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--- |
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license: mit |
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language: |
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- sw |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: v1 |
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results: |
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- task: |
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type: Offensive words classifier |
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name: Text Classification |
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metrics: |
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- type: f1 |
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value: 0.9272349272349272 |
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name: F1 Score |
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verified: false |
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- type: precision |
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value: 0.9550321199143469 |
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name: Precision |
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verified: false |
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- type: recall |
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value: 0.901010101010101 |
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name: Recall |
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verified: false |
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- type: accuracy |
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value: 0.9292214357937311 |
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name: Accuracy |
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verified: false |
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datasets: |
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- metabloit/offensive-swahili-text |
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--- |
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# swahBERT |
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This model was fine tuned using the dataset listed below. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4982 |
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- Accuracy: 0.9292 |
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- Precision: 0.9550 |
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- Recall: 0.9010 |
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- F1: 0.9272 |
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## Model description |
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This is a fine tuned swahBERT model. You can get the original model from [here](https://github.com/gatimartin/SwahBERT "swahBERT Model") |
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## Training and evaluation data |
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The model was fine tuned using [this dataset](https://huggingface.co/datasets/metabloit/offensive-swahili-text "Swahili offensive/non-offensive dataset") |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| No log | 1.0 | 310 | 0.6506 | 0.9282 | 0.9417 | 0.9131 | 0.9272 | |
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| 0.0189 | 2.0 | 620 | 0.4982 | 0.9292 | 0.9550 | 0.9010 | 0.9272 | |
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| 0.0189 | 3.0 | 930 | 0.5387 | 0.9323 | 0.9693 | 0.8929 | 0.9295 | |
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| 0.0314 | 4.0 | 1240 | 0.6365 | 0.9221 | 0.9524 | 0.8889 | 0.9195 | |
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| 0.0106 | 5.0 | 1550 | 0.6687 | 0.9282 | 0.9473 | 0.9071 | 0.9267 | |
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| 0.0106 | 6.0 | 1860 | 0.6671 | 0.9282 | 0.9454 | 0.9091 | 0.9269 | |
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| 0.0016 | 7.0 | 2170 | 0.6908 | 0.9242 | 0.9468 | 0.8990 | 0.9223 | |
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| 0.0016 | 8.0 | 2480 | 0.6832 | 0.9272 | 0.9471 | 0.9051 | 0.9256 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cpu |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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## References |
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@inproceedings{martin-etal-2022-swahbert, |
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title = "{S}wah{BERT}: Language Model of {S}wahili", |
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author = "Martin, Gati and Mswahili, Medard Edmund and Jeong, Young-Seob and Woo, Jiyoung", |
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booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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month = jul, |
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year = "2022", |
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address = "Seattle, United States", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2022.naacl-main.23", |
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pages = "303--313" |
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} |
