It would be worth to provide a tutorial how to train a simple cross-language classification model using sentencepiece. Supposed to have a given training set and have chosen a model (let'say a simple Word2Vec plus softmax or a LSTM model, etc), how to use the created sentencepiece model (vocabulary/codes) to feed this model for train and inference?

Almost all models we use now (see list in #298) are trained privately by different contributors. With code on notebooks or scripts that may be private or may be open source but difficult to follow.

To make PyThaiNLP more transparent and more customizable by users, should try to put training scripts or instructions (can be pointers) somewhere in the repo.

Known scripts/notebooks and data

For Juman++ to be widely usable, we want to have a documented and stable C API and an option to have a dynamically linked library.
That library probably should use -fvisibility=hidden and explicit visibility on exported symbols on Unixes and __declspec(dllimport/dllexport) on Windows.

The minimal API should be:

1. Loading a model using a config file
2. Analyzing a sentence
3. Accessing