# indicbert-scam-classifier by Anmol Shrivastav: Open Model Source: https://savrn.com/models/indicbert-scam-classifier Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve indicbert-scam-classifier (278M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.6 GB | 0.7 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.3 GB | 0.3 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.1 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 7, 2026. [indicbert-scam-classifier on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/indicbert-scam-classifier/gpus) ## Model Card By Anmol Shrivastav, published under mit, revision d5a02cb0e13e. ### IndicBERT Multilingual Scam & Fraud Classifier 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. ### Supported Languages | Language | Code | | --- | --- | | Assamese | as | | Bengali | bn | | English | en | | Gujarati | gu | | Hindi | hi | | Hinglish (Hindi in Latin script) | hi-Latn | | Kannada | kn | | Kashmiri | ks | | Malayalam | ml | | Marathi | mr | | Odia | or | | Punjabi | pa | | Tamil | ta | | Telugu | te | ### Intended Use & Capabilities 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 links. - Requests for OTPs, passwords, PINs, or banking information. - Fake delivery, refund, account-verification, and KYC messages. - Suspicious promotional and reward messages. [Read the full model card (765 words)](https://savrn.com/models/indicbert-scam-classifier/card) ## Configuration Architecture BertForSequenceClassification Context length (tokens) 512 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 250,000 Model type bert ## Identity and Version Repository anmolshrivastav/indicbert-scam-classifier Publisher Anmol Shrivastav Task Text classification Modality Text Library transformers Parameters 278M parameters Languages as, bn, en, gu, hi, kn, ks, ml Revision d5a02cb0e13e8d6a1eddccef6fa72c9b87da4462 First published 2026-09-28 Last updated 2026-10-04 ## Files and Weights 13 files, 2.5 GB in total. The weights are 3 files totalling 2.5 GB in onnx, safetensors. Weights3 files · 2.5 GB Configuration4 files · 3.1 KB Tokenizer4 files · 15.5 MB Documentation1 file · 7.9 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 1.1 GB | 73c952f5d60f | | onnx/model.onnx | Weights | 278.8 MB | da51e850ea57 | | onnx/model_fp32.onnx | Weights | 1.1 GB | 411a944e9449 | | config.json | Configuration | 909 B | — | | onnx/config.json | Configuration | 775 B | — | | onnx/ort_config.json | Configuration | 764 B | — | | onnx/special_tokens_map.json | Configuration | 695 B | — | | README.md | Documentation | 7.9 KB | — | | .gitattributes | Repository | 1.5 KB | — | | onnx/tokenizer.json | Tokenizer | 7.7 MB | — | | onnx/tokenizer_config.json | Tokenizer | 5.4 KB | — | | tokenizer.json | Tokenizer | 7.7 MB | — | | tokenizer_config.json | Tokenizer | 489 B | — | ## License and Download License mit Access Open weights, no gate Download size 2.5 GB [Download from Anmol Shrivastav](https://huggingface.co/anmolshrivastav/indicbert-scam-classifier) Released by Anmol Shrivastav through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Derived from ai4bharat/IndicBERTv2-MLM-only - Quantized from ai4bharat/IndicBERTv2-MLM-only - Trained on (disclosed) anmolshrivastav/scam_ham_india_14_languages ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 2.5 GB | | 16-bit | 0.6 GB | | 8-bit | 0.3 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About indicbert-scam-classifier ### How much GPU memory does indicbert-scam-classifier need? About 0.7 GB at 16-bit and 0.2 GB at 4-bit: the weights (278M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run indicbert-scam-classifier on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use indicbert-scam-classifier commercially? Yes. indicbert-scam-classifier is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ### What is indicbert-scam-classifier's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models 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) Model · Text classification ### [bge-reranker-base](https://savrn.com/models/bge-reranker-base) [Beijing Academy of Artificial Intelligence](https://savrn.com/model-publishers/baai) We have updated the new reranker, supporting larger lengths, more languages, and achieving better performance. More details please refer to our Github: FlagEmbedding. FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - 3/18/2024: Release new rerankers, built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation. - 3/18/2024: Release Visualized-BGE, equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text… Open weights mit 278M parameters 514 tokens sentence-transformers [View model](https://savrn.com/models/bge-reranker-base) Model · Text classification ### [Bangla-twoclass-Sentiment-Analyzer](https://savrn.com/models/bangla-twoclass-sentiment-analyzer) [Arunava Kar](https://savrn.com/model-publishers/arunavaonly) This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - trainingsteps: 1800 - mixedprecisiontraining: Native AMP - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2 Open weights mit 278M parameters 514 tokens transformers [View model](https://savrn.com/models/bangla-twoclass-sentiment-analyzer) Model · Text classification ### [snd_sentiment_analysis](https://savrn.com/models/snd-sentiment-analysis) [Danish Mahdi](https://savrn.com/model-publishers/danishmahdi) This repository contains a fine-tuned XLM-RoBERTa Base model for 3-class sentiment classification in Sindhi. The model predicts one of the following labels: - positive - neutral - negative This model is intended for research, experimentation, and lightweight production prototyping on Sindhi-language sentiment analysis tasks. It may be useful for short-text classification such as reviews, feedback, and social-media snippets. It should not be used as the only basis for high-stakes decisions. Performance can vary by domain, dialect, and writing style, and human review is recommended when errors could affect people or organizations. The saved model artifacts in this repository report the… Open weights mit 278M parameters 514 tokens transformers [View model](https://savrn.com/models/snd-sentiment-analysis) Model · Text classification ### [multilingual-intent-router](https://savrn.com/models/multilingual-intent-router) [Gaurav Gandhi](https://savrn.com/model-publishers/gauravgandhi2411) This is v1, the shipped model (branch main): a 12-class intent classifier for logistics chat messages, fine-tuned from intfloat/multilingual-e5-base, with an optional abstention flag for messages unlike the training data. A robustness variant (v3) is on branch robust-v3. Model fingerprint (sha256 of fp32 state dict): 7ca22e16d2ace345d9dcf7737887e85fedf400a1dfc863678c2fc2663bf102bd Abstention (the out-of-distribution flag) is NOT part of the plain model: it needs predict.py and oodbank.safetensors from the same repository, used as follows. Confidences are temperature-scaled (T = 1.0228); messages whose Mahalanobis score falls below the threshold (-128.06 v1 / -178.56 v3; this model's… Open weights mit 278M parameters 514 tokens transformers [View model](https://savrn.com/models/multilingual-intent-router) Model · Text classification ### [xlm-roberta-base-language-detection](https://savrn.com/models/xlm-roberta-base-language-detection) [Luca Papariello](https://savrn.com/model-publishers/papluca) This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset. This model is an XLM-RoBERTa transformer model with a classification head on top (i.e. a linear layer on top of the pooled output). For additional information please refer to the xlm-roberta-base model card or to the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 20 languages: arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl)… Open weights mit 278M parameters 514 tokens transformers [View model](https://savrn.com/models/xlm-roberta-base-language-detection) ## Anmol Shrivastav [All models and datasets](https://savrn.com/model-publishers/anmolshrivastav) ## Versions - [d5a02cb0e13e](https://savrn.com/models/indicbert-scam-classifier/versions/d5a02cb0e13e) · current 2026-10-04 ## Explore More - [All text classification models](https://savrn.com/models/tasks/text-classification) - [All models under mit](https://savrn.com/models/licenses/mit) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-04. - [Hugging Face record](https://huggingface.co/anmolshrivastav/indicbert-scam-classifier) - [How the hub is built](https://savrn.com/model-hub/methodology)