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bert-base-multilingual-cased-finetuned-swahili
Model description
bert-base-multilingual-cased-finetuned-swahili is a Swahili BERT model obtained by fine-tuning bert-base-multilingual-cased model on Swahili language texts. It provides better performance than the multilingual BERT on text classification and named entity recognition datasets.
Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on Swahili corpus.
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-swahili')
>>> unmasker("Jumatatu, Bwana Kagame alielezea shirika la France24 huko [MASK] kwamba "hakuna uhalifu ulitendwa")
[{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Paris kwamba hakuna uhalifu ulitendwa',
'score': 0.31642526388168335,
'token': 10728,
'token_str': 'Paris'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Rwanda kwamba hakuna uhalifu ulitendwa',
'score': 0.15753623843193054,
'token': 57557,
'token_str': 'Rwanda'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Burundi kwamba hakuna uhalifu ulitendwa',
'score': 0.07211585342884064,
'token': 57824,
'token_str': 'Burundi'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko France kwamba hakuna uhalifu ulitendwa',
'score': 0.029844321310520172,
'token': 10688,
'token_str': 'France'},
{'sequence': 'Jumatatu, Bwana Kagame alielezea shirika la France24 huko Senegal kwamba hakuna uhalifu ulitendwa',
'score': 0.0265930388122797,
'token': 38052,
'token_str': 'Senegal'}]
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 Swahili 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 | sw_bert F1 |
---|---|---|
MasakhaNER | 86.80 | 89.36 |
BibTeX entry and citation info
By David Adelani
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