swainsubhankar/STEMTOX-PaliGemma2-10B
Image-Text-to-Text • Updated • 9
Request access to ToxicTags
This dataset contains potentially toxic and offensive memes. It is released for non-commercial academic research only.
By requesting access you agree to: - use the dataset only for academic research - not redistribute the raw data - cite the STEMTOX paper if you use this dataset
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STEMTOX: From Collaborative Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning
🎉 Accepted at Transactions of the Association for Computational Linguistics (TACL 2026).
Figure 1. Overview of the STEMTOX framework.
🔎 TL;DR
TOXICTAGS is a large-scale real-world meme dataset comprising 6,300 manually annotated memes collected from publicly available online sources.
Unlike existing datasets, ToxicTags incorporates collaboratively generated social tags associated with the original posts, providing valuable contextual information that complements the visual and textual content.
The dataset is annotated using a two-stage human annotation pipeline. First, each meme is classified as Toxic or Normal. Toxic memes are then further categorized into one of three fine-grained classes: Hateful, Dangerous, or Offensive, resulting in a four-class taxonomy (Normal, Offensive, Dangerous, Hateful).
The dataset is designed to support research on fine-grained toxic meme detection, multimodal content moderation, and vision-language models.
⚠️ Warning: Contains potentially toxic contents
🧮 Dataset Structure
images/
train.csv
test.csv
📋 Fields
| Column | Description |
|---|---|
img |
Relative path to the meme image |
title |
Original meme title |
tags |
Collaborative tags |
ocr |
OCR-extracted text |
binary |
Binary toxicity label |
finegrained |
Fine-grained toxicity label |
🤗 Loading the Dataset
The dataset can be loaded directly from the Hugging Face Hub using the 🤗 Datasets library.
⚙️ Installation
pip install datasets
📦 Load the Dataset
from datasets import load_dataset
dataset = load_dataset("swainsubhankar/ToxicTags")
The dataset consists of two predefined splits:
print(dataset)
Output:
DatasetDict({
train: Dataset({
features: ['img', 'title', 'tags', 'ocr', 'binary', 'finegrained'],
num_rows: 5300
})
test: Dataset({
features: ['img', 'title', 'tags', 'ocr', 'binary', 'finegrained'],
num_rows: 1000
})
})
📑 Access Individual Splits
train = dataset["train"]
test = dataset["test"]
Citation
@misc{swain2026stemtoxsocialtagsfinegrained,
title={STEMTOX: From Social Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning},
author={Subhankar Swain and Naquee Rizwan and Vishwa Gangadhar S and Nayandeep Deb and Animesh Mukherjee},
year={2026},
eprint={2508.04166},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.04166}
}
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