This repository requires Python 3.5 or greater.
Example instructions to set up:
virtualenv -p python3 /path/to/venv
git clone https://github.com/rshin/seq2struct
cd seq2struct
pip install -e .
Required Python modules are specified in requirements.txt. This project currently uses PyTorch 0.4.
To train models for Spider, you also need the JVM to run Stanford CoreNLP (currently used for tokenization for GloVe embeddings).
To obtain the results in https://arxiv.org/abs/1906.11790, first download the Spider dataset and preprocess it:
- Download “Spider Dataset” from https://yale-lily.github.io/spider. You can use this bash function:
function gdrive_download () {
COOKIES=$(mktemp)
CONFIRM=$(wget --quiet --save-cookies ${COOKIES} --keep-session-cookies --no-check-certificate "https://docs.google.com/uc?export=download&id=$1" -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')
wget --content-disposition --load-cookies ${COOKIES} "https://docs.google.com/uc?export=download&confirm=$CONFIRM&id=$1"
rm -rf ${COOKIES}
}
like gdrive_download 11icoH_EA-NYb0OrPTdehRWm_d7-DIzWX
- Unzip it somewhere
- Run
bash data/spider-20190205/generate.sh /path/to/unzipped/spider - Run
python preprocess.py --config configs/spider-20190205/arxiv-1906.11790v1.jsonnet
Install Stanford CoreNLP:
- Download http://nlp.stanford.edu/software/stanford-corenlp-full-2018-10-05.zip
- Unzip it to
third_party/stanford-corenlp-full-2018-10-05
To train the model:
python train.py --config configs/spider-20190205/arxiv-1906.11790v1.jsonnet --logdir ../logs/arxiv-1906.11790v1
This should create a directory ../logs/arxiv-1906.11790v1.
To perform inference:
python infer.py --config configs/spider-20190205/arxiv-1906.11790v1.jsonnet --logdir ../logs/arxiv-1906.11790v1 --step <STEP NUMBER> --section val --beam-size 1 --output <PATH FOR INFERENCE OUTPUT>
To perform evaluation:
python eval.py --config configs/spider-20190205/arxiv-1906.11790v1.jsonnet --inferred <PATH FOR INFERENCE OUTPUT> --output <PATH FOR EVAL OUTPUT> --section val
To look at evaluation results:
>>> import json
>>> d = json.load(open('<PATH FOR EVAL OUTPUT>'))
>>> print(d['total_scores']['all']['exact']) # should be ~0.42
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent