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---
title: T5-Summarisation
emoji: ✌
colorFrom: yellow
colorTo: red
sdk: streamlit
app_file: src/visualization/visualize.py
pinned: false
---
summarization
==============================
T5 Summarisation Using Pytorch Lightning
Instructions
------------
1. Clone the repo.
1. Edit the `params.yml` to change the parameters to train the model.
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. Process your data, train and evaluate your model using `make run`
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.
│
├── 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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