Create README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+language:
+- fr
+- fon
+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"
+---
+
+> [!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.",
+}
+```