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 ``` ```