|
|
--- |
|
|
dataset_info: |
|
|
- config_name: edit |
|
|
features: |
|
|
- name: input |
|
|
dtype: string |
|
|
- name: target |
|
|
dtype: string |
|
|
- name: problem_id |
|
|
dtype: string |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 56166875 |
|
|
num_examples: 48386 |
|
|
- name: val |
|
|
num_bytes: 3336062 |
|
|
num_examples: 3338 |
|
|
- name: test |
|
|
num_bytes: 857857 |
|
|
num_examples: 794 |
|
|
download_size: 365069 |
|
|
dataset_size: 60360794 |
|
|
- config_name: generate |
|
|
features: |
|
|
- name: problem_id |
|
|
dtype: string |
|
|
- name: problem_description |
|
|
dtype: string |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 1793963 |
|
|
num_examples: 1262 |
|
|
- name: val |
|
|
num_bytes: 96855 |
|
|
num_examples: 69 |
|
|
- name: test |
|
|
num_bytes: 60776 |
|
|
num_examples: 49 |
|
|
download_size: 37588 |
|
|
dataset_size: 1951594 |
|
|
- config_name: generate_eval |
|
|
features: |
|
|
- name: problem_id |
|
|
dtype: string |
|
|
- name: runtimes |
|
|
sequence: float64 |
|
|
- name: memories |
|
|
sequence: float64 |
|
|
- name: num_sol |
|
|
dtype: int64 |
|
|
splits: |
|
|
- name: test |
|
|
num_bytes: 770704 |
|
|
num_examples: 48 |
|
|
download_size: 147211 |
|
|
dataset_size: 770704 |
|
|
configs: |
|
|
- config_name: edit |
|
|
data_files: |
|
|
- split: train |
|
|
path: edit/train-* |
|
|
- split: val |
|
|
path: edit/val-* |
|
|
- split: test |
|
|
path: edit/test-* |
|
|
- config_name: generate |
|
|
data_files: |
|
|
- split: train |
|
|
path: generate/train-* |
|
|
- split: val |
|
|
path: generate/val-* |
|
|
- split: test |
|
|
path: generate/test-* |
|
|
- config_name: generate_eval |
|
|
data_files: |
|
|
- split: test |
|
|
path: generate_eval/test-* |
|
|
--- |
|
|
# ECCO |
|
|
|
|
|
Dataset from the paper "ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?" |
|
|
|
|
|
 |
|
|
|
|
|
The dataset consists of 2 subsets `edit` and `generate` each with 3 splits (`train`, `val` and `test`). |
|
|
|
|
|
Code repository: [https://github.com/CodeEff/ECCO](https://github.com/CodeEff/ECCO) |
|
|
|
|
|
### Loading the dataset / benchmark |
|
|
```python |
|
|
dataset = load_dataset('CodeEff/ECCO', 'edit') # For history-based editing setting |
|
|
dataset = load_dataset('CodeEff/ECCO', 'generate') # For nl-instructed generation setting |
|
|
``` |
|
|
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. |
|
|
|
|
|
### Download the test cases |
|
|
```sh |
|
|
mkdir data && cd data |
|
|
wget https://huggingface.co/datasets/CodeEff/ECCO/resolve/main/test_cases.zip |
|
|
unzip test_cases.zip |
|
|
``` |
|
|
|
|
|
### Evaluation dataset |
|
|
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. |
|
|
This is used for the percentile evaluation of the **NL-instructed generation** paradigm. |
|
|
|
|
|
### Data Sources |
|
|
Dataset is sourced from [IBM CodeNet](https://github.com/IBM/Project_CodeNet) which consists of primarily competetive programming solutions. |
|
|
This is further filtered for efficiency and correctness as described in our paper. |
|
|
|
|
|
|
|
|
### Citation |
|
|
```bib |
|
|
@article{waghjale2024ecco, |
|
|
title={ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?}, |
|
|
author={Waghjale, Siddhant and Veerendranath, Vishruth and Wang, Zora Zhiruo and Fried, Daniel}, |
|
|
journal={arXiv preprint arXiv:2407.14044}, |
|
|
year={2024} |
|
|
} |
|
|
``` |
