Spam Deberta V4

🚀 RUSpam/spam_deberta_v4

A spam detection model based on the Deberta architecture, fine - tuned on Russian spam data. It classifies text as either spam or non - spam.

🚀 Quick Start

This is a spam detection model based on the Deberta architecture, which has been fine - tuned on Russian spam data. It can classify text into spam or non - spam categories.

✨ Features

• Spam Detection: Capable of accurately classifying Russian text as spam or non - spam.
• Based on Deberta: Utilizes the powerful Deberta architecture for better performance.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_path = "RUSpam/spam_deberta_v4"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predicted_class = torch.argmax(logits, dim=1).item()
    return "Спам" if predicted_class == 1 else "Не спам"

text = "Ваш текст для проверки здесь"
result = predict(text)
print(f"Результат: {result}")

📚 Documentation

📄 License

This project is licensed under the MIT license.

📚 Citation

@MISC{RUSpam/spam_deberta_v4,
    author  = {Denis Petrov, Kirill Fedko (Neurospacex),  Sergey Yalovegin},
    title   = {Russian Spam Classification Model},
    url     = {https://huggingface.co/RUSpam/spam_deberta_v4/},
    year    = 2024
}