update: star readme
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# STAR
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# 🌟 STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
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This is the project containing source code for the EMNLP 2022 paper "**STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing***"*
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<p align="center">
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<a href="./LICENSE"><img src="https://img.shields.io/badge/license-MIT-red.svg">
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</a>
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<a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects/tapex">
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<img alt="🤗 transformers support" src="https://img.shields.io/badge/🤗 transformers-master-green" />
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</a>
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<a href="support os"><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg">
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</a>
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<a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg">
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</a>
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<br />
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</p>
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This is the official project containing source code for the EMNLP 2022 paper "STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing"
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You can use our checkpoint to evaluation directly or train from scratch with our instructions.
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@ -10,8 +23,17 @@ You can use our checkpoint to evaluation directly or train from scratch with our
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The relevant models and data involved in the paper can be downloaded through [Baidu Netdisk](https://pan.baidu.com/s/1uA63h4zpwyDSqY5cprbeJQ?pwd=6666), or downloaded through Google Drive in the corresponding folder.
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## Citation
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```
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@article{cai2022star,
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title={STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing},
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author={Cai, Zefeng and Li, Xiangyu and Hui, Binyuan and Yang, Min and Li, Bowen and Li, Binhua and Cao, Zheng and Li, Weijie and Huang, Fei and Si, Luo and others},
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journal={arXiv preprint arXiv:2210.11888},
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year={2022}
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}
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```
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## Pretrain
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## 🪜 Pretrain
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### Create conda environment
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@ -49,7 +71,7 @@ python save_model.py
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Then you can get the trained model and its configuration (at least containing `model.bin` and `config.json`) under `pretrained/sss` direction.
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## Finetuning
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## 🚪 Fine-tuning and Evaluation
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This section presents the results on CoSQL and SParC datasets with STAR fine-tuned with LGESQL.
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@ -70,44 +92,46 @@ Create conda environment `lgesql`:
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python -c "import stanza; stanza.download('en')"
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python -c "import nltk; nltk.download('stopwords')"
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```
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## Using our checkpoint to evaluation:
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### Using our checkpoint to evaluation:
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- Download our processed datasets [CoSQL](https://drive.google.com/file/d/1suuQnHVPxZZKRiUBvsUIlw7BnY21Q_6u/view?usp=sharing) or [SParC](https://drive.google.com/file/d/1DrGBq7WGdieanq90TjkiO5JgZMwcDGUu/view?usp=sharing) and unzip them into the `cosql/data` and `sparc/data` respectively. Make sure the datasets are correctly located as:
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```
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data
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├── database
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├── dev_electra.json
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├── dev_electra.bin
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├── dev_electra.lgesql.bin
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├── dev_gold.txt
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├── label.json
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├── tables_electra.bin
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├── tables.json
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├── train_electra.bin
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├── train_electra.json
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└── train_electra.lgesql.bin
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```
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```
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data
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├── database
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├── dev_electra.json
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├── dev_electra.bin
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├── dev_electra.lgesql.bin
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├── dev_gold.txt
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├── label.json
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├── tables_electra.bin
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├── tables.json
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├── train_electra.bin
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├── train_electra.json
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└── train_electra.lgesql.bin
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```
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- Download our processed checkpoints [CoSQL](https://drive.google.com/file/d/1y4edJJ2xoA_JUGCoegEd8xLopAaUuvmp/view?usp=sharing) or [SParC](https://drive.google.com/file/d/1UDs956PgVlZT1hZ4pRm3Mox3Hs5u42sF/view?usp=sharing) and unzip them into the `cosql/checkpoints` and `sparc/checkpoints` respectively. Make sure the checkpoints are correctly located as:
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```
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checkpoints
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├── model_IM.bin
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└── params.json
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```
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```
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checkpoints
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├── model_IM.bin
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└── params.json
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```
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- Execute the following command and the results are recorded in result_XXX.txt(it will take 10 to 30 minutes on one Tesla V100-PCIE-32GB GPU):
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```
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sh run/run_evaluation.sh
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```
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## Train from scratch
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### Train from scratch
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- You can train STAR yourself by following the process in the `pretrain` file or download our pre-trained [STAR](https://drive.google.com/file/d/1zfvNpofVzLixzzFyqLO0NP-WQSKKENIC/view?usp=sharing) and unzip it into the `pretrained_models/sss` directory. Make sure the STAR are correctly located as:
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```
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pretrained_models
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└── sss
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```
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pretrained_models
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└── sss
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├── config.json
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├── pytorch_model.bin
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└── vocab.txt
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```
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```
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- You can preprocess the data with the `process_data&&label.py` file and refer to the methods in LGESQL, or download our processed data as described above directly.
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- Traning:
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(it will take 4 days on one Tesla V100-PCIE-32GB GPU)
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```
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sh run/run_lgesql_plm.sh
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```
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