TKOrrector

Transformer Based Korean Sentence Spacing Corrector

Architecture

License Summary

This solution is made available under Apache 2 license. See the LICENSE file.

Minimum Requirements

It is recommended that you run the Trainig on a machine with Nvidia GPU with drivers and CUDA installed.

Prerequisites

  1. Clone this repo and cd into it.

  2. Install dependencies. Preferrably in a virtual env.

    a. Optional: Create new virtual env. Conda example below.
    conda create --name TKOrrector python=3.9 -y
    conda activate TKOrrector

    b. Install PyTorch with CUDA conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia

    or

    b. Install PyTorch without GPU conda install pytorch torchvision torchaudio cpuonly -c pytorch

    c. Install dependencies
    pip install -r requirements.txt

Run

You can run the pretrained model without the need to Train.

Download the pretrained model and extract into the current directory (tar zxvf TKOrrector.tar.gz)

sh demo.sh

Example demo run screen and results.
Example Demo Run

Train

Download the Corpus

  1. Go to NIKL Corpus Download Site and apply for a new license.

    The cost is free but you need to sign an agreement. It is recommended that you upload the corpus file on an object storage such as GCS to quickly download on additional machines such as GCP GCE to use a VM with GPU for training as needed without huge upfront cost. Edit src/download_corpus.sh to download the Corpus file and expand it into the designated directory.

    cd src
    sh download_corpus.sh

Run the data prep stage

Change lines 51, 53 in prepare_corpus_with_tokenizer.sh to increase the training dataset size.  
The second argument is the number of files to include into the training set + 1.  
`get_corpus "../data/$CORPUS1/*" 10`  
Above command would include 9 files (manual pdf file is skipped) from the Newspaper corpus.

  1. Run the data prep command.

    sh prepare_corpus_with_tokenizer.sh

Run the training stage

  1. Run the training command.

    sh train.sh

Run the Evaluation

  1. After the training is done, evaluation of the model with test dataset can be performed with batch translations by running the command below.

    sh calculate_metrics.sh

Detailed Dataflow Diagram

Detailed Architecture

GitHub

View Github