readme: Hugging Face's logo
  ---
  language: lg
  datasets:
  
  ---
  # xlm-roberta-base-finetuned-luganda
  ## Model description
  **xlm-roberta-base-finetuned-luganda** is a **Luganda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Luganda language texts.  It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.  
  
  Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Luganda 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/xlm-roberta-base-finetuned-luganda')
  >>> unmasker("Ffe tulwanyisa abo abaagala okutabangula <mask>, Kimuli bwe yategeezezza.")
  
  
  
  ```
  #### 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 + [BUKKEDDE](https://github.com/masakhane-io/masakhane-ner/tree/main/text_by_language/luganda) +[Luganda CC-100](http://data.statmt.org/cc-100/)
  
  ## Training procedure
  This model was trained on a single NVIDIA V100 GPU
  
  ## Eval results on Test set (F-score, average over 5 runs)
  Dataset| XLM-R F1 | lg_roberta F1
  -|-|-
  [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 79.69 | 84.70
  
  ### BibTeX entry and citation info
  By David Adelani
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