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README.md
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@@ -93,6 +93,13 @@ dataset = load_dataset('CodeEff/ECCO', 'generate') # For nl-instructed generatio
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```
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These are used to generate code by each model across the 2 paradigms. We use the `test` split for the evaluation/results and the `train` and `val` splits for finetuning and few-shot prompting.
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### Evaluation dataset
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The dataset also consists of an additional 3rd subset `generate_eval` which consists of the runtime and memory of a spectrum of user solutions for each problem in the `test` split.
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This is used for the percentile evaluation of the **NL-instructed generation** paradigm.
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### Citation
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```
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@article{waghjale2024ecco,
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title={ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?},
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author={Waghjale, Siddhant and Veerendranath, Vishruth and Wang, Zora Zhiruo and Fried, Daniel},
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```
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These are used to generate code by each model across the 2 paradigms. We use the `test` split for the evaluation/results and the `train` and `val` splits for finetuning and few-shot prompting.
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### Download the test cases
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```sh
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mkdir data && cd data
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wget https://huggingface.co/datasets/CodeEff/ECCO/resolve/main/test_cases.zip
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unzip test_cases.zip
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```
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### Evaluation dataset
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The dataset also consists of an additional 3rd subset `generate_eval` which consists of the runtime and memory of a spectrum of user solutions for each problem in the `test` split.
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This is used for the percentile evaluation of the **NL-instructed generation** paradigm.
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### Citation
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```bib
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@article{waghjale2024ecco,
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title={ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?},
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author={Waghjale, Siddhant and Veerendranath, Vishruth and Wang, Zora Zhiruo and Fried, Daniel},
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