title	emoji	colorFrom	colorTo	sdk	app_file	pinned
T5-Summarisation	✌	yellow	red	streamlit	app.py	false

summarization

T5 Summarisation Using Pytorch Lightning

Instructions

1. Clone the repo.
2. Edit the params.yml to change the parameters to train the model.
3. Run make dirs to create the missing parts of the directory structure described below.
4. 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.
5. Run make requirements to install required python packages.
6. Process your data, train and evaluate your model using make run
7. 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.
│
├── 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.
│   └── 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
│
├── 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
│   │   └── process_data.py
│   │
│   ├── models         <- Scripts to train models 
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │   └── evaluate_model.py
│   │   └── 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
└── data.dvc          <- Traing a model on the processed data.

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