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README.md
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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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#### 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
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## Training procedure
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This model was trained on a single NVIDIA V100 GPU
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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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>>> from transformers import pipeline
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>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-yoruba')
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>>> unmasker("Arẹmọ Phillip to jẹ ọkọ [MASK] Elizabeth to ti wa lori aisan ti dagbere faye lẹni ọdun mọkandilọgọrun")
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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 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.
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## Training procedure
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This model was trained on a single NVIDIA V100 GPU
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