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language: am datasets:
bert-base-multilingual-cased-finetuned-amharic
Model description
bert-base-multilingual-cased-finetuned-amharic is a Amharic BERT model obtained by replacing mBERT vocabulary by amharic vocabulary because the language was not supported, and fine-tuning bert-base-multilingual-cased model on Amharic language texts. It provides better performance than the multilingual Amharic on named entity recognition datasets.
Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on Amharic corpus using Amharic vocabulary.
Intended uses & limitations
How to use
You can use this model with Transformers pipeline for masked token prediction.
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-amharic')
>>> unmasker("α¨α ααͺα« α¨α ααͺα« ααα΅ αα© ααααα°α αααͺ ααα΅αα α α α«α΅ α αα«α΅ α¨αα«α°αα΅α [MASK] αααα«αΈαα α¨α ααͺα« α¨ααͺ αα³α ααα΅α΄α α α΅α³ααα’")
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 Amharic 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 | mBERT F1 | am_bert F1 |
|---|---|---|
| MasakhaNER | 0.0 | 60.89 |
BibTeX entry and citation info
By David Adelani
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