readme: Hugging Face's logo
  ---
  language: 
  - yo
  - en
  datasets:
  - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
  ---
  # mT5_base_eng_yor_mt
  ## Model description
  **mT5_base_yor_eng_mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned  mT5-base  model.  It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.  
  
  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_eng_yor_mt")
  tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
  input_string = "Where are you?"
  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 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)
  9.82 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)
  
  ### BibTeX entry and citation info
  By David Adelani
  ```
  
  ```
  
  
  