This model is a fine-tuned DistilRoBERTa-base model for binary classification of social media text to distinguish between human-authored and bot-generated content. The model uses class-weighted training to handle dataset imbalance and has been validated using 5-fold cross-validation.

Performance

Cross-Validation Results (5-Fold)

Metric	Mean ± Std	Range
Accuracy	0.9433 ± 0.0052	0.9385 - 0.9497
F1-Score (Weighted)	0.9434 ± 0.0051	0.9387 - 0.9497
Precision (Weighted)	0.9444 ± 0.0045	0.9397 - 0.9498

Test Set Performance

Accuracy: 0.9423
F1-Score (Weighted): 0.9424
Precision (Weighted): 0.9428
Recall (Weighted): 0.9423
Inference Speed: 232.83 samples/second

Usage

Quick Start

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import re

# Load model and tokenizer
model_name = "junaid1993/distilroberta-bot-detection"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def preprocess_text(text):
    """Clean text for bot detection"""
    if not isinstance(text, str):
        return ""
    
    # Remove URLs
    text = re.sub(r'http\S+|www\.\S+', '', text)
    # Remove @ and # symbols
    text = re.sub(r'[@#]', '', text)
    # Remove punctuation and special characters
    text = re.sub(r'[^\w\s]', '', text)
    # Remove numbers
    text = re.sub(r'\d+', '', text)
    # Clean whitespace
    text = re.sub(r'\s+', ' ', text).strip()
    
    return text.lower()

def predict_bot(text, threshold=0.5):
    """Predict if text is bot-generated"""
    clean_text = preprocess_text(text)
    
    if not clean_text:
        return {"prediction": "unknown", "confidence": 0.5}
    
    inputs = tokenizer(
        clean_text,
        return_tensors="pt",
        truncation=True,
        padding=True,
        max_length=512
    )
    
    with torch.no_grad():
        outputs = model(**inputs)
        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    
    bot_prob = probabilities[0][1].item()
    prediction = "bot" if bot_prob > threshold else "human"
    
    return {
        "prediction": prediction,
        "bot_probability": round(bot_prob, 4),
        "human_probability": round(probabilities[0][0].item(), 4)
    }

# Example usage
text = "🔥 AMAZING DEAL! Click here now!"
result = predict_bot(text)
print(f"Prediction: {result['prediction']} (Bot: {result['bot_probability']})")

Training Details

Model Architecture

Base Model: distilroberta-base
Task: Binary sequence classification
Classes: Human (0) vs Bot (1)
Parameters: ~82M parameters

Training Configuration

Epochs: 10 (with early stopping)
Batch Size: 2 per device, gradient accumulation steps: 8
Learning Rate: Automatic (AdamW optimizer)
Weight Decay: 0.01
Mixed Precision: FP16
Class Weighting: Applied to handle dataset imbalance

Data Preprocessing

URL removal
Special character cleaning (@ symbols, hashtags)
Punctuation removal
Number removal
Whitespace normalization
Lowercase conversion

Validation Methodology

Cross-Validation: 5-fold Stratified K-Fold
Test Split: 20% holdout set
Metrics: Accuracy, Precision, Recall, F1-score (both weighted and macro)

Limitations

Domain: Primarily trained on social media text patterns
Language: English text only
Temporal: Bot patterns may evolve over time, requiring retraining
Context: Performance may vary with text length and complexity

Intended Use

This model is designed for:

Social media content moderation
Academic research on bot detection
Content analysis and verification

Ethical Considerations

This model should be used responsibly and not for harassment
Results should be interpreted with appropriate confidence thresholds
Human oversight is recommended for critical decisions
Regular model updates may be needed as bot techniques evolve

Citation

bibtex

@model{distilroberta-bot-detection-2024,
  title={Bot Detection Model using DistilRoBERTa},
  author={Junaid Ahmed and Dariusz Jemielniak and Leon Ciechanowski},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/junaid1993/distilroberta-bot-detection}
}

License

MIT License

Model Card Created: 2025-08-23
Framework: PyTorch + Transformers
Validation: 5-Fold Cross-Validation with Class Weighting