| Hugging Face's logo | |
| --- | |
| language: | |
| - yo | |
| - en | |
| datasets: | |
| - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) | |
| --- | |
| # mT5_base_yor_eng_mt | |
| ## Model description | |
| **mT5_base_yor_eng_mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned mT5-base model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English. | |
| Specifically, this model is a *mT5_base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) | |
| ## Intended uses & limitations | |
| #### How to use | |
| You can use this model with Transformers *pipeline* for MT. | |
| ```python | |
| from transformers import MT5ForConditionalGeneration, T5Tokenizer | |
| model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_yor_eng_mt") | |
| tokenizer = T5Tokenizer.from_pretrained("google/mt5-base") | |
| input_string = "Akọni ajìjàgbara obìnrin tó sun àtìmalé torí owó orí" | |
| inputs = tokenizer.encode(input_string, return_tensors="pt") | |
| generated_tokens = model.generate(inputs) | |
| results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) | |
| print(results) | |
| ``` | |
| #### Limitations and bias | |
| This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. | |
| ## Training data | |
| This model was fine-tuned on on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset | |
| ## Training procedure | |
| This model was trained on a single NVIDIA V100 GPU | |
| ## Eval results on Test set (BLEU score) | |
| 15.57 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) | |
| ### BibTeX entry and citation info | |
| By David Adelani | |
| ``` | |
| ``` | |