readme: Hugging Face's logo
  ---
  language: rw
  datasets:
  
  ---
  # bert-base-multilingual-cased-finetuned-kinyarwanda
  ## Model description
  **bert-base-multilingual-cased-finetuned-kinyarwanda** is a **Kinyarwanda BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Kinyarwanda language texts.  It provides **better performance** than the multilingual BERT on named entity recognition datasets.  
  
  Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Kinyarwanda corpus. 
  ## Intended uses & limitations
  #### How to use
  You can use this model with Transformers *pipeline* for masked token prediction.
  ```python
  >>> from transformers import pipeline
  >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-kinyarwanda')
  >>> unmasker("Twabonye ko igihe mu [MASK] hazaba hari ikirango abantu bakunze")
  
  ```
  #### 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 JW300 + [KIRNEWS](https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus) + [BBC Gahuza](https://www.bbc.com/gahuza)
  
  ## Training procedure
  This model was trained on a single NVIDIA V100 GPU
  
  ## Eval results on Test set (F-score, average over 5 runs)
  Dataset| mBERT F1 | rw_bert F1
  -|-|-
  [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 72.20 | 77.57
  
  ### BibTeX entry and citation info
  By David Adelani
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