| Hugging Face's logo | |
| --- | |
| language: ig | |
| datasets: | |
| --- | |
| # xlm-roberta-base-finetuned-igbo | |
| ## Model description | |
| **xlm-roberta-base-finetuned-igbo** is a **Igbo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Hausa language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. | |
| Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Igbo 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/xlm-roberta-base-finetuned-igbo') | |
| >>> unmasker("Reno Omokri na Gọọmentị <mask> enweghị ihe ha ga-eji hiwe ya bụ mmachi.") | |
| ``` | |
| #### 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 JW300 + OPUS CC-Align + [IGBO NLP Corpus](https://github.com/IgnatiusEzeani/IGBONLP) +[Igbo CC-100](http://data.statmt.org/cc-100/) | |
| ## Training procedure | |
| This model was trained on a single NVIDIA V100 GPU | |
| ## Eval results on Test set (F-score, average over 5 runs) | |
| Dataset| XLM-R F1 | ig_roberta F1 | |
| -|-|- | |
| [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 84.51 | 88.76 | |
| ### BibTeX entry and citation info | |
| By David Adelani | |
| ``` | |
| ``` | |