jni-rs VS tokenizers

I do have education and real experience in the field. See, for example, my recent work on the rather arcane subject of Rust↔Java FFI. You don't get to pull rank with me, son. I know quite well what CreateFile does. (Fun fact: it creates a file handle, i.e. opens a file, and may or may not create the file itself.)

I've been playing around with jni-rs, I'm eventually planning on developing an app with Java gui and Rust backend. However I'm having some trouble advancing past HelloWorld lol. For example, here's some simple code that doesn't work and I really can't figure out why:

- JNI - how well does https://github.com/jni-rs/jni-rs work? Assuming that Firefox runs on Android and uses Rust this should be reasonable to work with?

Even so, staying relatively close to the Java focus of the class might be a good idea, since it will help prepare you for the AP exam. One idea would be using jni-rs to build Java bindings for a useful Rust library. Interesting candidates might be io-uring, lockfree, or even pyo3, if you wanted to try your hand at integrating Java and Python.

Hugging Face seems to like Rust. They also wrote Tokenizers in Rust.

Your intuition is right. There are two ways (in increasing order of result performance) : 1. You can simply extend vocab file of the tokenizer and test the predictions 2. You can extend the vocab file and re-train your model on custom data which has these new tokens. Check the following issue on GitHub : https://github.com/huggingface/tokenizers/issues/247

SentencePiece -> implementation of some algorithms (there are several others, https://github.com/microsoft/BlingFire https://github.com/glample/fastBPE https://github.com/huggingface/tokenizers )

In sum I would like the idea to go with Rust as I more or less got to rewrite the whole thing anyway, but I am a bit skeptical if I will be able to interface with everything that might come up at some point. Or probably end up in a wrapper hell if I got to use more C++ libraries. On the other hand there are definitely a few Rust projects out there that might come in handy (for example https://github.com/huggingface/tokenizers). And the build process is pretty awful right now (CMake it is but with lots of hacks).

A full machine learning pipeline usually comprises far more than just the model, and this is the area where Rust may shine (the recent work by HuggingFace and their https://github.com/huggingface/tokenizers library is a good example)

As for other tokenizers, you can take a look at - Huggingface tokenizers library: https://github.com/huggingface/tokenizers - NLTK tokenize: https://www.nltk.org/api/nltk.tokenize.html - Polygot: https://pypi.org/project/polyglot/

Huggingface Tokenizers (https://github.com/huggingface/tokenizers), which are now used by default in their Transformers Python library, use pyO3 and became popular due to the pitch that it encoded text an order of magnitude faster with zero config changes.

It lives up to that claim. (I had issues with return object typing when going between Python/Rust at first but those are more consistent now)

huggingface/tokenizers (Rust): 💥Fast State-of-the-Art Tokenizers optimized for Research and Production