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.

>>> 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 + OPUS + 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 64.52 69.43

BibTeX entry and citation info

By David Adelani


Select AutoNLP in the “Train” menu to fine-tune this model automatically.

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