README.md

---
license: mit
datasets:
- ahmadreza13/human-vs-Ai-generated-dataset
pipeline_tag: text-classification
---
# AI-Generated Content Detection Model

## Model Description

This model is designed to detect AI-generated content by analyzing text using a combination of RoBERTa embeddings, Word2Vec embeddings, and engineered linguistic features.

## Model Architecture

The model utilizes a hybrid architecture that combines:

- **RoBERTa Base**: For contextual text embeddings
- **Word2Vec Embeddings**: For additional semantic information
- **Engineered Linguistic Features**: Including sentiment analysis metrics, readability scores, and lexical diversity

The model architecture consists of:
- A pre-trained RoBERTa base model with the first 6 layers frozen
- Gradient checkpointing enabled for memory efficiency
- A fully connected network that combines RoBERTa embeddings with Word2Vec and engineered features
- Three fully connected layers (512 → 128 → 1) with ReLU activations and dropout

## Training Information

- **Dataset**: https://huggingface.co/datasets/ahmadreza13/human-vs-Ai-generated-dataset
- **Training Strategy**:
- Mixed precision training with gradient accumulation
- OneCycleLR learning rate scheduler
- Early stopping based on validation F1 score
- **Hyperparameters**:
- Learning rate: 3e-5
- Batch size: 32
- Gradient accumulation steps: 2
- Dropout rate: 0.3
- Training epochs: Up to 3 with early stopping

## Performance Metrics

| Metric | Score |
|-----------|-------|
| Precision | {f1:0.9079} |
| Recall | {f1:0.9089} |
| F1 Score | {f1:0.907} |
| ROC AUC | {roc_auc:0.908} |

## Limitations

- The model's performance may vary based on the type of AI-generated content, as different AI models produce text with different characteristics
- Performance may be reduced on highly technical or domain-specific content that wasn't well-represented in the training data
- The model may produce occasional false positives on human-written content that exhibits unusually high coherence or consistency

## Ethics & Responsible Use

This model is intended to be used as a tool for:
- Research on AI-generated content characteristics
- Content moderation and filtration where transparency about content source is important
- Educational purposes to understand differences between human and AI-written content

This model should NOT be used to:
- Make high-stakes decisions without human oversight
- Discriminate against content creators
- Falsely attribute content to AI or humans with absolute certainty

## Usage Examples

```python
# Load model and tokenizer
from transformers import RobertaTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np

def predict_with_huggingface_model(text, repo_id="prasoonmhwr/ai_detection_model", device="cuda"):
    """
    Predicts using a model from the Hugging Face Model Hub.
    
    Args:
    text (str): The text to predict on.
    repo_id (str): The repository ID of the model on Hugging Face Hub.
    device (str): "cuda" if GPU is available, "cpu" otherwise
    
    Returns:
    float: The prediction probability (between 0 and 1).
    """
    # 1. Load the tokenizer
    tokenizer = RobertaTokenizer.from_pretrained(repo_id)
    
    # 2. Load the model
    model = AutoModelForSequenceClassification.from_pretrained(repo_id).to(device)
    model.eval() # Set the model to evaluation mode
    
    # 3. Tokenize the input text
    inputs = tokenizer(text,
    add_special_tokens=True,
    max_length=128,
    padding='max_length',
    truncation=True,
    return_tensors='pt').to(device) # Move inputs to device
    
    # 4. Make the prediction (no gradient calculation needed)
    with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    probabilities = torch.sigmoid(logits).cpu().numpy().flatten() # Get probabilities, move to CPU
    
    return probabilities[0] # Return the probability for the positive class


if __name__ == '__main__':
    # Example usage:
    text_to_predict = "This is a sample text to check if it was written by a human or AI"
    # text_to_predict = "This text was generated by an AI model." # uncomment to test on an AI generated text
    
    # Set the device
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    repo_id = "prasoonmhwr/ai_detection_model"
    
    # Make the prediction
    prediction = predict_with_huggingface_model(text_to_predict, repo_id, device)
    
    # Print the result
    print(f"Text: '{text_to_predict}'")
    print(f"Prediction (Probability of being AI-generated): {prediction:.4f}")
    
    if prediction > 0.5:
    print("The model predicts this text is likely AI-generated.")
    else:
    print("The model predicts this text is likely human-generated.")
```

## Citation

If you use this model in your research, please cite:

```
@misc{ai_detection_model,
author = {Prasoon Mahawar},
title = {AI-Generated Content Detection Model},
year = {2025},
publisher = {HuggingFace},
url = {https://huggingface.co/prasoonmhwr/ai_detection_model}
}
```