---
license: cc-by-nc-sa-4.0
language:
- fr
- fon
multilinguality:
  - multilingual
configs:
- config_name: FFRv2
  data_files:
  - split: train
    path: data/ffr_dataset_v2.txt
- config_name: FFR_Daily_dialog
  data_files:
  - split: train
    path: data/Fon_French_Parallel_Data.txt
task_categories:
- translation
---

> [!NOTE]
> Dataset origin: https://github.com/bonaventuredossou/ffr-v1


# Description
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).


# Citation

```
@inproceedings{emezue-dossou-2020-ffr,
    title = "{FFR} v1.1: {F}on-{F}rench Neural Machine Translation",
    author = "Emezue, Chris Chinenye  and
      Dossou, Femi Pancrace Bonaventure",
    editor = "Cunha, Rossana  and
      Shaikh, Samira  and
      Varis, Erika  and
      Georgi, Ryan  and
      Tsai, Alicia  and
      Anastasopoulos, Antonios  and
      Chandu, Khyathi Raghavi",
    booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.winlp-1.21",
    doi = "10.18653/v1/2020.winlp-1.21",
    pages = "83--87",
    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.",
}
```