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Official repo for ACL 2023 paper Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language.

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Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language

Official repo for paper Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language.

Environment Setup

conda env create -f environment.yml
conda activate code4struct

Data

Please refer to docs/DATA.md for detailed instructions.

Inference

You will need to obtain your API key from here.

export OPENAI_API_KEY="YOUR_API_KEY_HERE"
./src/scripts/model/batch-exp.sh

Evaluate generated results

./src/scripts/evaluation/eval-all-ace.sh ACE05-E/codex

Evaluation result for each experiment run will be saved to the corresponding output_dir (e.g., data/extraction/ace/inferred/ACE05-E/codex/v6.4-baseline+trigger+hierarchy-50shot-n1-t0.0).

Visualize Evaluation Result

You can also visualize evaluation result using localhost:8000 by running the following:

streamlit run --server.port 8000 src/scripts/evaluation/streamlit-viz.py

Citation

@article{wang2022code4struct,
  title={Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language},
  author={Wang, Xingyao and Li, Sha and Ji, Heng},
  journal={arXiv preprint arXiv:2210.12810},
  year={2022}
}

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Official repo for ACL 2023 paper Code4Struct: Code Generation for Few-Shot Structured Prediction from Natural Language.

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