---
license: mit
language:
- sw
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: v1
  results:
  - task:
      type: Offensive words classifier
      name: Text Classification
    metrics:
    - type: f1
      value: 0.9272349272349272
      name: F1 Score
      verified: false
    - type: precision
      value: 0.9550321199143469
      name: Precision
      verified: false
    - type: recall
      value: 0.901010101010101
      name: Recall
      verified: false
    - type: accuracy
      value: 0.9292214357937311
      name: Accuracy
      verified: false
datasets:
- metabloit/offensive-swahili-text
---

# swahBERT
This model was fine tuned using the dataset listed below.
It achieves the following results on the evaluation set:
- Loss: 0.4982
- Accuracy: 0.9292
- Precision: 0.9550
- Recall: 0.9010
- F1: 0.9272
## Model description
This is a fine tuned swahBERT model. You can get the original model from [here](https://github.com/gatimartin/SwahBERT "swahBERT Model")

## Training and evaluation data
The model was fine tuned using [this dataset](https://huggingface.co/datasets/metabloit/offensive-swahili-text "Swahili offensive/non-offensive dataset")

### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 310  | 0.6506          | 0.9282   | 0.9417    | 0.9131 | 0.9272 |
| 0.0189        | 2.0   | 620  | 0.4982          | 0.9292   | 0.9550    | 0.9010 | 0.9272 |
| 0.0189        | 3.0   | 930  | 0.5387          | 0.9323   | 0.9693    | 0.8929 | 0.9295 |
| 0.0314        | 4.0   | 1240 | 0.6365          | 0.9221   | 0.9524    | 0.8889 | 0.9195 |
| 0.0106        | 5.0   | 1550 | 0.6687          | 0.9282   | 0.9473    | 0.9071 | 0.9267 |
| 0.0106        | 6.0   | 1860 | 0.6671          | 0.9282   | 0.9454    | 0.9091 | 0.9269 |
| 0.0016        | 7.0   | 2170 | 0.6908          | 0.9242   | 0.9468    | 0.8990 | 0.9223 |
| 0.0016        | 8.0   | 2480 | 0.6832          | 0.9272   | 0.9471    | 0.9051 | 0.9256 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3

## References
@inproceedings{martin-etal-2022-swahbert,
    title = "{S}wah{BERT}: Language Model of {S}wahili",
    author = "Martin, Gati  and Mswahili, Medard Edmund  and Jeong, Young-Seob  and Woo, Jiyoung",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.23",
    pages = "303--313"
    }