readme: 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
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