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Research

A playground for NLP and Recommender systems

Usage

We're using pipenv to generate the environment. Please install it and run

pipenv install

and then

pipenv shell

to have the environment with all the dependencies.

For linting, please use black through the Visual studio code or by running

black .

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   └──papers
│       ├── 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.
│   │
│   └──example_structure
│   │
│   └──papers
│       ├── __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.testrun.org

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

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