---
language:
- id
license: mit
library_name: transformers
base_model:
- microsoft/deberta-v3-xsmall
pipeline_tag: text-classification
---

# Anti-Judol DeBERTa: Online Gambling Text Classification Model

## Model Description

This model is a fine-tuned version of [`microsoft/deberta-v3-xsmall`](https://huggingface.co/microsoft/deberta-v3-xsmall) specifically designed for detecting and classifying online gambling-related content in Indonesian text. The model helps identify potentially harmful gambling content to support digital safety initiatives and content moderation efforts. It can distinguish between gambling-related and non-gambling text with high accuracy, making it valuable for automated content filtering systems.

## Intended Uses

- Content moderation for social media platforms
- Automated detection of gambling advertisements
- Educational content filtering
- Digital safety applications
- Research on harmful content detection

## Data

The model was trained on a curated dataset of Indonesian text samples containing both gambling-related and non-gambling content. The dataset includes various types of gambling content such as online casino advertisements, sports betting promotions, lottery schemes, and gambling-related discussions, balanced with legitimate content from news, social media, and educational sources.

## Training

| Step | Training Loss | Validation Loss | F1       |
| ---- | ------------- | --------------- | -------- |
| 50   | 0.676000      | 0.598374        | 0.806867 |
| 100  | 0.439700      | 0.275495        | 0.911765 |
| 150  | 0.240300      | 0.161314        | 0.950226 |
| 200  | 0.135600      | 0.130217        | 0.966767 |
| 250  | 0.120000      | 0.115214        | 0.971168 |
| 300  | 0.098600      | 0.114574        | 0.975758 |
| 350  | 0.071900      | 0.111686        | 0.978788 |


## Ethical Considerations

This model is developed to support digital safety and harm reduction efforts. It should be used responsibly and in compliance with laws and regulations.