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language: lg datasets:
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
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