T5-Summarization / README.md
Gagan Bhatia
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summarization
==============================
T5 Summarisation Using Pytorch Lightning
Instructions
------------
1. Clone the repo.
1. Run `make dirs` to create the missing parts of the directory structure described below.
1. *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.
1. Run `make requirements` to install required python packages.
1. Put the raw data in `data/raw`.
1. To save the raw data to the DVC cache, run `dvc commit raw_data.dvc`
1. Edit the code files to your heart's desire.
1. Process your data, train and evaluate your model using `dvc repro eval.dvc` or `make reproduce`
1. 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.
├── data
│   ├── processed <- The final, canonical data sets for modeling.
│   └── raw <- The original, immutable data dump.
├── eval.dvc <- The end of the data pipeline - evaluates the trained model on the test dataset.
├── 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`.
├── process_data.dvc <- Process the raw data and prepare it for training.
├── raw_data.dvc <- Keeps the raw data versioned.
├── 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
│   │   └── make_dataset.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
└── train.dvc <- Traing a model on the processed data.
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<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>