README.md

NL QL data augmentation

This repo served all codes related to data augmentation approach for NL-to-SQL tasks.

Components introduction

Data folder /data

Each subfolder under data holds the data for different datasets respectively. After entering the subfolder, the subsubfolders contain the data that stands for,

• data_aug for data augmentation files for scripts in src/data_augmentation
• generative for data augmentation files for scripts in src/syntehtic_data
• handmade_training_data for generated data to train / evaluate ValueNet
• original for containing data schemata for the database

Script folder /src

Folder src holds all scripts for the data augmentation. The following subfolders serve for different puposes,

• data_augmentation for data augmentation pipe with seeding data and training CRITIC model.

• synthetic_data for data augmentation pipe with data generative schema

• intermediate_representation, preprocessing and spider for all AST related helper files

• tools for all other helper files

Usage

Install dependencies and prerequisites

1. Run pip install -r requirements.txt to install all required dependencies

2. Sign up in openai.com and configure an API key in the page https://beta.openai.com/account/api-keys

3. Add the api key as the line shown below in .env file under the root folder of the project. If you haven't this file, please create it.

OPENAI_API_KEY=<api_key_from_openai>

Data augmentation based on shuffling on AST

with analytics-based re-ranking via sentence transformers

1. Prepare generative schema

   • Run te script shown as below

   python3 src/tools/transform_generative_schema.py --data_path <datapath>

   , where <datapath> is the path in data which contains the subfolder data/<datapath>/generative and original schema data/<datapath>/original/tables.json

2. Add more query types into src/synthetic_data/common_query_types if neccessary -- find out query types with

   python3 src/synthetic_data/group_paris_to_find_templates.py \
   --data_containing_semql <training_or_dev_json_file>

   -- Add query types found into file src/synthetic_data/common_query_types.py

3. Generate data

   • Write your own generating file as similar as src/synthetic_data/generate_synthetical_data_cordis (TODO: generalize this step for all dataset)

4. re-ranking data and generate handmade training data

   python3 src/synthetic_data/apply_sentence_embedding_reranking.py \
   --input_data_path <input_data_path> \
   --output_file <output_path> \
   --db_id <db_id>

Hyperparameters and hardware specifications in evaluation experiments

Evaluation on ValueNet

Hyperparameter	Selected Values
Pretrained model of encoder	bart-base
Seed	90
Dropout	0.3
Optimizer	Adam
Learning rate base	0.001
Beam size	1
Clip grad	5
Accumulated iterations	4
Batch size	4
Column attention	affine
Number of epochs	100
Hidden size	300
Attention vector size	300
Learning rate connection	0.0001
Max grad norm	1
Column embedding size	300
Column poniter	true
Learning rate of Transformer	2e-5
Max sequence length	1024
Scheduler gamma	0.5
Type embedding size	128
Action embedding size	128
Sketch loss weight	1
Decode max time step	80
Loss epoch threshold	70

Hardware specifications	Values
CPU count	8
GPU count	1
GPU type	nVidia V100
Total running time	ca. 12 days

Evaluation on T5-Large

Hyperparameter	Selected Values
Pretrained model	T5-Large
Dropout	0.1
Learning rate	base 0.0001
Optimizer	Adafactor
Clip grad	5
Accumulated iterations	4
Batch size	4
Gradient Accum.	135
Max sequence length	512
No of steps	6500

Hardware specifications	Values
CPU count	8
GPU count	1
GPU type	nVidia A100
Total running time	ca. 16 days

Evaluation on SmBoP

Hyperparameter	Selected Values
Pretrained model	GraPPa-Large
Dropout	0.1
Learning rate base	0.000186
Optimizer	Adam
RAT Layers	8
Beam Size	30
Batch size	16
Gradient Accum.	4
Max sequence length	512
Max steps	60000
Early Stopping	5 epochs

Hardware specifications	Values
CPU count	8
GPU count	1
GPU type	nVidia T4
Total running time	ca. 26 days

Contributing

License