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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. | |
| -------- | |
| <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> | |