metadata
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
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
]
}