SWE-bench_bm25_50k_llama

### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.

### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com

### Languages

The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.

## Dataset Structure

### Data Instances
An example of a SWE-bench datum is as follows:

```
instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number.
patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue.
repo: (str) - The repository owner/name identifier from GitHub.
base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied.
hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date.
created_at: (str) - The creation date of the pull request.
test_patch: (str) - A test-file patch that was contributed by the solution PR.
Problem_statement: (str) - The issue title and body.
Version: (str) - Installation version to use for running evaluation.
environment_setup_commit: (str) - commit hash to use for environment setup and installation.
FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution.
PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application.
text: (str) - The generated text according to the retrieval criterion and the style-2 prompt found in [github:SWE-bench](https://github.com/princeton-nlp/SWE-bench).
input_ids: (List[int]) - The llama tokens for each text.
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 数据集摘要
SWE-bench 是一款用于评测各类系统自动解决GitHub议题能力的数据集。该数据集从12个主流Python开源仓库中收集了2294个议题-拉取请求（Issue-Pull Request）对。评估环节采用单元测试验证的方式，以拉取请求合并后的行为作为参考解决方案。

### 支持任务与排行榜
SWE-bench 提出了一项全新任务：在提供完整代码仓库与GitHub议题的前提下完成议题修复。官方排行榜可通过网址 www.swebench.com 访问。

### 语言分布
本数据集的文本主体为英文，且未针对语言类型进行任何过滤或清洗操作。

### 数据集结构
#### 数据实例
SWE-bench 的单条数据示例如下：

instance_id: (str) - 格式化的实例标识符，通常格式为「仓库所有者__仓库名称-PR编号」。
patch: (str) - 基准补丁，即由解决该议题的拉取请求生成的标准补丁（已剔除与测试相关的代码）。
repo: (str) - GitHub上的仓库所有者与名称标识符。
base_commit: (str) - 应用解决方案拉取请求前，代码仓库HEAD指向的提交哈希值。
hints_text: (str) - 在解决方案拉取请求的首次提交创建前，针对该议题所留下的评论。
created_at: (str) - 拉取请求的创建日期。
test_patch: (str) - 由解决方案拉取请求贡献的测试文件补丁。
Problem_statement: (str) - 议题的标题与正文内容。
Version: (str) - 执行评估时需使用的安装版本。
environment_setup_commit: (str) - 用于环境搭建与安装的提交哈希值。
FAIL_TO_PASS: (str) - JSON格式的字符串列表，代表由该拉取请求解决并与议题修复相关的测试集合。
PASS_TO_PASS: (str) - JSON格式的字符串列表，代表在拉取请求应用前后均应通过的测试集合。
text: (str) - 依据检索准则与[github:SWE-bench](https://github.com/princeton-nlp/SWE-bench)中定义的风格2提示生成的文本。
input_ids: (List[int]) - 对应每段文本的Llama Token序列。

[需补充更多信息](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)