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---
license: cc-by-4.0
language: ti
widget:
- text: "<text-to-classify>"
datasets:
- fgaim/tigrinya-abusive-language-detection
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: tiroberta-tiald-all-tasks
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8666502037288554
- name: Precision
type: precision
value: 0.8668478260869565
- name: Recall
type: recall
value: 0.8666666666666667
---
# TiRoBERTa Fine-tuned for Tigrinya Abusive Language Detection
This model is a fine-tuned version of [TiRoBERTa](https://huggingface.co/fgaim/tiroberta-base) on the [TiALD](https://huggingface.co/datasets/fgaim/tigrinya-abusive-language-detection) dataset.
**Tigrinya Abusive Language Detection (TiALD) Dataset** is a large-scale, multi-task benchmark dataset for abusive language detection in the Tigrinya language. It consists of **13,717 YouTube comments** annotated for **abusiveness**, **sentiment**, and **topic** tasks. The dataset includes comments written in both the **Ge’ez script** and prevalent non-standard Latin **transliterations** to mirror real-world usage.
> ⚠️ The dataset contains explicit, obscene, and potentially hateful language. It should be used for research purposes only. ⚠️
This work accompanies the paper ["A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings"](https://arxiv.org/abs/2505.12116).
## Model Usage
```python
from transformers import pipeline
tiald_pipe = pipeline("text-classification", model="fgaim/tiroberta-abusiveness-detection")
tiald_pipe("<text-to-classify>")
```
## Performance Metrics
This model achieves the following results on the evaluation set:
```json
"abusiveness_metrics": {
"accuracy": 0.8666666666666667,
"macro_f1": 0.8666502037288554,
"macro_precision": 0.8668478260869565,
"macro_recall": 0.8666666666666667,
"weighted_f1": 0.8666502037288554,
"weighted_precision": 0.8668478260869565,
"weighted_recall": 0.8666666666666667
}
```
## Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- optimizer: Adam (betas=0.9, 0.999, epsilon=1e-08)
- lr_scheduler_type: linear
- num_epochs: 4.0
- seed: 42
## Intended Usage
The TiALD dataset and models designed to support:
- Research in abusive language detection in low-resource languages
- Context-aware abuse, sentiment, and topic modeling
- Multi-task and transfer learning with digraphic scripts
- Evaluation of multilingual and fine-tuned language models
Researchers and developers should avoid using this dataset for direct moderation or enforcement tasks without human oversight.
## Ethical Considerations
- **Sensitive content**: Contains toxic and offensive language. Use for research purposes only.
- **Cultural sensitivity**: Abuse is context-dependent; annotations were made by native speakers to account for cultural nuance.
- **Bias mitigation**: Data sampling and annotation were carefully designed to minimize reinforcement of stereotypes.
- **Privacy**: All the source content for the dataset is publicly available on YouTube.
- **Respect for expression**: The dataset should not be used for automated censorship without human review.
This research received IRB approval (Ref: KH2022-133) and followed ethical data collection and annotation practices, including informed consent of annotators.
## Citation
If you use this model or the `TiALD` dataset in your work, please cite:
```bibtex
@misc{gaim-etal-2025-tiald-benchmark,
title = {A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings},
author = {Fitsum Gaim and Hoyun Song and Huije Lee and Changgeon Ko and Eui Jun Hwang and Jong C. Park},
year = {2025},
eprint = {2505.12116},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2505.12116}
}
```
## License
This dataset is released under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
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