pythran VS tokenizers

Numpy and Scipy are good reasons. Unfortunately Scipy does not even compile on FreeBSD lately, and I have opened three issues about it against Scipy and Pythran (and the fix was with xsimd).

https://github.com/serge-sans-paille/pythran/issues/2070

In someone lands here seeking a maintained compiler for Python, there's a lot, on top of my head:

- Pythran (https://pythran.readthedocs.io) (ahead of time compiler)

Yes, I mean Pythran ( https://github.com/serge-sans-paille/pythran ). Thank you.

Was Nuitka better? Pythran is quite simple to install and use in Jupyter.

The only project that compares 1:1 is Pythran: https://github.com/serge-sans-paille/pythran

Pythran is fairly nice, and it really does work. I tried it last year and it compiles down to modifiable templated C++. I was able to use it to build Python for a highly specialized environment.

All the others compile down to dynamically linked binaries, and that just puts them in the "other" box.

You might want to contact the author of Pythran [1], maybe something can be learned from what they do.

[1] https://github.com/serge-sans-paille/pythran/commits/master

[1] https://github.com/serge-sans-paille/pythran

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