| Hugging Face's logo | |
| --- | |
| language: luo | |
| datasets: | |
| --- | |
| # xlm-roberta-base-finetuned-luo | |
| ## Model description | |
| **xlm-roberta-base-finetuned-luo** is a **Luo RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Luo 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 Luo 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-luo') | |
| >>> unmasker("Obila ma Changamwe <mask> pedho achije angwen mag njore") | |
| ``` | |
| #### 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 | |
| ## 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 | luo_roberta F1 | |
| -|-|- | |
| [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 74.86 | 75.27 | |
| ### BibTeX entry and citation info | |
| By David Adelani | |
| ``` | |
| ``` | |