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Abuzaid01/
Ai_Human_text_detect

Name: Ai_Human_text_detect
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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}")