toXibert

ToXiBERT: Hate Speech Detection Model

Model Description

ToXiBERT is a fine-tuned RoBERTa-based model specifically designed to detect and classify hate speech, offensive language, and normal content on X (formerly Twitter). The model is built on top of BERTweet and provides three-class classification capabilities.

Model Details

Model Type

• Base Model: BERTweet (RoBERTa architecture)
• Language: English

Intended Use

• Primary Use: Hate speech detection and content moderation
• Intended Users: Researchers, content moderators, social media platforms

Training Details

Training Data

• Source: Custom compiled dataset from X (Twitter) platform

Training Procedure

• Training Infrastructure: Google Colab
• Hyperparameter Tuning: Performed with systematic optimization
• Fine-tuning Strategy: Multi-class classification on pre-trained BERTweet

Classes

The model classifies text into three categories:

1. Hate Speech: Content containing hateful language targeting individuals or groups
2. Offensive Content: Language that is offensive but doesn't constitute hate speech
3. Normal Content: Non-offensive, regular social media content

Performance

Evaluation Results

The model achieves the following performance with optimized class-specific thresholds:

Class-Specific Thresholds

• Hate Speech: 0.560
• Offensive: 0.420
• Normal: 0.320

Performance Metrics

Usage

Loading the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "your-username/toXibert"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

### Recommended Thresholds
For optimal performance, use the following class-specific thresholds:
```python
thresholds = {
    "hate_speech": 0.560,
    "offensive": 0.420,
    "normal": 0.320
}

Limitations and Bias

Limitations

• Context-dependent hate speech may be challenging to detect
• Performance may degrade on content significantly different from training data
• Requires careful threshold tuning for different use cases

Bias Considerations

• Training data sourced from X platform may not represent all demographics equally
• Model may reflect biases present in the training data
• Regular evaluation for fairness across different groups is recommended

Ethical Considerations

• Decisions based on model predictions should include human oversight
• Regular auditing for bias and fairness is essential
• Consider the impact of false positives and false negatives in your specific use case