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language: yo datasets:

Bible, JW300, Menyo-20k, Yoruba Embedding corpus and CC-Aligned, Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.

bert-base-multilingual-cased-finetuned-yoruba

Model description

bert-base-multilingual-cased-finetuned-yoruba is a Yoruba BERT model obtained by fine-tuning bert-base-multilingual-cased model on Yorùbá 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 Yorùbá corpus.

Intended uses & limitations

How to use

You can use this model with Transformers pipeline for masked token prediction.

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("")
model = AutoModelForTokenClassification.from_pretrained("")
nlp = pipeline("", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_results)

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 on JW300 Yorùbá corpus and Menyo-20k dataset

Training procedure

This model was trained on a single NVIDIA V100 GPU

Eval results on Test set (F-score)

Dataset	F1-score

Yoruba GV NER |86.26 MasakhaNER |75.76 BBC Yoruba |91.75

BibTeX entry and citation info

By David Adelani