datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SWE-smith
SWE-smith Dataset
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[12/14/2025] NOTE: We will no longer actively update this dataset.
While this dataset is still functional and usable, we recommend you use the `SWE-bench/SWE-smith-[lang]` datasets.
For better maintainability and ease-of-use, we are maintaining language-specific datasets in lieu of this mono-repo.
The SWE-smith Dataset is a training dataset of 50137 task instances from 128 GitHub repositories, collected using the SWE-smith toolkit.… See the full description on the dataset page: https://huggingface.co/datasets/SWE-bench/SWE-smith.SWE-smith-py
SWE-smith Dataset
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As of 12/14/2025, SWE-smith: Python contains 50908 task instances from 131 GitHub repositories
The SWE-smith Dataset is the largest open source dataset for training software engineering agents.
All SWE-smith task instances come with an executable environment.
To learn more about how to use this dataset to train Language Models for Software Engineering, please refer to the documentation.
SWE-smith-javaSWE-smith-pythonSWE-smith2SWE-smith-trajectories
SWE-smith Trajectories
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This dataset contains the 5017 trajectories we fine-tuned Qwen 2.5 Coder Instruct on, leading to
SWE-agent-LM-32B, a coding LM agent that
achieve 40.2% on SWE-bench Verified (no verifiers or multiple rollouts, just 1 attempt per instance).
Trajectories were generated by running SWE-agent + Claude 3.7 Sonnet on task instances from
the SWE-smith dataset.
SWE-smith-jsswesmith-cleanSWE-smith-tsSWE-smith-cppSWE-smith-go
SWE-smith Dataset
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As of 12/14/202, SWE-smith: Golang contains 8212 task instances from 87 GitHub repositories
The SWE-smith Dataset is the largest open source dataset for training software engineering agents.
All SWE-smith task instances come with an executable environment.
To learn more about how to use this dataset to train Language Models for Software Engineering, please refer to the documentation.
SWE-smith-mini_swe_agent_plus-trajectories-66k
Dataset: SWE-smith-mini_swe_agent_plus-trajectories-66k
A corpus of ~66k issue-solving trajectories collected with mini-swe-agent-plus on issues derived from SWE-smith. Each trajectory records the agent’s end-to-end process.
We training the Qwen3-8B model on different sizes of the training data. The results are shown in the figure, it could be observed that the solve rate on SWE-bench Verified improves approximately linearly with the logarithm of the data scale (1k → 66k… See the full description on the dataset page: https://huggingface.co/datasets/Kwai-Klear/SWE-smith-mini_swe_agent_plus-trajectories-66k.SWE-smith-oracleThis is a version of SWE-bench/SWE-smith filtered for non-empty problem_statement and formatted into the oracle setting of SWE-bench where the files edited by the patch are displayed to the agent. This problem presentation is made available in a text column, following the format of princeton-nlp/SWE-bench_Lite_oracle.
SWE-smith-rsSWE-smith-filteredFiltered dataset from SWE-bench/SWE-smith that the problem_statement is not empty for quick loading.
After removing the rows with empty problem_statement, it comes to 52945 -> 10924 rows (removed 42021 empty problem_statements)
SweSmith-RL-DatasetSWE-smith-trajectories-harborimport datasets
def map_to_harbor(example):
# in image name, change jyangballin/ for swesmith/
example['image_name'] = example['image_name'].replace('jyangballin/', 'swesmith/')
return example
smith = datasets.load_dataset('SWE-bench/SWE-smith', split='train')
smith = smith.map(map_to_harbor)
smith = datasets.DatasetDict({'train': smith})
smith.push_to_hub('ricdomolm/SWE-smith-trajectories-harbor')
SWE-smith-js-4259-filteredSWE-smith-phpSWE-smith-java-6704-filtered-for-problem-statementsSWE-smith-jsSWE-smith-filteredFiltered SWE-smith instances that have problem statements as of May 23rd, 2025. Also maximally compressed with categorical variables for low cardinality values.
swe_smith_js_5902_filteredharbor-swesmith-rl-artifacts
Harbor SWE-Smith 强化学习数据产物
本数据集是 Harbor Qwen 工具调用代码智能体强化学习项目使用的冻结任务集，服务于 GRPO、原生价值模型/GAE PPO、训练过程诊断和统一协议评测。
项目已于 2026 年 8 月 30 日完成 P0 评测并进入阶段性归档。本数据集用于保留实验所依赖的数据切分、任务执行文件和审计信息，不代表新的通用代码能力基准。
数据概况
切分
任务数
训练集
187
验证集
42
测试集
38
合计
267
数据覆盖 89 个上游代码仓库。三个切分之间同时执行任务标识和仓库级隔离检查。
正式数据集名称：
swesmith-curated-grpo-267-v1
冻结切分的语义摘要：
ae5df9a3f4a3fc8af44fac420b36529e283839e1bd3de9daba65d5bcda51447d
该值来自 split-manifest.json 的 sha256 字段，用于标识切分语义，不等同于该文件本身的字节级… See the full description on the dataset page: https://huggingface.co/datasets/keryszhan/harbor-swesmith-rl-artifacts.SWE-smith-py-39471-filtered-for-problem-statementsswe_smith_back_translationBack translate the swe-smith data to get the problem statment following the R2E sylte prompt. More details in https://github.com/SWE-bench/SWE-smith/issues/127
SWE-smith-trajectories-harbor-foundSWE-Smith
A extended version of the original SWE-smith-py dataset with more problem descriptions!
SWE-smith-java-6450-filteredSWE-Smith-Seeds-Clean
SWE-Smith Seeds, agent-verified
1,552 of SWE-smith's 59,136 instances, repackaged as terminal tasks and kept only where every claim about them was executed and held: the bug is present, the reference fix earns the grader's reward, the repository's own suite still passes, and a coding agent solved the task from its instruction alone in a sandbox that had neither the fix nor the tests nor the network. Every row carries the verdict and the conditions it was taken under; nothing… See the full description on the dataset page: https://huggingface.co/datasets/Fzz1/SWE-Smith-Seeds-Clean.