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Model description

bert-base-multilingual-cased-finetuned-igbo is a Igbo BERT model obtained by fine-tuning bert-base-multilingual-cased model on Igbo language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets.

Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on Igbo 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-igbo')
>>> unmasker("Reno Omokri na Gọọmentị [MASK] enweghị ihe ha ga-eji hiwe ya bụ mmachi.")

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 + OPUS CC-Align + IGBO NLP Corpus +Igbo CC-100

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 ig_bert F1
MasakhaNER 85.11 86.75

BibTeX entry and citation info

By David Adelani

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Mask token: [MASK]
This model can be loaded on the Inference API on-demand.