readme: Hugging Face's logo
  ---
  language: yo
  datasets:
  - JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
  ---
  # mT5_base_yoruba_adr
  ## Model description
  **mT5_base_yoruba_adr** is a **automatic diacritics restoration** model for Yorùbá language based on a fine-tuned  mT5-base  model.  It achieves the **state-of-the-art performance** for adding the correct diacritics or tonal marks to Yorùbá texts.  
  
  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 ADR.
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  tokenizer = AutoTokenizer.from_pretrained("")
  model = AutoModelForTokenClassification.from_pretrained("")
  nlp = pipeline("", model=model, tokenizer=tokenizer)
  example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
  ner_results = nlp(example)
  print(ner_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)
  64.63 BLEU on [Global Voices test set](https://arxiv.org/abs/2003.10564)
  70.27 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)
  
  ### BibTeX entry and citation info
  By Jesujoba Alabi and David Adelani
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