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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"
} |