| Hugging Face's logo | |
| --- | |
| 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. | |
| ```python | |
| >>> 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](https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus) + [BBC Gahuza](https://www.bbc.com/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](https://github.com/masakhane-io/masakhane-ner) | 72.20 | 77.57 | |
| ### BibTeX entry and citation info | |
| By David Adelani | |
| ``` | |
| ``` | |