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  - text: "Ambasaderi Bellomo yavuze ko bishimira ubufatanye burambye hagati ya EU n’u Rwanda, bushingiye nanone ku bufatanye hagati y’imigabane ya Afurika n’u Burayi."
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- Model description
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - text: "Ambasaderi Bellomo yavuze ko bishimira ubufatanye burambye hagati ya EU n’u Rwanda, bushingiye nanone ku bufatanye hagati y’imigabane ya Afurika n’u Burayi."
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+ # Model description
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+ **mbert-base-uncased-kin** is a model based on the fine-tuned multilingual BERT base uncased model. It has been trained to recognize four types of entities:
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+ - dates & time (DATE)
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+ - Location (LOC)
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+ - Organizations (ORG)
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+ - Person (PER)
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+
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+ # Intended Use
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+ - Intended to be used for research purposes concerning Named Entity Recognition for African Languages.
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+ - Not intended for practical purposes.
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+
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+ # Training Data
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+ This model was fine-tuned on the Kinyarwanda corpus **(kin)** 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.
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+
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+ # Training procedure
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+ This model was trained on a single NVIDIA P5000 from [Paperspace](https://www.paperspace.com)
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+
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+ #### Hyperparameters
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+ - **Learning Rate:** 5e-5
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+ - **Batch Size:** 32
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+ - **Maximum Sequence Length:** 164
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+ - **Epochs:** 30
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+
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+ # Evaluation Data
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+ We evaluated this model on the test split of the Kinyarwandan corpus **(kin)** present in the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) with no thresholding.
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+
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+ # Metrics
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+ - Precision
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+ - Recall
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+ - F1-score
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+
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+ # Limitations
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+ - The size of the pre-trained language model prevents its usage in anything other than research.
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+ - Lack of analysis concerning the bias and fairness in these models may make them dangerous if deployed into production system.
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+ - 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.
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+
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+ # Caveats and Recommendations
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+ - The topics in the dataset corpus are centered around **News**. Future training could be done with a more diverse corpus.
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+
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+ # Results
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+ Model Name| Precision | Recall | F1-score
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+ -|-|-|-
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+ **mbert-base-uncased-kin**| 81.35 | 83.98 | 82.64
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+
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+ # Usage
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForTokenClassification
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+ from transformers import pipeline
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+
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+ tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-kin")
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+ model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-kin")
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+
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+ nlp = pipeline("ner", model=model, tokenizer=tokenizer)
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+ example = "Rayon Sports yasinyishije rutahizamu w’Umurundi"
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+
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+ ner_results = nlp(example)
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+ print(ner_results)
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+ ```