Reduce resources to load language models

[pemistahl]

Currently, the language models are parsed from json files and loaded into simple maps at runtime. Even though accessing the maps is pretty fast, they consume a significant amount of memory. The goal is to investigate whether there are more suitable data structures available that require less storage space in memory, something like NumPy for Python.

One promising candidate could be ndarray.

[ghost]

Which files ? if you require the processing in Python or in JavaScript(Node) I can work on a Google proto buffer format; quite sure the persisted model would be way lighter, maybe the processing would be fast, I do not know.
Any way, I'm glad to help.
I'm happy that you provide a JS binding as well, I'm looking for a fast language detection runnable on Node.
Thanks

[ghost]

I know this is half road, as you were asking for a better structure to gain processing time. But for big model on memory here is a solution:

I changed the format a little bit from regular Map<string: string> to Map<number[]: string[]>. I guess you treat as so anyway, so hopefully not a problem.

Here is a working example in JavaScript/Node: https://github.com/bacloud23/lingua-rs-bigrams

So here how it goes:

• You encode/persist JSON once back and forth into a lightweight binary file.
• With proto buffers, you definitely can load the binary file with the defined format, and decode the entire object (it would be way lighter).
• But If you work with the values of the ngrams key iteratively and not cumulatively (I guess so), you can (I guess) load one Pair at a time inside a loop. I think it comes with a processing cost though (again if even possible).
• You can do the same in Rust or in Python with the same proto model and the new encoding.

Drawback: new protobufjs dependency.

[getreu]

@ghost: By how much your solution reduces the binary size?