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summarization
T5 Summarisation Using Pytorch Lightning
Instructions
- Clone the repo.
- Edit the
params.yml
to change the parameters to train the model. - Run
make dirs
to create the missing parts of the directory structure described below. - Optional: Run
make virtualenv
to create a python virtual environment. Skip if using conda or some other env manager.- Run
source env/bin/activate
to activate the virtualenv.
- Run
- Run
make requirements
to install required python packages. - Process your data, train and evaluate your model using
make run
- 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.
│
│
├── 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.