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---
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 **publicly available abusive language datasets**, harmonizing labels and preprocessing textual samples to create a **broader and more representative training distribution**.
**Key Findings using 10 datasets:**
- 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**
---
## 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, offensive, or toxic language in text from social media, online forums, or messaging platforms.
- Supporting research on online harassment, cyber violence, and hate speech analysis.
- Assisting human moderators in content review or flagging potentially harmful content.
- Evaluating trends, prevalence, or patterns of abusive language in large-scale textual datasets.
**Not Recommended:**
- Fully automated moderation without human oversight
- High-stakes legal or policy decisions
---
## Usage Example
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
# Load the model
model_name = "Samanehmoghaddam/AbuseBERT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Create a pipeline for text classification
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
# Example texts to classify
texts = [
"@user You are amazing!",
"@user You are stupid!",
]
# Run the classifier
results = classifier(texts)
# Print results
for text, result in zip(texts, results):
print(f"Text: {text}")
print(f"Prediction: {result}")
print("-" * 40) |