I didn't want to shell out $20 a month for the general thing, so I spent $90 on data collection and built my own for code comments specifically. It runs locally in your browser with a relatively small classification model trained on old-school stylometric features. You can try that before turning to Pangram for uncertain cases, if you wish.[1]
It's easy to get high accuracy numbers if you're testing on long (50+ words) texts. Much harder when the documens are short, as code comments tend to be.[2]
kqr · · focus · HN ↗
It's easy to get high accuracy numbers if you're testing on long (50+ words) texts. Much harder when the documens are short, as code comments tend to be.[2]
[1]: <a href="https://xkqr.org/aicomment" rel="nofollow">https://xkqr.org/aicomment
[2]: <a href="https://entropicthoughts.com/better-ai-comment-classifier" rel="nofollow">https://entropicthoughts.com/better-ai-comment-classifier