readme: Hugging Face's logo
  ---
  language: ha
  datasets:
  
  ---
  # bert-base-multilingual-cased-finetuned-hausa
  ## Model description
  **bert-base-multilingual-cased-finetuned-hausa** is a **Hausa BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Hausa 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 Hausa 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-hausa')
  >>> unmasker("Shugaban [MASK] Muhammadu Buhari ya amince da shawarar da ma’aikatar sufuri karkashin jagoranci")
                      
  [{'sequence': 
  '[CLS] Shugaban Nigeria Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 
  'score': 0.9762618541717529, 
  'token': 22045, 
  'token_str': 'Nigeria'}, 
  {'sequence': '[CLS] Shugaban Ka Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.007239189930260181, 
  'token': 25444, 
  'token_str': 'Ka'}, 
  {'sequence': '[CLS] Shugaban, Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001990817254409194, 
  'token': 117, 
  'token_str': ','}, 
  {'sequence': '[CLS] Shugaban Ghana Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.001566368737258017, 
  'token': 28682, 
  'token_str': 'Ghana'}, 
  {'sequence': '[CLS] Shugabanmu Muhammadu Buhari ya amince da shawarar da ma [UNK] aikatar sufuri karkashin jagoranci [SEP]', 'score': 0.0009375187801197171, 
  'token': 11717, 
  'token_str': '##mu'}]
  
  ```
  #### 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 [Hausa 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 | ha_bert F1
  -|-|-
  [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 86.65 | 91.31
  [VOA Hausa Textclass](https://huggingface.co/datasets/hausa_voa_topics) | 84.76 | 90.98 
  
  ### BibTeX entry and citation info
  By David Adelani
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  ```
  
  
  