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summarization
T5 Summarisation Using Pytorch Lightning
Instructions
- Clone the repo.
- 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. - Put the raw data in
data/raw
. - To save the raw data to the DVC cache, run
dvc commit raw_data.dvc
- Edit the code files to your heart's desire.
- Process your data, train and evaluate your model using
dvc repro eval.dvc
ormake reproduce
- 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.