SWE-bench_bm25_40K

# Dataset Card for "SWE-bench_bm25_40K"
### 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.

The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770)

This dataset `SWE-bench_bm25_40K` includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 40,000 `cl100k_base` tokens from the [`tiktoken`](https://github.com/openai/tiktoken) tokenization package used for OpenAI models.
The `text` column can be used directly with LMs to generate patch files.
Models are instructed to generate [`patch`](https://en.wikipedia.org/wiki/Patch_(Unix)) formatted file using the following template:
```diff
<patch>
diff
--- a/path/to/file.py
--- b/path/to/file.py
@@ -1,3 +1,3 @@
This is a test file.
-It contains several lines.
+It has been modified.
This is the third line.
</patch>
```

This format can be used directly with the [SWE-bench inference scripts](https://github.com/princeton-nlp/SWE-bench/tree/main/inference). Please refer to these scripts for more details on inference.

### 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.
text: (str) - The input text including instructions, the "Oracle" retrieved file, and an example of the patch format for output.
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.
```

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

# SWE-bench_bm25_40K 数据集卡片
### 数据集概述
SWE-bench是一款用于评测系统自动解决GitHub议题能力的数据集。该数据集从12个主流Python项目中收集了2294个议题-拉取请求（Issue-Pull Request）对，评测环节采用单元测试验证的方式，以拉取请求（PR）合并后的行为作为参考解决方案。

该数据集随论文《SWE-bench: Can Language Models Resolve Real-World GitHub Issues?》（https://arxiv.org/abs/2310.06770）一同发布。

本`SWE-bench_bm25_40K`数据集按照论文所述的方式，使用Pyserini的BM25检索工具对每个样本进行格式化处理。其代码上下文长度限制为40000个`cl100k_base`词元（Token），该词元体系来自OpenAI模型所使用的[`tiktoken`](https://github.com/openai/tiktoken)分词包。

`text`列可直接与大语言模型（Large Language Model）配合使用，用于生成补丁文件。模型需按照以下模板生成[`patch`](https://en.wikipedia.org/wiki/Patch_(Unix))格式的文件：
diff
<patch>
diff
--- a/path/to/file.py
--- b/path/to/file.py
@@ -1,3 +1,3 @@
This is a test file.
-It contains several lines.
+It has been modified.
This is the third line.
</patch>

该格式可直接配合[SWE-bench推理脚本](https://github.com/princeton-nlp/SWE-bench/tree/main/inference)使用，如需了解推理流程的更多细节，请参考该脚本。

### 支持任务与排行榜
SWE-bench提出了一项全新任务：在给定完整代码仓库与GitHub议题的前提下完成议题修复。相关排行榜可访问www.swebench.com查看。

### 语言类型
本数据集的文本以英文为主，且未针对语言类型进行任何过滤或清洗操作。

## 数据集结构
### 样本字段说明
SWE-bench数据集的单条样本字段说明如下：

instance_id: (str) - 格式化后的样本标识符，通常格式为 repo_owner__repo_name-PR-number。
text: (str) - 输入文本，包含任务提示、「Oracle」检索到的文件，以及输出所需的补丁格式示例。
patch: (str) - 金标准补丁，即由拉取请求生成的、用于修复该议题的补丁（已剔除与测试相关的代码）。
repo: (str) - 来自GitHub的代码仓库所有者/名称标识符。
base_commit: (str) - 代码仓库的提交哈希值，代表解决方案拉取请求应用前的仓库HEAD状态。
hints_text: (str) - 在解决方案拉取请求的首次提交创建日期之前，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提交的测试文件补丁。
problem_statement: (str) - 议题的标题与正文内容。
version: (str) - 运行评测时所需使用的安装版本。
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，代表由该拉取请求修复并与议题解决相关的测试集合。
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。

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