mRAT-SQL-FIT - A Multilingual Translator to SQL with Database Schema Pruning to Improve Self-Attention

Code and model from the paper paper published in Springer-Nature - International Journal of Information Technology, here the SharedIt link.

mRAT-SQL+GAP - Multilingual version of the RAT-SQL+GAP

Code and model from our BRACIS 2021 paper published in Springer Lecture Notes in Computer Science, here the pre-print in arXiv.

Based on: RAT-SQL+GAP: Github. Paper: AAAI 2021 paper

Abstract

mRAT-SQL+GAP is a multilingual version of the RAT-SQL+GAP, wich start with Portuguese Language. Here is available the code, dataset and the results.

Directory Structure

Go to the directory where you want to install the structure

git clone https://github.com/C4AI/gap-text2sql
cd gap-text2sql/mrat-sql-gap 

Download Spider Dataset

Go to you browser and download:

https://drive.google.com/uc?id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0

Put the spider.zip file into the directory: gap-text2sql/mrat-sql-gap

Conda mtext2slq Environment Setup

conda create --name mtext2sql python=3.7
conda activate mtext2sql
conda install pytorch=1.5 cudatoolkit=10.2 -c pytorch
pip install -U "huggingface_hub[cli]"
pip install hf-transfer
export HF_HUB_ENABLE_HF_TRANSFER=0
pip install gdown
conda install -c conda-forge jsonnet
pip install -r requirements.txt
python -c "import nltk; nltk.download('stopwords'); nltk.download('punkt')"
conda install jupyter notebook
conda install -c conda-forge jupyter_contrib_nbextensions

Setup Script

Just run this script below, it will copy the datasets. The original version of the Spider dataset is distributed under the CC BY-SA 4.0 license. The modified versions (translated to Portuguese, Spanish, French, double-size(English and Portuguese) and quad-size (English, Portuguese, Spanish and French)) of train_spider.json, train_others.json, and dev.json are distributed under the CC BY-SA 4.0 license, respecting ShareAlike.

chmod +x setup.sh
./setup.sh

Specific setup

The models and checkpoints have big files (GBytes), so if you have enough disk space you can run all shell scripts. To understand how things work, run just BART_large.sh and after run the others.

./BART_large.sh
./mBART50MtoM-large.sh
./mT5_large.sh
./BERTimbau-base.sh
./BERTimbau-large.sh

Environment Test

Now the environment is ready for training (fine-tune) and inferences. The training is very slow more than 60 hours for BART, BERTimbau, mBART50, and more than 28 hours for mT5. Therefore I recommend testing the environment with the inference.

Preprocess dataset

This preprocess step is necessary both for inference and for training. It will take some time, maybe 40 minutes. I will use the script for BART, but you can use the other, look the directory experiments/spider-configs

python run.py preprocess experiments/spider-configs/spider-BART-large-en-train_en-eval.jsonnet

You can see the files processed in the paths: data/spider-en/nl2code-1115,output_from=true,fs=2,emb=bart,cvlink

Inference

I will use the script for BART again. Note: We are making inferences using the checkpoint already trained (directory logdir) and defined in: experiments/spider-configs/spider-BART-large-en-train_en-eval.jsonnet logdir: "logdir/BART-large-en-train", and
eval_steps: [40300],

python run.py eval experiments/spider-configs/spider-BART-large-en-train_en-eval.jsonnet

You then get the inference results and evaluation results in the paths:

ie_dirs/BART-large-en-train/bart-large-en_run_1_true_1-step41000.infer

and

ie_dirs/BART-large-en-train/bart-large-en_run_1_true_1-step41000.eval.

