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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- fr |
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- fon |
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configs: |
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- config_name: FFRv2 |
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data_files: |
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- split: train |
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path: "data/ffr_dataset_v2.txt" |
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- config_name: FFR_Daily_dialog |
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data_files: |
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- split: train |
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path: "data/Fon_French_Parallel_Data.txt" |
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--- |
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> [!NOTE] |
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> Dataset origin: https://github.com/bonaventuredossou/ffr-v1 |
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# Description |
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The authors of the dataset provide a description in the following PDFs: [here](https://huggingface.co/datasets/de-francophones/FFR/blob/main/FFR_Dataset_Documentation.pdf) and [here](https://huggingface.co/datasets/de-francophones/FFR/blob/main/Data_Statement_FFR_Dataset.pdf). |
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# Citation |
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``` |
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@inproceedings{emezue-dossou-2020-ffr, |
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title = "{FFR} v1.1: {F}on-{F}rench Neural Machine Translation", |
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author = "Emezue, Chris Chinenye and |
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Dossou, Femi Pancrace Bonaventure", |
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editor = "Cunha, Rossana and |
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Shaikh, Samira and |
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Varis, Erika and |
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Georgi, Ryan and |
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Tsai, Alicia and |
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Anastasopoulos, Antonios and |
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Chandu, Khyathi Raghavi", |
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booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop", |
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month = jul, |
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year = "2020", |
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address = "Seattle, USA", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2020.winlp-1.21", |
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doi = "10.18653/v1/2020.winlp-1.21", |
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pages = "83--87", |
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abstract = "All over the world and especially in Africa, researchers are putting efforts into building Neural Machine Translation (NMT) systems to help tackle the language barriers in Africa, a continent of over 2000 different languages. However, the low-resourceness, diacritical, and tonal complexities of African languages are major issues being faced. The FFR project is a major step towards creating a robust translation model from Fon, a very low-resource and tonal language, to French, for research and public use. In this paper, we introduce FFR Dataset, a corpus of Fon-to-French translations, describe the diacritical encoding process, and introduce our FFR v1.1 model, trained on the dataset. The dataset and model are made publicly available, to promote collaboration and reproducibility.", |
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} |
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``` |
