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Open-weight model · Text classification
by Aungruk Vanichanai AungMoonLord/bert-log-anomaly-detection
bert-log-anomaly-detection is an open-weight model for text classification from Aungruk Vanichanai, released under Apache License 2.0. It has 109M parameters and a 512-token context. At 16-bit it needs about 0.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 241.3k downloads a month.
1. bert-log-anomaly-detection is a BERT-based NLP model fine-tuned for single SQL transaction log anomaly detection. 2.
What it takes to serve bert-log-anomaly-detection (109M 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 | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x 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, read Oct 16, 2026.
bert-log-anomaly-detection on every accelerator the SAVRN Index prices, at every precision
This model does one narrow job: it reads a single SQL transaction log line and labels it Normal or Anomaly. It is a fine-tuned BERT at 109M parameters, released in January 2026, and it has drawn roughly 241,000 downloads a month despite almost no likes, which suggests automated pulls rather than a broad community.
Apache 2.0 allows commercial use. We would treat it as a starting point, not a control. Anomaly labels learned from someone else's databases rarely transfer cleanly, so fine-tune on your own logs and measure false positives before it feeds any alerting or security workflow.
By Aungruk Vanichanai, published under apache-2.0, revision 9ece304f330c.
Model Summary
bert-log-anomaly-detection is a BERT-based NLP model fine-tuned for single SQL transaction log anomaly detection.
The model classifies each database transaction log as either Normal or Anomaly, with the goal of supporting AI-powered fraud detection and cybersecurity monitoring systems.
This model was developed as part of the Samsung × KBTG Digital Fraud Cybersecurity Hackathon (Thailand) under the AI-Powered Fraud Detection & Prevention track.
This model analyzes individual SQL database transaction logs and detects abnormal patterns that may indicate fraudulent, malicious, or suspicious behavior.
Demo: Hackathon prototype
import torch
from transformers import BertForSequenceClassification, BertTokenizer
MODEL_PATH = "AungMoonLord/bert-log-anomaly-detection"
model = BertForSequenceClassification.from_pretrained(MODEL_PATH)
tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)
model.eval()
8 files, 438.9 MB in total. The weights are 1 file totalling 438.0 MB in safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 438.0 MB | 9cd32047860f |
| config.json | Configuration | 707 B | — |
| special_tokens_map.json | Configuration | 732 B | — |
| README.md | Documentation | 5.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 711.7 KB | — |
| tokenizer_config.json | Tokenizer | 1.5 KB | — |
| vocab.txt | Tokenizer | 231.5 KB | — |
Released by Aungruk Vanichanai through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 438.0 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (109M parameters) plus a working margin. A long context needs more.
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.
Yes. bert-log-anomaly-detection is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
512 tokens, from the maximum position embeddings in its published configuration.
This model is a fine-tuned version of google/electra-base-discriminator on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 16 - lrschedulertype: linear - lrschedulerwarmupsteps: 50 - numepochs: 10 - Transformers 5.2.0 - Pytorch 2.10.0+cu128 - Datasets 4.5.0 - Tokenizers 0.22.2
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 32 - lrschedulertype: linear - lrschedulerwarmupsteps: 100 - numepochs: 5 - Transformers 5.17.0 - Pytorch 2.14.0+cu130 - Datasets 3.6.0 - Tokenizers 0.23.2
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
libraryname: transformers - autotrain - text-classification basemodel: google-bert/bert-base-uncased f1macro: 0.7533020080884588 f1micro: 0.7533333333333333 f1weighted: 0.7533020080884587 precisionmacro: 0.7551310982162045 precisionmicro: 0.7533333333333333 precisionweighted: 0.7551310982162046 recallmacro: 0.7533333333333333 recallmicro: 0.7533333333333333 recallweighted: 0.7533333333333333
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