anshuKr/

sentence-compression

The Project is designed around several scripts that simulate a typical machine learning workflow. Starting with data preparation after preparing data, training model, evaluation and inference model. google/t5-small model was being trained on above dataset for 10 epochs. Later inference ran on evaluation data, performance metrics and evaluation results were stored inside result subdirectory of project directory.

I added Makefile which can be used to run python scripts separately using following bash commands.

run is a bash command which can aggregately run entire project.

clean is a bash command which can be used to clean the previous runs.

Performance metrics stores into performance.json file inside results directory.

And also, eval_results.csv containing predictions of evaluation file.

Download trained checkpoint from given drive link checkpoint