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Hugging Face's logo |
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language: pcm |
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datasets: |
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--- |
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# xlm-roberta-base-finetuned-naija |
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## Model description |
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**xlm-roberta-base-finetuned-naija** is a **Nigerian Pidgin RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Nigerian Pidgin language texts. It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets. |
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Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Nigerian Pidgin corpus. |
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## Intended uses & limitations |
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#### How to use |
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You can use this model with Transformers *pipeline* for masked token prediction. |
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```python |
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>>> from transformers import pipeline |
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>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-naija') |
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>>> unmasker("Another attack on ambulance happen for Koforidua in March <mask> year where robbers kill Ambulance driver") |
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``` |
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#### Limitations and bias |
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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. |
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## Training data |
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This model was fine-tuned on JW300 + [BBC Pidgin](https://www.bbc.com/pidgin) |
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## Training procedure |
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This model was trained on a single NVIDIA V100 GPU |
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## Eval results on Test set (F-score, average over 5 runs) |
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Dataset| XLM-R F1 | pcm_roberta F1 |
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[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.26 | 90.00 |
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### BibTeX entry and citation info |
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By David Adelani |
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