Rank-Retrieval-Model

Using Tf-Idf weights for information retrieval. Ranking documents based on their relevance using their Tf-Idf weights and calculating cosine similarity with the query.

Instructions

1. Clone the repo.
2. Run make dirs to create the missing parts of the directory structure described below.
3. Optional: Run make virtualenv to create a python virtual environment. Skip if using conda or some other env manager.
   1. Run source env/bin/activate to activate the virtualenv.
4. Run make requirements to install required python packages.
5. Put the raw data in data/raw.
6. To save the raw data to the DVC cache, run dvc commit raw_data.dvc
7. Edit the code files to your heart's desire.
8. Process your data, train and evaluate your model using dvc repro eval.dvc or make reproduce
9. When you're happy with the result, commit files (including .dvc files) to git.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make dirs` or `make clean`
├── README.md          <- The top-level README for developers using this project.
├── raw_data.dvc       <- Keeps the raw data versioned.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── dvc.lock           <- The version definition of each dependency, stage, and output from the 
│                         data pipeline.
├── dvc.yaml           <- Defining the data pipeline stages, dependencies, and outputs.
│
├── 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
│   └── metrics.txt             <- Relevant metrics after evaluating the model.
│   └── training_metrics.txt    <- Relevant metrics from training the model.
│
├── 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
│   │   ├── __init__.py
│   │   └── make_dataset.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── __init__.py
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       ├── __init__.py
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

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