---
language: en
tags:
- text-classification
- abusive-language
- hate-speech
- toxicity
- cyberviolence
- abusive-language-detection
- BERT
license: mit
---

# AbuseBERT

## Model Description

**AbuseBERT** is a **BERT-based classification model** fine-tuned for **abusive language detection**, optimized for **cross-dataset generalization**.

> Abusive language detection models often suffer from poor generalization due to **sampling and lexical biases** in individual datasets. Our approach addresses this by integrating **ten publicly available abusive language datasets**, harmonizing labels and preprocessing textual samples to create a **broader and more representative training distribution**.

**Key Findings:**
- Individual dataset models: average F1 = **0.60**  
- Integrated model: F1 = **0.84**  
- Dataset contribution to performance improvements correlates with **lexical diversity (0.71 correlation)**  
- Integration exposes models to diverse abuse patterns, enhancing **real-world generalization**

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## Conclusion / Takeaways

- No single dataset captures the full spectrum of abusive language; each dataset reflects a **limited slice** of the problem space.  
- Systematically integrating ten heterogeneous datasets significantly improves classification performance on a **held-out benchmark**.  
- Lexically dissimilar datasets contribute more to **enhancing generalization**.  
- The integrated model demonstrates superior **cross-dataset performance** compared to models trained on individual datasets.

---

## Paper Reference

Samaneh Hosseini Moghaddam, Kelly Lyons, Frank Rudzicz, Cheryl Regehr, Vivek Goel, Kaitlyn Regehr,  
“**Enhancing machine learning in abusive language detection with dataset aggregation**,” in *Proc. 35th IEEE Int. Conf. Collaborative Advances in Software Computing (CASC)*, 2025.

---

## Intended Use

**Recommended:**
- Detecting abusive language in text from social media or online platforms
- Research on bias mitigation and cross-dataset generalization
- Supporting safe and inclusive online environments

**Not Recommended:**
- Fully automated moderation without human oversight
- High-stakes legal or policy decisions

---

## Usage Example

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Samanehmoghaddam/AbuseBERT")
model = AutoModelForSequenceClassification.from_pretrained("Samanehmoghaddam/AbuseBERT")

# Sample input
text = "Your example text here."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

# Predicted label
predicted_label = torch.argmax(outputs.logits, dim=1).item()
print(f"Predicted label: {predicted_label}")