Uzbek Spam Detector

A fine-tuned XLM-RoBERTa model for detecting spam messages in Uzbek language, specifically designed for Telegram-style messages.

Model Description

This model classifies Uzbek text messages as either spam or normal (ham). It was fine-tuned on a synthetic dataset of 2,000 Uzbek messages covering common spam patterns found in Telegram and messaging platforms.

Intended Use

Primary Use Cases

• Spam filtering for Uzbek Telegram bots
• Content moderation for Uzbek social platforms
• Message classification in Uzbek chat applications

Out-of-Scope Use

• Languages other than Uzbek
• Long-form document classification
• Detecting other types of harmful content (hate speech, etc.)

How to Use

Quick Start

from transformers import pipeline

# Load the model
classifier = pipeline("text-classification", model="sukhrobnurali/uzbek-spam-detector")

# Classify messages
result = classifier("Salom! Bugun uchrashuvga kela olasanmi?")
print(result)
# [{'label': 'normal', 'score': 0.98}]

result = classifier("TEZKOR KREDIT! 50% chegirma! Bosing: example.com")
print(result)
# [{'label': 'spam', 'score': 0.99}]

Batch Classification

messages = [
    "Rahmat katta yordam uchun!",
    "Tabriklaymiz! Siz 1000$ yutdingiz!",
    "Kecha juda charchab uyga keldim",
]

results = classifier(messages)
for msg, res in zip(messages, results):
    print(f"{res['label']}: {msg[:40]}...")

Using with PyTorch

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("sukhrobnurali/uzbek-spam-detector")
model = AutoModelForSequenceClassification.from_pretrained("sukhrobnurali/uzbek-spam-detector")

text = "Bizning kanalga obuna bo'ling!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

with torch.no_grad():
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=-1)

label = model.config.id2label[prediction.item()]
print(f"Prediction: {label}")

Training Details

Training Data

The model was trained on sukhrobnurali/uzbek_spam_dataset, a synthetic dataset of 2,000 Uzbek messages:

Spam Categories Covered

• Aggressive advertising and promotions
• Get-rich-quick schemes
• Unsolicited loan/credit offers
• Fake prize/giveaway announcements
• Clickbait messages
• Channel/group promotion spam

Training Procedure

Evaluation Results

Performance on the held-out test set (200 samples):

Classification Report

              precision    recall  f1-score   support

      normal       1.00      1.00      1.00       106
        spam       1.00      1.00      1.00        94

    accuracy                           1.00       200
   macro avg       1.00      1.00      1.00       200
weighted avg       1.00      1.00      1.00       200

Note: The perfect scores are due to the synthetic nature of the training data, where spam and normal messages have distinct, learnable patterns. Real-world performance may vary with organic messages that have more subtle spam indicators.

Limitations

1. Synthetic Data: The model was trained on AI-generated messages, which may not capture all real-world spam patterns.

2. Domain Specific: Optimized for Telegram-style short messages. Performance may vary on:

   • Long-form content
   • Formal documents
   • Other messaging platforms

3. Language Coverage: Primarily tested on Uzbek. May have unpredictable behavior on:

   • Code-mixed Uzbek-Russian text
   • Heavy use of transliteration

4. Evolving Spam: Spam tactics change over time. The model may need retraining to catch new patterns.

Ethical Considerations

• False Positives: The model may incorrectly flag legitimate messages as spam. Always provide users a way to report misclassifications.
• Bias: Synthetic training data may contain biases from the generation model.
• Privacy: This model processes text locally and does not store or transmit user messages.

Citation

If you use this model in your research or project, please cite:

@misc{uzbek-spam-detector,
  author = {Sukhrob Nurali},
  title = {Uzbek Spam Detector: Fine-tuned XLM-RoBERTa for Uzbek Spam Classification},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/sukhrobnurali/uzbek-spam-detector}
}

Links

• Model: sukhrobnurali/uzbek-spam-detector
• Dataset: sukhrobnurali/uzbek_spam_dataset
• Base Model: xlm-roberta-base

License

This model is released under the MIT License.