README.md

---
license: mit
pipeline_tag: text-classification
library_name: transformers
tags:
- TEXT
- MODEL
- text-classification
- ai-detection
- xlm-roberta
- multilingual
- ext-classification
- human-vs-ai
---

# Text Detector

## 🧠 Model Description
This model is designed to detect whether a text is AI-generated or human-written.  
It uses **XLM-RoBERTa** architecture for accurate **multilingual text classification**.

---

## 🔍 Model Usage

### 🐍 Python Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("yaya36095/text-detector")
model = AutoModelForSequenceClassification.from_pretrained("yaya36095/text-detector")

def detect_text(text):
    # Tokenize input
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

    # Get prediction
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

    # Process results
    scores = predictions[0].tolist()
    results = [
        {"label": "HUMAN", "score": scores[0]},
        {"label": "AI", "score": scores[1]}
    ]

    return {
        "prediction": results[0]["label"],
        "confidence": f"{results[0]['score']*100:.2f}%",
        "detailed_scores": [
            f"{r['label']}: {r['score']*100:.2f}%" for r in results
        ]
    }