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README.md

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+---
+language:
+- en
+pipeline_tag: text-classification
+tags:
+- sms-spam
+- phishing-detection
+- scam-detection
+- security
+metrics:
+- f1
+- accuracy
+- precision
+- recall
+widget:
+- text: "Your account is blocked! Verify immediately with OTP. Send money to scam@ybl using https://scam.xyz/"
+  example_title: "Bank KYC Scam"
+- text: "Congratulations! You won Rs 50,000 lottery prize. Contact urgently to claim via link: http://bit.ly/claim"
+  example_title: "Lottery Scam"
+- text: "Hey, are we still meeting for lunch tomorrow at 12?"
+  example_title: "Safe Message"
+---
+
+# SCAMBERT: DistilBERT for SMS Fraud & Scam Detection
+
+SCAMBERT is a fine-tuned `distilbert-base-uncased` model specifically designed to detect social engineering, financial fraud, phishing, and scam payloads in SMS and short-form conversational text. It is built as Layer 3 of the AI Honeypot (CIPHER) Threat Intelligence Pipeline.
+
+## Model Summary
+
+- **Model Type:** Text Classification (Binary)
+- **Base Model:** `distilbert-base-uncased`
+- **Language:** English (en)
+- **Task:** Spam/Scam Detection
+- **License:** MIT (or your project's license)
+- **Size:** ~255 MB
+
+### Labels
+
+- `0`: Safe / Legitimate
+- `1`: Scam / Fraud / Phishing
+
+## Performance & Metrics
+
+The model was fine-tuned on a dataset of **8,438** samples (27.5% Scam / 72.5% Safe). Due to class imbalance, class weights were applied during training.
+
+### Calibration & Validation Results
+
+- **Best Accuracy:** 99.41%
+- **Best F1-Score:** 98.92%
+- **Calibrated Precision:** 95.08%
+- **Calibrated Recall:** 100.0%
+- **Optimal Threshold:** `0.0028` (For high-recall environments)
+
+### Robustness Evaluation
+
+The model was tested against common bad-actor obfuscation tactics:
+
+| Tactic | Example Input | Prediction Probability | Passed |
+| :--- | :--- | :--- | :--- |
+| **URL Obfuscation** | `Win $1000 fast! Click hxxp://scammy...` | 99.9% Scam | ✅ |
+| **Numeric Substitution** | `W1NNER! Y0u have b33n select3d...` | 99.3% Scam | ✅ |
+| **Mixed Case** | `cOnGrAtUlAtIoNs, yOu WoN a FrEe...` | 89.8% Scam | ✅ |
+
+*Note: The model occasionally struggles with extremely short, contextless messages (e.g., "Call me now") as intended, relying on earlier heuristic layers for context.*
+
+## Usage
+
+You can use this model directly with Hugging Face's `pipeline`:
+
+```python
+from transformers import pipeline
+
+# Load the pipeline
+classifier = pipeline("text-classification", model="Digvijay05/SCAMBERT")
+
+# Inference
+text = "Earn Rs 5000 daily income from home part time. Click this link: http://bit.ly/job"
+result = classifier(text)
+
+print(result)
+# [{'label': 'LABEL_1', 'score': 0.99...}] 
+```
+
+Or run via the **Inference API**:
+
+```python
+import httpx
+
+API_URL = "https://api-inference.huggingface.co/models/Digvijay05/SCAMBERT"
+headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
+
+response = httpx.post(API_URL, headers=headers, json={"inputs": "Your account is locked. Verify at bit.ly/secure"})
+print(response.json())
+```
+
+## Deployment Considerations
+
+- **CPU Latency Estimate:** ~10-30ms / sequence
+- **GPU Latency Estimate:** ~2-5ms / sequence
+- **Recommendation:** Can be efficiently hosted on serverless CPU environments (like Render Free Tier) using Hugging Face's Inference API, or deployed natively if 512MB+ RAM is available. ONNX quantization is recommended for edge deployments.
+
+## Intended Use
+
+This model is designed as a *semantic booster* tie-breaker layer within a multi-layered classification engine. It excels at detecting complex sentence structures, urgency, and manipulative context that standard Regex/Heuristic rules might miss.