Qwen2 Vl 7b Rslora Offensive Meme Singapore

🚀 Qwen2-VL 7B RSLORA Offensive Meme Singapore

This model is a fine - tuned version of Qwen2 - VL - 7B - Instruct, designed for offensive meme classification in the Singapore context, leveraging the multimodal capabilities of the base model.

🚀 Quick Start

See the model repository's README for usage examples: https://github.com/aliencaocao/vlm-for-memes-aisg

✨ Features

• This model can classify memes as offensive or not, considering the Singaporean social context.
• It can be used directly for meme classification and can also be further fine - tuned for other related tasks or integrated into a larger content moderation system.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Model Details

Model Description

This model classifies memes as offensive or not, taking into account Singaporean social context. It leverages the visual and textual understanding capabilities of Qwen2 - VL - 7B - Instruct.

Model Sources

• Repository: https://github.com/aliencaocao/vlm-for-memes-aisg
• Paper: Detecting Offensive Memes with Social Biases in Singapore Context Using Multimodal Large Language Models

Uses

Direct Use

This model can be used directly to classify memes. See the code example in the "How to Get Started" section.

Downstream Use [optional]

This model can be further fine - tuned for other related tasks or incorporated into a larger content moderation system.

Out - of - Scope Use

This model is specifically trained for the Singaporean context and may not generalize well to other cultures or languages. It should not be used to make definitive judgments about individuals or groups.

Bias, Risks, and Limitations

Like any machine learning model, this model may exhibit biases present in the training data. It is important to be aware of these limitations and use the model responsibly. Further research is needed to assess and mitigate potential biases.

⚠️ Important Note

This model is specifically trained for the Singaporean context and may not generalize well to other cultures or languages. It should not be used to make definitive judgments about individuals or groups.

💡 Usage Tip

Users should be aware of the potential for bias and limitations in the model's performance. It is recommended to use this model as a tool to assist human moderators rather than a replacement for human judgment.

Training Details

Training Data

The model was trained on the multimodal_meme_classification_singapore dataset. This dataset contains memes labeled as offensive or not within the Singaporean context.

Training Procedure

More details about the training procedure can be found in the paper.

Evaluation

The model achieved an AUROC of 0.8192 and an accuracy of 0.8043 on a held - out test set. See the paper for more details on the evaluation methodology.

Citation

@misc{yuxuan2025detectingoffensivememessocial,
      title={Detecting Offensive Memes with Social Biases in Singapore Context Using Multimodal Large Language Models}, 
      author={Cao Yuxuan and Wu Jiayang and Alistair Cheong Liang Chuen and Bryan Shan Guanrong and Theodore Lee Chong Jen and Sherman Chann Zhi Shen},
      year={2025},
      eprint={2502.18101},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.18101}, 
}