Davlan / mT5_base_yoruba_adr

Hugging Face's logo

language: yo datasets: - JW300 + Menyo-20k

mT5_base_yoruba_adr

Model description

mT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned mT5-base model. It achieves the state-of-the-art performance for adding the correct diacritics or tonal marks to Yorùbá texts.

Specifically, this model is a mT5_base model that was fine-tuned on JW300 Yorùbá corpus and Menyo-20k

Intended uses & limitations

How to use

You can use this model with Transformers pipeline for ADR.

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 (BLEU score)

64.63 BLEU on Global Voices test set 70.27 BLEU on Menyo-20k test set

BibTeX entry and citation info

By Jesujoba Alabi and David Adelani