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README.md
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language:
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- en
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- yo
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- ig
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- ha
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- pcm
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---
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language:
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- en
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- yo
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- ha
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- ig
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- pcm
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---
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# naija-bert-base
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NaijaBERT was created by pre-training a [BERT model with token dropping](https://aclanthology.org/2022.acl-long.262/) on five Nigerian languages (English, Hausa, Igbo, Naija, and Yoruba) texts for about 100K steps.
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It was trained using BERT-base architecture with [Tensorflow Model Garden](https://github.com/tensorflow/models/tree/master/official/projects)
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### Pre-training corpus
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A mix of WURA, Wikipedia and MT560 data
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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/naija-bert-large')
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>>> unmasker("Ọjọ kẹsan-an, [MASK] Kẹjọ ni wọn ri oku Baba")
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```
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```
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[{'score': 0.9981744289398193, 'token': 3785, 'token_str': 'osu', 'sequence': 'ojo kesan - an, osu kejo ni won ri oku baba'}, {'score': 0.0015279919607564807, 'token': 3355, 'token_str': 'ojo', 'sequence': 'ojo kesan - an, ojo kejo ni won ri oku baba'}, {'score': 0.0001734074903652072, 'token': 11780, 'token_str': 'osun', 'sequence': 'ojo kesan - an, osun kejo ni won ri oku baba'}, {'score': 9.066923666978255e-05, 'token': 21579, 'token_str': 'oṣu', 'sequence': 'ojo kesan - an, oṣu kejo ni won ri oku baba'}, {'score': 1.816015355871059e-05, 'token': 3387, 'token_str': 'odun', 'sequence': 'ojo kesan - an, odun kejo ni won ri oku baba'}]
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```
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### Acknowledgment
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We thank [@stefan-it](https://github.com/stefan-it) for providing the pre-processing and pre-training scripts. Finally, we would like to thank Google Cloud for providing us access to TPU v3-8 through the free cloud credits. Model trained using flax, before converted to pytorch.
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### BibTeX entry and citation info.
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```
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@misc{david_adelani_2025,
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author = { David Adelani },
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title = { naija-bert-base (Revision 22c83d8) },
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year = 2025,
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url = { https://huggingface.co/Davlan/naija-bert-base },
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doi = { 10.57967/hf/5864 },
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publisher = { Hugging Face }
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}
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```
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