This repo contains the code for NL2SQL paper titled "Data Agnostic RoBERTa-based Natural Language to SQL Query Generation". Our code was based on the previous works introduced in the paper "Content Enhanced BERT-based Text-to-SQL Generation", the code to which can be found here. Feel free to experiment with the models.
Our work was mainly done in a Google Colab workspace with a GPU runtime accelerator, in order to reproduce the results we would recommend running the code in a similar workspace. Thus we have created a notebook which can be found at: https://colab.research.google.com/drive/1qYJTbbEXYFVdY6xae9Zmt96hkeW8ZFrn Below are the step by step instructions to run the models:
- Visit https://drive.google.com/drive/folders/13f2MrdpieC9QGXM_DJnj2f1Hs6ZBh2ZT?usp=sharing. This Drive contains all the datasets and pretrained model weights. Add a shortcut to your drive. This step is important.
- Open the notebook, the link to which is provided above.
- Make sure the runtime accelerator is set to GPU.
- Mount your Google Drive and clone this repository in the runtime environment, the code for this step has already been provided in the notebook.
- The Notebook is now ready for use.
All the package requirements have been mentioned in the notebook.
| Dev Logical Form Acc | Dev Execution Accuracy | Test Logical Form Accuracy | Test Execution Accuracy |
|---|---|---|---|
| 69.4% | 77.0% | 68.9% | 76.7% |