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Text Detoxification

Name: Leila Khaertdinova

Email: [email protected]

Group number: BS21 DS-02

This project is intended for the first assignment in the Practical Machine Learning and Deep Learning course at Innopolis University.

Task description

Text Detoxification Task is a process of transforming the text with toxic style into the text with the same meaning but with neutral style.

Dataset Description

The dataset is a subset of the ParaNMT corpus (50M sentence pairs). The filtered ParaNMT-detox corpus (500K sentence pairs). It is already downloaded from here, you can find it in data/raw folder. This is the main dataset for this assignment detoxification task.

Getting started

To run this project, run the following commands in the repo root directory:

  1. Create the virtual environment

    python3 -m venv .venv
source .venv/bin/activate

  2. Install the required dependencies:

    pip install -r requirements.txt

  3. Download the English language model for spaCy

    python -m spacy download en_core_web_sm

  4. Make sure you have a compatible version of Python 3.9.13 before running the code.

  5. For dataset preprocess and creation, run the following command:

    python ./src/data/make_dataset.py

    You can provide an optional argument --size <CUT_SIZE> to specify the size of the data to be processed.

  6. To train the models, run the following commands:

    # for training the pytorch transformer
python ./src/models/pytorch_transformer/train.py --batch_size <BATCH_SIZE> --epochs <NUM_EPOCHS>

# for fine-tuning the t5 model
python ./src/models/t5/train.py --batch_size <BATCH_SIZE> --epochs <NUM_EPOCHS>

    You can provide the arguments <BATCH_SIZE>, <NUM_EPOCHS> to specify the batch size and number of epochs for the training.

  7. To download weights, run the following command:

    python ./src/data/download_weights.py

    You can also provide --model <MODEL_NAME> (t5 or transfromer) to download weights for a specified model.

  8. To get the prediction results on a test set (5000 text examples), run the commands:

    # for the pytorch transformer
python ./src/models/pytorch_transformer/predict.py 

# for the t5 model
python ./src/models/t5/predict.py

  9. To run on your example:

    # for the pytorch transformer
python ./src/models/pytorch_transformer/predict.py --inference "<YOUR_EXAMPLE>"

# for the t5 model
python ./src/models/t5/predict.py --inference "<YOUR_EXAMPLE>"

    Provide <YOUR_EXAMPLE> to make inference on your own example.