The project was created during the ML in production course.
Team: Andrey Korotkiy, Kirill Voronin, Kirill Surkov
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`.
├── README.md <- The top-level README for developers using this project.
├── 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.
│
├── poetry.lock <- The requirements file for reproducing the analysis environment.
│ Generated with poetry.
│
├── pyproject.toml <- Project settings and configuration.
│
├── b4u_serving <- FastAPI here.
│
├── tests <- Tests here.
│
├── 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
│
└── start.py <- Start file.
create environment via poertry
poerty install
poerty lockadd broken torch dependencies via pip
pip3 install "transformers[torch]" Project based on the cookiecutter data science project template. #cookiecutterdatascience