metadata

title: t5s
emoji: 💯
colorFrom: yellow
colorTo: red
sdk: streamlit
app_file: src/visualization/visualize.py
pinned: false

t5s

T5 Summarisation Using Pytorch Lightning, DVC, DagsHub and HuggingFace Spaces

Here you will find the code for the project, but also the data, models, pipelines and experiments. This means that the project is easily reproducible on any machine, but also that you can contribute data, models, and code to it.

Have a great idea for how to improve the model? Want to add data and metrics to make it more explainable/fair? We'd love to get your help.

Installation

To use and run the DVC pipeline install the t5s package

pip install t5s

Usage

Firstly we need to clone the repo containing the code so we can do that using:

t5s clone

We would then have to create the required directories to run the pipeline

t5s dirs

Then we need to pull the models from DVC

t5s pull

Now to run the training pipeline we can run:

t5s run

Finally to push the model to DVC

t5s push

To push this model to HuggingFace Hub for inference you can run:

t5s upload

Next if we would like to test the model and visualise the results we can run:

t5s visualize

And this would create a streamlit app for testing