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--- |
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language: |
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- swa |
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tags: |
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- NER |
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datasets: |
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- masakhaner |
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metrics: |
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- f1 |
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- precision |
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- recall |
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license: apache-2.0 |
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widget: |
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- text: "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa, watu takriban 14 zaidi wamepata maambukizi ya Covid-19." |
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--- |
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# Model description |
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**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: |
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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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# 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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# Training Data |
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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. |
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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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#### 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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# Evaluation Data |
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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. |
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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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# 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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# 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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# Results |
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Model Name| Precision | Recall | F1-score |
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-|-|-|- |
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**mbert-base-uncased-ner-swa**| 82.85 | 88.13 | 85.41 |
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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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tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-ner-swa") |
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model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-ner-swa") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa, watu takriban 14 zaidi wamepata maambukizi ya Covid-19." |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
