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---
language: yo
datasets:
- Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus](https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), 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.
```python
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](https://huggingface.co/datasets/menyo20k_mt) 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
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