Datasets:
Add task category and library name to dataset card
#3
by
nielsr
HF Staff
- opened
README.md
CHANGED
@@ -1,4 +1,8 @@
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---
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dataset_info:
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features:
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- name: instance_id
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@@ -94,10 +98,8 @@ configs:
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data_files:
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- split: test
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path: data/test-*
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license: cc-by-4.0
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---
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-
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# Dataset Summary
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SWE-rebench is a large-scale dataset designed to support training and evaluation of LLM-based software engineering (SWE) agents, building upon and expanding our earlier release, [SWE-bench-extra](https://huggingface.co/datasets/nebius/SWE-bench-extra). It is constructed using a fully automated pipeline that continuously extracts real-world interactive SWE tasks from GitHub repositories at scale, as detailed in our paper [SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents](https://arxiv.org/abs/2505.20411). The dataset currently comprises over 21,000 issue–pull request pairs from 3,400+ Python repositories, each validated for correctness through automated environment setup and test execution. A curated subset of these tasks also forms the basis of our continuously updated [SWE-rebench leaderboard](https://swe-rebench.com/leaderboard).
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@@ -162,5 +164,4 @@ The dataset is licensed under the Creative Commons Attribution 4.0 license. Howe
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archivePrefix={arXiv},
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primaryClass={cs.SE},
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url={https://arxiv.org/abs/2505.20411}
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-
}
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```
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---
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license: cc-by-4.0
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task_categories:
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- other
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library_name: datasets
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dataset_info:
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features:
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- name: instance_id
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data_files:
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- split: test
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path: data/test-*
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---
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# Dataset Summary
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SWE-rebench is a large-scale dataset designed to support training and evaluation of LLM-based software engineering (SWE) agents, building upon and expanding our earlier release, [SWE-bench-extra](https://huggingface.co/datasets/nebius/SWE-bench-extra). It is constructed using a fully automated pipeline that continuously extracts real-world interactive SWE tasks from GitHub repositories at scale, as detailed in our paper [SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents](https://arxiv.org/abs/2505.20411). The dataset currently comprises over 21,000 issue–pull request pairs from 3,400+ Python repositories, each validated for correctness through automated environment setup and test execution. A curated subset of these tasks also forms the basis of our continuously updated [SWE-rebench leaderboard](https://swe-rebench.com/leaderboard).
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archivePrefix={arXiv},
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primaryClass={cs.SE},
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url={https://arxiv.org/abs/2505.20411}
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}
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