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Multilingual SBD using BERT models

Source attribution:

BERT punctuator Framework code borrowed from here

Modifications have been made to the notebooks, the data preprocessing, and the model architecture.

Installation and Usage:

Virtual environment:

It is HIGHLY RECOMMENDED to use a virtualenv for this project:

  1. Setup is fairly easy, open up a terminal and do:
  • python -m venv /path/to/root/dire/.../.../venv_name
  1. Then everytime you want to run the program, just do
    • source ./venv/bin/activate

Libraries:

Install the required libraries via the requirements.txt file
1. Activate your venv, and then do pip install requirements.txt

Dataset:

Pre-built:

Get the dataset here. Store the dataset in any folder and provide the necessary arguments to the model

The dataset folder structure is as follows:

dataset/
|
|-dual/
|   |
|   |-xlm-roberta-base/
|            |-- train.pkl
|            |-- valid.pkl
|            |-- test.pkl
|-en
| |-...
|
|-zh

Use the --data-path argument to set the data directory:

  --data-path DATA_PATH
                        path to dataset directory

Creation (WIP):

Source for the IWSLT dataset is over here (train and dev) and here(test). The notebooks folder has the necessary notebooks for the dataset creation.
WIP: converting the notebooks to python scripts.

Running the program

Run the main.py file:

usage: main.py [-h] [--save-model] [--break-train-loop] [--stage STAGE]
               [--model-path MODEL_PATH] [--data-path DATA_PATH]
               [--num-epochs NUM_EPOCHS]
               [--log-level {INFO,DEBUG,WARNING,ERROR}]
               [--save-n-steps SAVE_N_STEPS] [--force-save]

arguments for the model

optional arguments:
  -h, --help            show this help message and exit
  --save-model          save model
  --break-train-loop    prevent training for debugging purposes
  --stage STAGE         load model from checkpoint stage
  --model-path MODEL_PATH
                        path to model directory
  --data-path DATA_PATH
                        path to dataset directory
  --num-epochs NUM_EPOCHS
                        no. of epochs to run the model
  --log-level {INFO,DEBUG,WARNING,ERROR}
                        Logging info to be displayed
  --save-n-steps SAVE_N_STEPS
                        Save after n steps, default=1 epoch
  --force-save          Force save, overriding all settings

Sample command:

python main.py \
--model-path  '/content/drive/MyDrive/SUD_PROJECT/neural-punctuator/models-xlm-roberta-dual/' \
--num-epochs 2 \
--data-path '/content/drive/MyDrive/SUD_PROJECT/neural-punctuator/dataset/en/xlm-roberta-base/'  \
--stage 'xlm-roberta-base-epoch-1.pth'

NOTE: Make sure that any directory mentioned in the command actually exists!

Deployment (with docker)

See webapp/ directory

Bibiliography

Nagy, Attila, Bence Bial, and Judit Ács. "Automatic punctuation restoration with BERT models." arXiv preprint arXiv:2101.07343 (2021).

Federico, Marcello, et al. "Overview of the IWSLT 2012 evaluation campaign." IWSLT-International Workshop on Spoken Language Translation. 2012.

Husein, Zolkepli. "Malay-Dataset." https://github.com/huseinzol05/Malay-Dataset. (2018).
- Husein, Zolkepli. "Malay-Dataset." https://github.com/huseinzol05/malay-dataset/tree/master/crawl/iium-confession. (2018).
- Husein, Zolkepli. "Malay-Dataset." https://github.com/huseinzol05/malay-dataset/tree/master/translation/local-movies-subtitle. (2018).

Husein, Zolkepli. "Malaya." https://github.com/huseinzol05/malaya. (2018).