Repository for Content-Aware Transformer

CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences

Python 3.6 PyTorch 1.2 cuDNN 7.3.1 License CC BY-NC-SA

This is the origin Pytorch implementation of CAT in the following paper: [CAT: Beyond Efficient Transformer for Content-Aware AnomalyDetection in Event Sequences].

Figure 1. The architecture of CAT.


  • Python 3.6
  • matplotlib == 3.1.1
  • numpy == 1.19.4
  • pandas == 0.25.1
  • scikit_learn == 0.21.3
  • torch == 1.8.0

Dependencies can be installed using the following command:

pip install -r requirements.txt


The log datasets used in the paper can be found in the repo loghub. In this repository, an small sample of the HDFS dataset is proposed for a quick hands-up.

For generating the Log template files, please refer to the official implementation repo of logparser.


The simplest way of running CAT is to run python --data HDFS.


If you have any questions, feel free to contact Shengming Zhang through Email ([email protected]) or Github issues.

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