Datasets:

openbmb
/

UltraData-Code

@@ -85,6 +85,10 @@ default_config_name: UltraData-Code-L2
 UltraData-Code is a complete implementation of the [UltraData](https://ultradata.openbmb.cn/) [L0-L4 tiered data management framework](https://arxiv.org/pdf/2602.09003). It covers four code data states from L0 through L3, with each level corresponding to a distinct construction stage. The pipeline starts from approximately **192 million** public GitHub repositories at L0. This release provides **UltraData-Code-L2** (\~**400B tokens**) and **UltraData-Code-L3** (\~**150B tokens**) across **11 programming languages**.
 **L0: Repository archival.** We preserve the latest revision on the default branch of approximately 192 million public [GitHub](https://github.com/) repositories, together with code, directory structure, file relationships, and provenance metadata. This archive provides complete raw data and traceability for subsequent processing.
 **L1: Standardized natural code.** Starting from L0, we perform large-scale filtering, cleaning, format normalization, and near-duplicate deduplication. General rules remove oversized files, invalid paths, unsupported text, and obvious anomalies. Checks tailored to each language and file type then normalize and clean the remaining content. For deduplication, files are partitioned by extension. MinHash signatures are generated and LSH retrieves near-duplicate candidates. Similarity edges define connected components. For groups containing multiple near-duplicates, the highest ranked 50% are retained. Files with no detected near duplicate are all kept. The result is a standardized natural code corpus.
@@ -107,10 +111,6 @@ UltraData-Code is a complete implementation of the [UltraData](https://ultradata
 - **Fine-grained curation of algorithmically relevant code:** We introduce file-level algorithmic relevance as a selection objective and combine file-role cues with language-specific heuristic evidence to identify computational content beyond files explicitly labeled as algorithm implementations.
 - **Task-oriented synthesis grounded in implementations:** Each selected implementation serves as the common source for a task, analysis, solution, and test record, preserving computational intent while adding task semantics and structured supervision.
-<div align="center">
-  <img src="assets/ultradata-code-l0-l3-overview.png" width="760" alt="UltraData-Code L0-L3 construction pipeline"/>
-</div>
 ## 📈 Evaluation Results
 The 10B-token comparisons use the same 1B base model, architecture, optimization settings, preprocessing, decontamination, and evaluation protocol.

 UltraData-Code is a complete implementation of the [UltraData](https://ultradata.openbmb.cn/) [L0-L4 tiered data management framework](https://arxiv.org/pdf/2602.09003). It covers four code data states from L0 through L3, with each level corresponding to a distinct construction stage. The pipeline starts from approximately **192 million** public GitHub repositories at L0. This release provides **UltraData-Code-L2** (\~**400B tokens**) and **UltraData-Code-L3** (\~**150B tokens**) across **11 programming languages**.
+<div align="center">
+  <img src="assets/ultradata-code-l0-l3-overview.png" width="760" alt="UltraData-Code L0-L3 construction pipeline"/>
+</div>
 **L0: Repository archival.** We preserve the latest revision on the default branch of approximately 192 million public [GitHub](https://github.com/) repositories, together with code, directory structure, file relationships, and provenance metadata. This archive provides complete raw data and traceability for subsequent processing.
 **L1: Standardized natural code.** Starting from L0, we perform large-scale filtering, cleaning, format normalization, and near-duplicate deduplication. General rules remove oversized files, invalid paths, unsupported text, and obvious anomalies. Checks tailored to each language and file type then normalize and clean the remaining content. For deduplication, files are partitioned by extension. MinHash signatures are generated and LSH retrieves near-duplicate candidates. Similarity edges define connected components. For groups containing multiple near-duplicates, the highest ranked 50% are retained. Files with no detected near duplicate are all kept. The result is a standardized natural code corpus.
 - **Fine-grained curation of algorithmically relevant code:** We introduce file-level algorithmic relevance as a selection objective and combine file-role cues with language-specific heuristic evidence to identify computational content beyond files explicitly labeled as algorithm implementations.
 - **Task-oriented synthesis grounded in implementations:** Each selected implementation serves as the common source for a task, analysis, solution, and test record, preserving computational intent while adding task semantics and structured supervision.
 ## 📈 Evaluation Results
 The 10B-token comparisons use the same 1B base model, architecture, optimization settings, preprocessing, decontamination, and evaluation protocol.

