readme: ---
  language: 
  - swa
  tags:
  - NER
  datasets:
  - masakhaner
  metrics:
  - f1
  - precision
  - recall
  license: apache-2.0
  widget:
  - text: "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa, watu takriban 14 zaidi wamepata maambukizi ya Covid-19."
  ---
  
  # Model description
  **mbert-base-uncased-ner-swa** is a model based on the fine-tuned Multilingual BERT base uncased model, previously fine-tuned for Named Entity Recognition using 10 high-resourced languages. It has been trained to recognize four types of entities:
  
  - dates & time (DATE)
  - Location (LOC)
  - Organizations (ORG)
  - Person (PER)
  
  # Intended Use
  - Intended to be used for research purposes concerning Named Entity Recognition for African Languages.
  - Not intended for practical purposes.
  
  # Training Data
  This model was fine-tuned on the Swahili corpus **(swa)** of the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. However, we thresholded the number of entity groups per sentence in this dataset to 10 entity groups.
  
  # Training procedure
  This model was trained on a single NVIDIA P5000 from [Paperspace](https://www.paperspace.com)
  #### Hyperparameters
  - **Learning Rate:** 5e-5
  - **Batch Size:** 32
  - **Maximum Sequence Length:** 164
  - **Epochs:** 30
  
  
  # Evaluation Data
  We evaluated this model on the test split of the Swahili corpus **(swa)** present in the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) with no thresholding.
  
  # Metrics
  - Precision
  - Recall
  - F1-score
  
  # Limitations
  - The size of the pre-trained language model prevents its usage in anything other than research.
  - Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system.
  - The train data is a less populated version of the original dataset in terms of entity groups per sentence. Therefore, this can negatively impact the performance.
  
  # Caveats and Recommendations
  - The topics in the dataset corpus are centered around **News**. Future training could be done with a more diverse corpus.
  
  # Results
  Model Name| Precision | Recall | F1-score
  -|-|-|-
  **mbert-base-uncased-ner-swa**| 82.85 | 88.13 | 85.41
  
  # Usage
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-ner-swa")
  model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-ner-swa")
  
  nlp = pipeline("ner", model=model, tokenizer=tokenizer)
  example = "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa, watu takriban 14 zaidi wamepata maambukizi ya Covid-19."
  
  ner_results = nlp(example)
  print(ner_results)
  ```