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.
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.")
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.
This model was trained on a single NVIDIA V100 GPU
|Dataset||mBERT F1||wo_bert F1|
By David Adelani
Select AutoNLP in the “Train” menu to fine-tune this model automatically.
- Downloads last month