| Hugging Face's logo | |
| --- | |
| language: yo | |
| datasets: | |
| --- | |
| # 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 pipeline | |
| >>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-yoruba') | |
| >>> unmasker("Arẹmọ Phillip to jẹ ọkọ [MASK] Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun") | |
| [{'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Mary Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.1738305538892746, | |
| 'token': 12176, | |
| 'token_str': 'Mary'}, | |
| {'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Queen Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.16382873058319092, | |
| 'token': 13704, | |
| 'token_str': 'Queen'}, | |
| {'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ ti Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.13272495567798615, | |
| 'token': 14382, | |
| 'token_str': 'ti'}, | |
| {'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ King Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.12823280692100525, | |
| 'token': 11515, | |
| 'token_str': 'King'}, | |
| {'sequence': '[CLS] Arẹmọ Phillip to jẹ ọkọ Lady Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun [SEP]', 'score': 0.07841219753026962, | |
| 'token': 14005, | |
| 'token_str': 'Lady'}] | |
| ``` | |
| #### 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 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. | |
| ## 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 | yo_bert F1 | |
| -|-|- | |
| [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 78.97 | 82.58 | |
| [BBC Yorùbá Textclass](https://huggingface.co/datasets/yoruba_bbc_topics) | 75.13 | 79.11 | |
| ### BibTeX entry and citation info | |
| By David Adelani | |
| ``` | |
| ``` | |