README_ZH.md CHANGED Viewed

@@ -17,6 +17,10 @@
 UltraData-Code 是基于 [UltraData](https://ultradata.openbmb.cn/) [L0-L4 分级治理体系](https://arxiv.org/pdf/2602.09003) 的一次完整实现，覆盖 L0 至 L3 的分级代码数据，各层对应不同的数据构建阶段。流水线从 L0 约 **1.92 亿** 个公开 GitHub 仓库出发；本次发布包含 11 种编程语言上的 **UltraData-Code-L2**（约 **400B tokens**）和 **UltraData-Code-L3**（约 **150B tokens**）。
 **L0：仓库归档层。** 从约 1.92 亿个公开 [GitHub](https://github.com/) 仓库中保留默认分支上的最新版本，以及相应的代码、目录结构、文件关系和来源信息，为后续处理提供完整的原始数据和溯源记录。
 **L1：规范化自然代码层。** 在 L0 基础上进行规模化过滤清洗、格式规范化和近重复去重。通用规则排除过大文件、无效路径、不支持的文本和明显异常内容。随后根据语言和文件类型执行针对性的质量检查、归一化与内容清理。去重阶段按文件扩展名划分数据分区，使用 MinHash 生成签名并通过 LSH 检索候选近重复文件。相似度边用于构建连通分量。对于包含多个近重复文件的文件组，保留排名靠前的 50%。未发现近重复项的文件全部保留，最终形成标准化的自然代码语料。
@@ -39,10 +43,6 @@ UltraData-Code 是基于 [UltraData](https://ultradata.openbmb.cn/) [L0-L4 分
 - **细粒度算法相关代码精选：** 将文件级算法相关性定义为选择目标，结合文件角色线索与语言特定启发式证据，识别超出显式算法实现标签范围的计算内容。
 - **面向任务且以实现为基础的合成：** 以每个精选实现作为任务、分析、解答和测试记录的共同来源，在保留计算意图的同时补充任务语义和结构化监督。
-<div align="center">
-  <img src="assets/ultradata-code-l0-l3-overview.png" width="760" alt="UltraData-Code L0-L3 构建流程"/>
-</div>
 ## 📈 评测结果
 10B-token 对比均使用相同的 1B 基座模型、模型架构、优化设置、预处理、去污染流程和评测协议。

 UltraData-Code 是基于 [UltraData](https://ultradata.openbmb.cn/) [L0-L4 分级治理体系](https://arxiv.org/pdf/2602.09003) 的一次完整实现，覆盖 L0 至 L3 的分级代码数据，各层对应不同的数据构建阶段。流水线从 L0 约 **1.92 亿** 个公开 GitHub 仓库出发；本次发布包含 11 种编程语言上的 **UltraData-Code-L2**（约 **400B tokens**）和 **UltraData-Code-L3**（约 **150B tokens**）。
+<div align="center">
+  <img src="assets/ultradata-code-l0-l3-overview.png" width="760" alt="UltraData-Code L0-L3 construction pipeline"/>
+</div>
 **L0：仓库归档层。** 从约 1.92 亿个公开 [GitHub](https://github.com/) 仓库中保留默认分支上的最新版本，以及相应的代码、目录结构、文件关系和来源信息，为后续处理提供完整的原始数据和溯源记录。
 **L1：规范化自然代码层。** 在 L0 基础上进行规模化过滤清洗、格式规范化和近重复去重。通用规则排除过大文件、无效路径、不支持的文本和明显异常内容。随后根据语言和文件类型执行针对性的质量检查、归一化与内容清理。去重阶段按文件扩展名划分数据分区，使用 MinHash 生成签名并通过 LSH 检索候选近重复文件。相似度边用于构建连通分量。对于包含多个近重复文件的文件组，保留排名靠前的 50%。未发现近重复项的文件全部保留，最终形成标准化的自然代码语料。
 - **细粒度算法相关代码精选：** 将文件级算法相关性定义为选择目标，结合文件角色线索与语言特定启发式证据，识别超出显式算法实现标签范围的计算内容。
 - **面向任务且以实现为基础的合成：** 以每个精选实现作为任务、分析、解答和测试记录的共同来源，在保留计算意图的同时补充任务语义和结构化监督。
 ## 📈 评测结果
 10B-token 对比均使用相同的 1B 基座模型、模型架构、优化设置、预处理、去污染流程和评测协议。