En2Ru Scientific Translator

Translator focused on exact science texts

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── conf               <- Configuration files
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

---

Installation

pip3 install -r requirements.txt

Dataset

Training dataset

The training dataset is based on saier/unarxive_citrec hf.

Details:

Train size: 9082
Valid size: 702
Test size: 568

All the samples have length from 128 to 512 characters (TO-DO: characters -> tokens)
More in notebooks/data/dataset_download.ipynb

After collecting the dataset, we carefully translated the samples from English to Russian using the OpenAI API.
Details in notebooks/data/dataset_translate.ipynb

Dataset for model comparison (EvalDataset)

This dataset is based on turkic_xwmt, subset=ru-en, split=test hf.

Dataset size: 1000

Models comparison

Models comparison is based on bleu score of the translated samples and reference translation by OpenAI.

Models:
transformer-en-ru: Helsinki-NLP/opus-mt-en-ru hf
nnlb-1.3B-distilled: facebook/nllb-200-distilled-1.3B hf

Results:

transformer-en-ru BLEU: 2.58
nnlb-1.3B-distilled BLEU: 2.55

Even though results aren't statistically important, transformer-en-ru model was chosen since it's faster and has smaller size.
Details in src/finetune/eval_bleu.py

Model finetuning

Simple seq2seq model finetuning transformer-en-ru.
Details in notebooks/finetune/finetune.ipynb.
Model on hf

Fine-tuned model results:

eval_loss: 0.656
eval_bleu: 67.197

(BLEU is suspeciously high)

Translation App

Synonyms Searcher
Simple version is based on word2vec model, namely fasttext (link). We've chosen fasttext because it solves the problem of out-of-vocabulary words.

Project based on the cookiecutter data science project template. #cookiecutterdatascience