Training

Execute if it is really necessary, if you want to fine-tune the model, this will take a long time. But if you have a good machine available and want to see different checkpoints in the logdir, do it.

python run.py train experiments/spider-configs/spider-BART-large-en-train_en-eval.jsonnet

You then get the training checkpoints in the paths: logdir/BART-large-en-train

Checkpoints and Inferences

The checkpoints are available here (ESM - Exact Set Matching Accuracy): Paper mRAT-SQL+GAP - Multilingual version of the RAT-SQL+GAP

• BART-large trained in English [, ESM all: 0.718]
  • Checkpoint: 40300
    • Inference - English: ESM all: 0.718 - Baseline
• BERTimbau-base trained in Portuguese
  • Checkpoint: 24100
    • Inference - Portuguese: ESM all: 0.417
• mBART50MtoM-large trained in English
  • Checkpoint 23100
    • Inference - English: ESM all: 0.651
• mBART50MtoM-large trained in Portuguese
  • Checkpoint 39100
    • Inference - Portuguese: ESM all: 0.588
• mBART50MtoM-large trained in English and Portuguese (together)
  • Checkpoint 41000
    • Inference - English: ESM all: 0.664
    • Inference - Portuguese: ESM all: 0.595 Best inferences in Portuguese
  • Checkpoint 21100
    • Inference - English: ESM all: 0.678 Best inferences in English
    • Inference - Portuguese: ESM all: 0.581

Future work of the paper mRAT-SQL+GAP

• BERTimbau-large trained in Portuguese
  • Checkpoint: 40100
    • Inference - Portuguese: ESM all: 0.418
• mBART50MtoM-large trained in English, Portuguese, Spanish and French (together) - just best inferences
  • Checkpoint: 39100
    • Inference - English: ESM all: 0.696
  • Checkpoint: 42100
    • Inference - Portuguese: ESM all pt: 0.626
    • Inference - Spanish: ESM all: 0.628
  • Checkpoint: 44100
    • Inference - French: ESM all: 0.649

Paper mRAT-SQL-FIT

• mT5-large trained in English 51Ksteps

  • Checkpoint: 50100
    • Inference - English: ESM all: 0.684

• mT5-large trained in English, Portuguese, Spanish and French (together) 51Ksteps - just best inferences

  • Checkpoint: 51100
    • Inference - English: ESM all: 0.715
  • Checkpoint: 42100
    • Inference - Portuguese: ESM all: 0.680
  • Checkpoint: 50100
    • Inference - Spanish: ESM all: 0.660
    • Inference - French: ESM all: 0.672

• mT5-large trained in English, Portuguese, Spanish and French (together) 120Ksteps - just best inferences

  • Checkpoint: 77500
    • Inference - English: ESM all: 0.718
  • Checkpoint: 85500
    • Inference - Portuguese: ESM all: 0.675
  • Checkpoint: 76500
    • Inference - Spanish: ESM all: 0.675
  • Checkpoint: 67500
    • Inference - French: ESM all: 0.681

• mT5-large trained in English, Portuguese, Spanish and French (together) FIT 120Ksteps - just best inferences

  • Checkpoint: 105100
    • Inference - English: (simplemma.load_data('en','pt','es','fr')): ESM all: 0.735 Best inferences in English
    • Inference - English: (simplemma.load_data('en'): ESM all: 0.736 Best inferences in English
  • Checkpoint: 102100
    • Inference - Portuguese: ESM all: 0.687
  • Checkpoint: 114100
    • Inference - Spanish: ESM all: 0.689
    • Inference - French: ESM all: 0.698

• mT5-large trained in English, Portuguese, Spanish and French (together) 2048TKs - 480Ksteps - just inference in English

  • Checkpoint: 290100
    • Inference - English: ESM all: 0.697

Other Best Results

• T5-v1_1-large trained in English FIT 150Ksteps

  • Checkpoint: 150300
    • Inference - English: ESM all: 0.736

• mT5-large trained in English, Portuguese, Spanish and French (together) + Non Linear Data Augmentation by rules for extra question 3enr-3ptr-3esr-3frr FIT 150Ksteps

  • Checkpoint: 128100
    • Inference - English: ESM all: 0.726
  • Checkpoint: 125100
    • Inference - Portuguese: ESM all: 0.698
    • Inference - French: ESM all: 0.700
  • Checkpoint: 136100
    • Inference - Spanish: ESM all: 0.691

Results

All intermediate files of the results are in the directory inference-results.

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.