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---
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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