fastText VS Clipboard

- trained with cross-entropy, meaning that model scores can be used more effectively as a 'confidence' - e.g. for spam if you want to say something like "if prediction score > X, then filter", Naive Bayes is not ideal due to the 'naive' assumption which makes the scores very un-calibrated (it tends to give extremely high or low confidence scores for most things).

disclaimer: I haven't really thought about NLP for about 3 years so there may be something better than this now

[1] https://github.com/facebookresearch/fastText

fasttext 50

https://github.com/facebookresearch/fastText the readme may have what I need built in. But not sure. I hate ML documentation. I would love to see data input to data output examples because people expect us to understand their line of thought, and it just doesn't work out that way. This looks like what I need, but I've completely misinterpreted ML documentation many times. Ha

To use the model trained with FastText from the browser, it is necessary to load it via WebAssembly. However, you don't require a WebAssembly knowledge as you can use the fasttext.js file which has all the glue code.

Having the backend code in the plugin repo, and in python held me off. I wrote it to split vimscript/python from the command that finds the info, as it allows to use powerful tools like fasttext rather than a dictionary.

And `cb` which works cross-platform, via https://github.com/Slackadays/clipboard

There's also CB which works with both X11 and Wayland.

I'm working on my C++ project here which is getting a special new feature soon. However, that feature is going to involve iterating over potentially hundreds of thousands of directories. So, to make sure it stays fast even on slow platforms, I decided to do some benchmarking on the slowest system you could conceivably run it on, the LicheePi with the sad little single core Allwinner D1 with the C906 CPU.