Create README.md

README.md

@@ -0,0 +1,69 @@
+---
+language:
+- ig
+metrics:
+- bleu
+- ter
+base_model:
+- masakhane/m2m100_418M_ibo_en_rel_news
+pipeline_tag: translation
+---
+
+
+### Model Description
+
+This is a machine translation model from dialectal Igbo to English with the ability to handle regional language variations.
+
+
+**Paper:** https://huggingface.co/papers/2405.00997
+
+## How to use
+
+
+```python
+from transformers import AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer
+
+
+model = AutoModelForSeq2SeqLM.from_pretrained('Ifyokoh/m2m100_dialect_ibo_en_mt')
+tokenizer = AutoTokenizer.from_pretrained('Ifyokoh/m2m100_dialect_ibo_en_mt')
+```
+
+
+## Citation
+
+```cite
+@inproceedings{emezue-etal-2024-igboapi,
+    title = "The {I}gbo{API} Dataset: Empowering {I}gbo Language Technologies through Multi-dialectal Enrichment",
+    author = "Emezue, Chris Chinenye  and
+      Okoh, Ifeoma  and
+      Mbonu, Chinedu Emmanuel  and
+      Chukwuneke, Chiamaka  and
+      Lal, Daisy Monika  and
+      Ezeani, Ignatius  and
+      Rayson, Paul  and
+      Onwuzulike, Ijemma  and
+      Okeke, Chukwuma Onyebuchi  and
+      Nweya, Gerald Okey  and
+      Ogbonna, Bright Ikechukwu  and
+      Oraegbunam, Chukwuebuka Uchenna  and
+      Awo-Ndubuisi, Esther Chidinma  and
+      Osuagwu, Akudo Amarachukwu",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.1384",
+    pages = "15932--15941",
+    abstract = "The Igbo language is facing a risk of becoming endangered, as indicated by a 2025 UNESCO study. This highlights the need to develop language technologies for Igbo to foster communication, learning and preservation. To create robust, impactful, and widely adopted language technologies for Igbo, it is essential to incorporate the multi-dialectal nature of the language. The primary obstacle in achieving dialectal-aware language technologies is the lack of comprehensive dialectal datasets. In response, we present the IgboAPI dataset, a multi-dialectal Igbo-English dictionary dataset, developed with the aim of enhancing the representation of Igbo dialects. Furthermore, we illustrate the practicality of the IgboAPI dataset through two distinct studies: one focusing on Igbo semantic lexicon and the other on machine translation. In the semantic lexicon project, we successfully establish an initial Igbo semantic lexicon for the Igbo semantic tagger, while in the machine translation study, we demonstrate that by finetuning existing machine translation systems using the IgboAPI dataset, we significantly improve their ability to handle dialectal variations in sentences.",
+}
+```
+
+