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README.md

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 # ai-detect: Fast methods to classify text as AI or human-generated
 
-[![Classifiation accuracy testing](https://github.com/Tail-Pipe/ai-detect/actions/workflows/pytest.yml/badge.svg)](https://github.com/Tail-Pipe/ai-detect/actions/workflows/pytest.yml)
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 This is a research repo for fast AI detection methods as we experiment with different techniques.
 While there are a number of existing LLM detection systems, they all use a large model trained on either an LLM or
 its training data to calculate the probability of each word given the preceeding, then calculating a score where
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 ## LZMA compression detector (`lzma_detect.py` and `nlzmadetect`)
 
+[![Python classifiation accuracy testing](https://github.com/Tail-Pipe/ai-detect/actions/workflows/pytest.yml/badge.svg)](https://github.com/Tail-Pipe/ai-detect/actions/workflows/pytest.yml)
+[![Nim classification accuracy testing](https://github.com/Tail-Pipe/ai-detect/actions/workflows/nimtest.yml/badge.svg)](https://github.com/Tail-Pipe/ai-detect/actions/workflows/nimtest.yml)
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 This is the first attempt, using the LZMA compression ratios as a way to indirectly measure the perplexity of a text.
 Compression ratios have been used in the past to [detect anomalies in network data](http://owncloud.unsri.ac.id/journal/security/ontheuse_compression_Network_anomaly_detec.pdf)
 for intrusion detection, so if perplexity is roughly a measure of anomalous tokens, it may be possible to use compression to detect low-perplexity text.