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language: lg datasets:

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bert-base-multilingual-cased-finetuned-luganda

Model description

bert-base-multilingual-cased-finetuned-luganda is a Luganda BERT model obtained by fine-tuning bert-base-multilingual-cased model on Luganda language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets.

Specifically, this model is a bert-base-multilingual-cased 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.

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-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 +Luganda 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	mBERT F1	lg_bert F1
MasakhaNER	80.36	84.70

BibTeX entry and citation info

By David Adelani