update: star readme
This commit is contained in:
出蛰
parent
b29637b237
commit
0746daf1c0
|
|
@ -1,6 +1,19 @@
|
|||
# STAR
|
||||
# 🌟 STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
|
||||
|
||||
This is the project containing source code for the EMNLP 2022 paper "**STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing***"*
|
||||
<p align="center">
|
||||
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-MIT-red.svg">
|
||||
</a>
|
||||
<a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects/tapex">
|
||||
<img alt="🤗 transformers support" src="https://img.shields.io/badge/🤗 transformers-master-green" />
|
||||
</a>
|
||||
<a href="support os"><img src="https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg">
|
||||
</a>
|
||||
<a href=""><img src="https://img.shields.io/badge/python-3.7+-aff.svg">
|
||||
</a>
|
||||
<br />
|
||||
</p>
|
||||
|
||||
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"
|
||||
|
||||
You can use our checkpoint to evaluation directly or train from scratch with our instructions.
|
||||
|
||||
|
|
@ -10,8 +23,17 @@ You can use our checkpoint to evaluation directly or train from scratch with our
|
|||
|
||||
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.
|
||||
|
||||
## Citation
|
||||
```
|
||||
@article{cai2022star,
|
||||
title={STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing},
|
||||
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},
|
||||
journal={arXiv preprint arXiv:2210.11888},
|
||||
year={2022}
|
||||
}
|
||||
```
|
||||
|
||||
## Pretrain
|
||||
## 🪜 Pretrain
|
||||
|
||||
|
||||
### Create conda environment
|
||||
|
|
@ -49,7 +71,7 @@ python save_model.py
|
|||
|
||||
Then you can get the trained model and its configuration (at least containing `model.bin` and `config.json`) under `pretrained/sss` direction.
|
||||
|
||||
## Finetuning
|
||||
## 🚪 Fine-tuning and Evaluation
|
||||
|
||||
This section presents the results on CoSQL and SParC datasets with STAR fine-tuned with LGESQL.
|
||||
|
||||
|
|
@ -70,44 +92,46 @@ Create conda environment `lgesql`:
|
|||
python -c "import stanza; stanza.download('en')"
|
||||
python -c "import nltk; nltk.download('stopwords')"
|
||||
```
|
||||
## Using our checkpoint to evaluation:
|
||||
### Using our checkpoint to evaluation:
|
||||
- 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:
|
||||
```
|
||||
data
|
||||
├── database
|
||||
├── dev_electra.json
|
||||
├── dev_electra.bin
|
||||
├── dev_electra.lgesql.bin
|
||||
├── dev_gold.txt
|
||||
├── label.json
|
||||
├── tables_electra.bin
|
||||
├── tables.json
|
||||
├── train_electra.bin
|
||||
├── train_electra.json
|
||||
└── train_electra.lgesql.bin
|
||||
```
|
||||
```
|
||||
data
|
||||
├── database
|
||||
├── dev_electra.json
|
||||
├── dev_electra.bin
|
||||
├── dev_electra.lgesql.bin
|
||||
├── dev_gold.txt
|
||||
├── label.json
|
||||
├── tables_electra.bin
|
||||
├── tables.json
|
||||
├── train_electra.bin
|
||||
├── train_electra.json
|
||||
└── train_electra.lgesql.bin
|
||||
```
|
||||
- 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:
|
||||
```
|
||||
checkpoints
|
||||
├── model_IM.bin
|
||||
└── params.json
|
||||
```
|
||||
```
|
||||
checkpoints
|
||||
├── model_IM.bin
|
||||
└── params.json
|
||||
```
|
||||
- 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):
|
||||
```
|
||||
sh run/run_evaluation.sh
|
||||
```
|
||||
|
||||
sh run/run_evaluation.sh
|
||||
|
||||
## Train from scratch
|
||||
### Train from scratch
|
||||
- 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:
|
||||
```
|
||||
pretrained_models
|
||||
└── sss
|
||||
├── config.json
|
||||
├── pytorch_model.bin
|
||||
└── vocab.txt
|
||||
```
|
||||
```
|
||||
pretrained_models
|
||||
└── sss
|
||||
├── config.json
|
||||
├── pytorch_model.bin
|
||||
└── vocab.txt
|
||||
```
|
||||
- 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.
|
||||
- Traning:
|
||||
(it will take 4 days on one Tesla V100-PCIE-32GB GPU)
|
||||
```
|
||||
sh run/run_lgesql_plm.sh
|
||||
```
|
||||
```
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue