Instructions to use Ife/BM-FR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ife/BM-FR with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ife/BM-FR") model = AutoModelForSeq2SeqLM.from_pretrained("Ife/BM-FR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - bm | |
| - fr | |
| @inproceedings{adebara-abdul-mageed-2021-improving, | |
| title = "Improving Similar Language Translation With Transfer Learning", | |
| author = "Adebara, Ife and | |
| Abdul-Mageed, Muhammad", | |
| booktitle = "Proceedings of the Sixth Conference on Machine Translation", | |
| month = nov, | |
| year = "2021", | |
| address = "Online", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2021.wmt-1.27", | |
| pages = "273--278", | |
| abstract = "We investigate transfer learning based on pre-trained neural machine translation models to translate between (low-resource) similar languages. This work is part of our contribution to the WMT 2021 Similar Languages Translation Shared Task where we submitted models for different language pairs, including French-Bambara, Spanish-Catalan, and Spanish-Portuguese in both directions. Our models for Catalan-Spanish (82.79 BLEU)and Portuguese-Spanish (87.11 BLEU) rank top 1 in the official shared task evaluation, and we are the only team to submit models for the French-Bambara pairs.", | |
| } |