| Hugging Face's logo | |
| --- | |
| language: ha | |
| datasets: | |
| --- | |
| # 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. | |
| ```python | |
| >>> 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](http://data.statmt.org/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](https://github.com/masakhane-io/masakhane-ner) | 86.80 | 89.36 | |
| ### BibTeX entry and citation info | |
| By David Adelani | |
| ``` | |
| ``` | |