| Hugging Face's logo | |
| --- | |
| language: wo | |
| datasets: | |
| --- | |
| # bert-base-multilingual-cased-finetuned-wolof | |
| ## Model description | |
| **bert-base-multilingual-cased-finetuned-wolof** is a **Wolof BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Wolof 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 Wolof 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-wolof') | |
| >>> unmasker("Màkki Sàll feeñal na ay xalaatam ci mbir yu am solo yu soxal [MASK] ak Afrik.") | |
| ``` | |
| #### 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 [Bible OT](http://biblewolof.com/) + [OPUS](https://opus.nlpl.eu/) + News Corpora (Lu Defu Waxu, Saabal, and Wolof Online) | |
| ## 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 | wo_bert F1 | |
| -|-|- | |
| [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 64.52 | 69.43 | |
| ### BibTeX entry and citation info | |
| By David Adelani | |
| ``` | |
| ``` | |