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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.
>>> 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 + BBC 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 | 72.20 | 77.57 |
BibTeX entry and citation info
By David Adelani