Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- ahmadreza13/human-vs-Ai-generated-dataset
+---
+# 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 | {f1:0.9979} |
+| Recall | {f1:0.9989} |
+| F1 Score | {f1:0.997} |
+| ROC AUC | {roc_auc:0.998} |
+
+## 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
+from transformers import AutoTokenizer, AutoModel
+import torch
+import numpy as np
+
+# Load model and tokenizer
+from transformers import RobertaTokenizer, AutoModelForSequenceClassification
+import torch
+
+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}
+}
+```