| --- |
| language: |
| - kin |
| tags: |
| - NER |
| datasets: |
| - masakhaner |
| metrics: |
| - f1 |
| - precision |
| - recall |
| license: apache-2.0 |
| widget: |
| - 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." |
| --- |
| |
| # Model description |
| **mbert-base-uncased-ner-kin** 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 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. |
|
|
| # 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 Kinyarwandan corpus **(kin)** 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-kin**| 81.95 |81.55 |81.75 |
|
|
| # Usage |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForTokenClassification |
| from transformers import pipeline |
| |
| tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-ner-kin") |
| model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-ner-kin") |
| |
| nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
| example = "Rayon Sports yasinyishije rutahizamu w’Umurundi" |
| |
| ner_results = nlp(example) |
| print(ner_results) |
| ``` |
