hamza-amin/urdu-spam-classifier

Urdu Spam Detection Model

Description

This model classifies Urdu text into:

• 0 → Not Spam
• 1 → Spam

It is designed for AI-powered emergency helpline systems (e.g., 1122/911) to filter prank or irrelevant calls in real time.

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Model Details

• Base Model: xlm-roberta-base
• Task: Binary Text Classification
• Language: Urdu
• Framework: Hugging Face Transformers

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Training

• Dataset: hamza-amin/urdu-spam-dataset
• Epochs: 3
• Batch size: 16
• Learning rate: 2e-5

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Evaluation Results

Metric	Score
Accuracy	0.983
F1 Score	0.982
Precision	0.993
Recall	0.972

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Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "hamza-amin/urdu-spam-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)
    return probs.tolist()
print(predict("یہ ایک ایمرجنسی ہے فوراً مدد کریں"))
print(predict("ہیلو بس مذاق کر رہا تھا"))

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Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.1146	1.0	169	0.1139	0.9834	0.9826	0.9860	0.9792
0.0902	2.0	338	0.0954	0.9834	0.9825	0.9929	0.9722
0.0085	3.0	507	0.0972	0.9867	0.9859	1.0	0.9722

Use Cases

• Emergency call filtering
• Spam detection in voice agents
• Urdu NLP classification tasks

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Limitations

• Trained on synthetic data

• May struggle with:

  • slang / mixed Urdu-English
  • very short inputs
  • noisy speech-to-text output

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Future Work

• Add urgency classification (multi-class)
• Train on real call data
• Optimize for real-time inference (ONNX / quantization)

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License

MIT