Anmol Shrivastav

Model · Text classification

indicbert-scam-classifier

Anmol Shrivastav

A sequence classification model fine-tuned on top of ai4bharat/IndicBERTv2-MLM-only to detect fraudulent, phishing, and scam messages across 14 Indian languages and language varieties. The model is designed with a focus on lightweight multilingual scam detection for Indian communications and common fraud patterns. This model is intended to detect common fraud and scam patterns prevalent across Indian communications, including: - Electricity and utility disconnection threats. - Impersonation of major institutions such as banks, India Post, and courier services. - Fake lottery, subsidy, and government-scheme claims. - Suspicious payment requests and fee demands. - Phishing and malicious…

Model · Text classification

indicbert-scam-classifier-v2

Anmol Shrivastav

A sequence classification model fine-tuned on top of ai4bharat/IndicBERTv2-MLM-only to detect fraudulent, phishing, and scam messages across 14 Indian languages and language varieties[cite: 4]. This v2 model represents a significant upgrade over the baseline v1 iteration, leveraging advanced entity masking and continuous feedback loop training on a T4 GPU to heavily reduce False Negatives (scam $ightarrow$ ham misclassifications). The original v1 baseline achieved a highly respectable 98.29% accuracy but exhibited vulnerabilities to specific scam evasion tactics (e.g., protocol obfuscation like hxxp://, naked domains, and specific tele-fraud requests)[cite: 4]. To resolve this, v2 was…