---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

## AI-Generated Text Detector
This repository contains a RoBERTa-based model trained to distinguish between AI-generated and human-written text. The model can help identify content created by large language models like ChatGPT, Claude, and other AI text generators.

## Model Description

Architecture: RoBERTa-base fine-tuned for binary classification
Task: Detecting whether text is written by a human (0) or generated by AI (1)
Training Data: The model was trained on a balanced dataset of human-written and AI-generated texts
Input: Text with maximum length of 256 tokens
Output: Binary classification with probability score

### Use Cases

- **Content moderation**: Identify AI-generated content in submissions  
- **Academic integrity**: Help detect AI-generated essays or assignments  
- **Research**: Study the differences between human and AI writing patterns  
- **Media verification**: Support efforts to label AI-generated content  

### Limitations

The model may not perform as well on:
- Very short texts  
- Highly technical or specialized content  
- Content from newer AI models it wasn't trained on  
- Text that has been deliberately edited to evade detection

Made with ❤️ by Abuzaid




## How to use

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "Abuzaid01/Ai_Human_text_detect"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare text for classification
text = "Your text to classify goes here."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True)

# Run inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    
# Get the predicted class and probabilities
probabilities = torch.nn.functional.softmax(logits, dim=1)
predicted_class_idx = torch.argmax(probabilities, dim=1).item()
confidence = probabilities[0][predicted_class_idx].item()

# Map class index to label
labels = ["Human-written", "AI-generated"]
predicted_label = labels[predicted_class_idx]

print(f"Prediction: {predicted_label}")
print(f"Confidence: {confidence:.4f}")
```