Machine Translation

Russian to English machine translation using Transformer model.

Purpose

This project was done during the Practical Machine Learning and Deep Learning course at Spring 2020 semester at Innopolis University (Innopolis, Russia).

How to run

Firstly install all requirements from requirements.txt.

Data preparation

Then you need to prepare your dataset for the model, running the preparation.py:

usage: preparation.py [-h] [--dataset_size [DATASET_SIZE]]
                      [--vocab_size [VOCAB_SIZE]]
                      [--temp_file_path [TEMP_FILE_PATH]]
                      dataset_path store_path

positional arguments:
  dataset_path          path to the dataset.
  store_path            path there to store the results.

optional arguments:
  -h, --help            show this help message and exit
  --dataset_size [DATASET_SIZE]
                        max number of samples in dataset.
  --vocab_size [VOCAB_SIZE]
                        the size of the tokenizer's vocabulary.
  --temp_file_path [TEMP_FILE_PATH]
                        path there to save the temp file for the tokenizer.

It will create the train/test/val datasets and a BPE tokenizer for the model.

Training

After that you can train the model using the run_transformer.py:

usage: run_transformer.py [-h] [--model_save_path [MODEL_SAVE_PATH]]
                          [--batch_size [BATCH_SIZE]]
                          [--learning_rate [LEARNING_RATE]]
                          [--n_words [N_WORDS]] [--emb_size [EMB_SIZE]]
                          [--n_hid [N_HID]] [--n_layers [N_LAYERS]]
                          [--n_head [N_HEAD]] [--dropout [DROPOUT]]
                          data_path n_epochs tokenizer_path

positional arguments:
  data_path             path to the train/test/val sets.
  n_epochs              number of training epochs.
  tokenizer_path        path to the tokenizer.

optional arguments:
  -h, --help            show this help message and exit
  --model_save_path [MODEL_SAVE_PATH]
                        there to load/save the model.
  --batch_size [BATCH_SIZE]
                        batch size for training/validation.
  --learning_rate [LEARNING_RATE]
                        learning rate for training.
  --n_words [N_WORDS]   number of words to train on.
  --emb_size [EMB_SIZE]
                        embedding dimension.
  --n_hid [N_HID]       the dimension of the feedforward network model in
                        nn.TransformerEncoder.
  --n_layers [N_LAYERS]
                        the number of encoder/decoder layers in transformer.
  --n_head [N_HEAD]     the number of heads in the multiheadattention layers.
  --dropout [DROPOUT]   dropout rate during the training.

After the training it will compute the BLEU score on test dataset.

Translation

To translate the text use the translate.py:

usage: translate.py [-h] [--encoding [ENCODING]] [--max_len [MAX_LEN]]
                    model_path tokenizer_path in_data_path out_data_path

positional arguments:
  model_path            path to the trained model.
  tokenizer_path        path to the tokenizer.
  in_data_path          path to the input data.
  out_data_path         path where to save the results.

optional arguments:
  -h, --help            show this help message and exit
  --encoding [ENCODING]
                        encoding for files.
  --max_len [MAX_LEN]   maximum translation length.

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