALBI, (Géog.) ville de France, capitale de l'Albigeois, dans le haut |
| | Languedoc : elle est sur le Tarn. Long. 19. 49. lat. 43. 55. 44. |
| | --- |
| | |
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| | # bert-base-multilingual-cased-edda-domain-classification |
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| | <!-- Provide a quick summary of what the model is/does. --> |
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| | This model is designed to classify encyclopedia articles into knowledge domains (e.g., History, Geography, Medicine, ...). |
| | It is a fine-tuned version of the bert-base-multilingual-cased model. |
| | It has been trained on the French *Encyclopédie ou dictionnaire raisonné des sciences des arts et des métiers par une société de gens de lettres (1751-1772)* edited by Diderot and d'Alembert (provided by the [ARTFL Encyclopédie Project](https://artfl-project.uchicago.edu)). |
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| | ## Model Description |
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| | <!-- Provide a longer summary of what this model is. --> |
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| | - **Developed by:** [Alice Brenon](https://perso.liris.cnrs.fr/abrenon/), [Ludovic Moncla](https://ludovicmoncla.github.io), [Katherine McDonough](https://www.lancaster.ac.uk/dsi/about-us/members/katherine-mcdonough#projects), and Khaled Chabane in the framework of the [GEODE](https://geode-project.github.io) project. |
| | - **Model type:** Text classification |
| | - **Repository:** [https://gitlab.liris.cnrs.fr/geode/EDdA-Classification/](https://gitlab.liris.cnrs.fr/geode/EDdA-Classification/) |
| | - **Language(s) (NLP):** French |
| | - **License:** cc-by-nc-4.0 |
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| | ## Class labels |
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| | %TODO |
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| | ## Bias, Risks, and Limitations |
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| | <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| | This model was trained entirely on French encyclopaedic entries and will likely not perform well on text in other languages or other corpora. |
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| | ## Cite this work |
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| | > Brenon, A., Moncla, L., & McDonough, K. (2022). Classifying encyclopedia articles: Comparing machine and deep learning methods and exploring their predictions. Data & Knowledge Engineering, 142, 102098. |
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| | ## Acknowledgement |
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| | The authors are grateful to the [ASLAN project](https://aslan.universite-lyon.fr) (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR). |
| | Data courtesy the [ARTFL Encyclopédie Project](https://artfl-project.uchicago.edu), University of Chicago. |
