# Anmol Shrivastav: Open-Weight Models and Datasets
Source: https://savrn.com/model-publishers/anmolshrivastav
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## Models

Model · Text classification

### [indicbert-scam-classifier](https://savrn.com/models/indicbert-scam-classifier)

[Anmol Shrivastav](https://savrn.com/model-publishers/anmolshrivastav)

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…

Open weights mit 278M parameters 512 tokens transformers

[View model](https://savrn.com/models/indicbert-scam-classifier)

Model · Text classification

### [indicbert-scam-classifier-v2](https://savrn.com/models/indicbert-scam-classifier-v2)

[Anmol Shrivastav](https://savrn.com/model-publishers/anmolshrivastav)

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…

Open weights mit 278M parameters 512 tokens transformers

[View model](https://savrn.com/models/indicbert-scam-classifier-v2)

## Explore More

- [All model publishers](https://savrn.com/model-publishers)
- [The model directory](https://savrn.com/models)
- [The dataset directory](https://savrn.com/datasets)

## Source

- Listed from their public repositories, read 2026-10-04.
- [Hugging Face profile](https://huggingface.co/anmolshrivastav)
- [How the hub is built](https://savrn.com/model-hub/methodology)