🤓 ProfessorLang!

ProfessorLang is my new solution to detect languages, in Telegram-ML-Contest ROUND II.

My previous solution was not good enough, so I decided to implement a new solution from the ground up, to be fast and accurate enough.

🔥 It's now super-fast (response in a few mili-seconds) and up to 99% accurate based on the language.

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Steps

• I. Prepare dataset
• II. Pre-process the sample data
• III. Tokenize
• IV. Detect important features
• V. ReTokenize based on important features
• VI. Train the model
• VII. Make it a .so library
• ENJOY!

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i.Dataset

To prepare dataset, I've used GitHub public repositories.

Also, to detect normal chats (containing no codes), I've used:

All the steps to prepare dataset can be found in i.dataset directory:

The file i.languageExtensions.csv is the list of languages and their related extensions.

2.repositoryFinder mini-project is used to find repositories for each language. To use it, put a GitHub token is the js file and specify the number of pages you want the crawler, crawl from git for each language.

3.repositories.csv is the output of running the previous js script.

4.repoDownloader mini-project downloads the source codes into 5.sources directory. This directory will be processed in next steps.

⚠️ After this step, rename CPP to CPLUSPLUS and OBJC to OBJECTIVE_C in sources directory.

5.sources are the source-codes created from previous downloader script.

6.additionalSources are the source files should be copied into 5.sources to support all languages.

7.listSources lists the source codes and their related language.

8.sourceFiles.csv is the output of 7.listSources script.

9.samplesGenerator creates 10.sampleFiles to be used in the trainer! For now we use this script to create train and valid directories seaparately with different configs in .js file. and finally move valid files into v.test directory of the project to validate final outputs.

11.keywords contains keywords dictionary for languages to be used in our tokenizer during the training phase. We create a words keyword to merge using keywordExtractor.py.

ii.dataset_other_code

I've stored all the non-source-code files here and finally after processing, copied them to source codes in the project as OTHER dir. (i.dataset/sources/OTHER/*.txt)

iii.Trainer

1.preprocess creates a merged version of keywords sorted by length to preprocess all the sample codes and tokenize them. Then tokenizes all the sample source codes.

2.preprocessedData is the directory containing these preprocessed data.

⚠️ Please make sure you empty prev directory on re-creating this directory!

3.importantFeatures is used to determine what features are more important.

4.importantFeatures is the output of script 3. We can optionally edit and only save most important ones with score more than 0 from script #3 outputs. (I did that)

5.reprocessor is used to tokenize source code based on important features. ALSO It considers other tokens as token 0 to determine non-source codes better.

6.reprocessedData is the output of previous script.

⚠️ Please make sure you empty prev directory on re-creating this directory!

7.train is the trainer using 6.reproccessedData. It finally creates a RandomForestRegressor.h file to be used in our cpp project.

You should add #include <stdint.h> top of the RandomForestRegressor.h file.

iv.tglib

Just copy RandomForestRegressor.h here and replace features in features.cpp file with FINAL_KEYWORDS.json result. Then:

mkdir build
cd build
cmake ..
cmake --build .

v.test

To test the project, you should put .so file here, and just follow these instructions:

mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
cmake --build .

To test the library output, launch the resulting binary file tglang-tester with the following parameters:

tglang-tester <input_file>