| Hugging Face's logo | |
| --- | |
| 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 | |
| ``` | |
| ``` | |