Datasets:

openbmb
/

UltraData-Code

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README.md ADDED Viewed

	@@ -0,0 +1,203 @@

+---
+language:
+- en
+- zh
+license: apache-2.0
+size_categories:
+- n>100B
+task_categories:
+- text-generation
+pretty_name: UltraData-Code
+tags:
+- llm
+- code
+- code-pretraining
+- algorithmic-code
+- synthetic-data
+configs:
+- config_name: UltraData-Code-L2
+  data_files:
+  - split: cpp
+    path: "data/UltraData-Code-L2/cpp/*.parquet"
+  - split: cs
+    path: "data/UltraData-Code-L2/cs/*.parquet"
+  - split: go
+    path: "data/UltraData-Code-L2/go/*.parquet"
+  - split: java
+    path: "data/UltraData-Code-L2/java/*.parquet"
+  - split: js
+    path: "data/UltraData-Code-L2/js/*.parquet"
+  - split: php
+    path: "data/UltraData-Code-L2/php/*.parquet"
+  - split: py
+    path: "data/UltraData-Code-L2/py/*.parquet"
+  - split: r
+    path: "data/UltraData-Code-L2/r/*.parquet"
+  - split: rb
+    path: "data/UltraData-Code-L2/rb/*.parquet"
+  - split: rust
+    path: "data/UltraData-Code-L2/rust/*.parquet"
+  - split: sh
+    path: "data/UltraData-Code-L2/sh/*.parquet"
+- config_name: UltraData-Code-L3
+  data_files:
+  - split: cpp
+    path: "data/UltraData-Code-L3/cpp/*.parquet"
+  - split: cs
+    path: "data/UltraData-Code-L3/cs/*.parquet"
+  - split: go
+    path: "data/UltraData-Code-L3/go/*.parquet"
+  - split: java
+    path: "data/UltraData-Code-L3/java/*.parquet"
+  - split: js
+    path: "data/UltraData-Code-L3/js/*.parquet"
+  - split: php
+    path: "data/UltraData-Code-L3/php/*.parquet"
+  - split: py
+    path: "data/UltraData-Code-L3/py/*.parquet"
+  - split: r
+    path: "data/UltraData-Code-L3/r/*.parquet"
+  - split: rb
+    path: "data/UltraData-Code-L3/rb/*.parquet"
+  - split: rust
+    path: "data/UltraData-Code-L3/rs/*.parquet"
+  - split: sh
+    path: "data/UltraData-Code-L3/sh/*.parquet"
+default_config_name: UltraData-Code-L2
+---
+# UltraData-Code
+<div align="center">
+  <img src="assets/ultradata-code-logo.png" width="600" alt="UltraData-Code logo"/>
+</div>
+<p align="center">
+<a href="https://huggingface.co/collections/openbmb/ultradata">📦 UltraData Collection</a> |
+<a href="https://ultradata.openbmb.cn/">🌐 UltraData</a> |
+<a href="https://huggingface.co/collections/openbmb/minicpm5">🤗 MiniCPM5 Series</a> |
+📖 Tech Report (Coming Soon)
+</p>
+<p align="center">English | <a href="README_ZH.md">中文</a></p>
+## 📚 Introduction
+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.
+**L2: Algorithmically relevant code.** Across 11 programming languages, we select algorithmically relevant code (**ALGO**) from L1. Each file receives a semantic embedding. File-role annotations and language-specific heuristics provide dual-cue supervision. An MLP relevance classifier learns a continuous relevance score. File-role predictions and a code-quality threshold complete the final selection. The selector covers core algorithm and data structure implementations as well as computational and problem-solving logic in DATA, WEB, TOOL, and TEST files. L2 contains approximately **400B tokens** and retains about 12.23% of L1 files. Under the same 1B base model and 10B-token continual pre-training budget, L2 improves over L1 by 7.80 and 5.13 points on EvalPlus and MultiPL-E, and over Stack-Edu by 4.37 and 3.05 points.
+**L3: Task-oriented synthesis.** Using ALGO data from L2 as seeds, we construct a standalone task description, solution analysis, reference implementation, and test code. The task is independent of the original code snippet. The analysis explains the algorithm, edge cases, and complexity. The reference implementation provides executable code that runs independently. The tests target the required behavior. Each real implementation is transformed into a structured training sample aligning task, reasoning, code, and verification. L3 covers the same 11 programming languages and contains approximately **150B tokens**. Under the same 1B base model and 10B-token continual pre-training budget, replacing half of the L2 training tokens with L3 further improves EvalPlus and MultiPL-E by 8.42 and 8.07 points over L2-only training. With the same 1:1 token mix, L2-L3 mixed training improves over the L2 + CodeAlchemy-code-qa mix by 5.57 and 7.80 points on EvalPlus and MultiPL-E.
+## 📢 What's New
+- **[2026.09.07]** The [***UltraData-Code***](https://huggingface.co/datasets/openbmb/UltraData-Code) dataset is released! The 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. This release currently open-sources **L2** (~400B tokens) and **L3** (~150B tokens) across 11 programming languages. 🚀🚀🚀
+- **[2026.09.07]** ***[MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) is released!***, the second model in the MiniCPM5 series after MiniCPM5-1B. It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios. It reaches **2B-class open-source SOTA**, remains competitive with 4B-class models, and shows particular advantages in coding, mathematics, long-context understanding, tool use, and agentic tasks. UltraData-RL-2609 serves as the core RL dataset for MiniCPM5-2B. 🚀🚀🚀
+- **[2026.02.08]** The [***UltraData***](https://ultradata.openbmb.cn/) platform is now live, introducing the [L0-L4 tiered data management framework](https://arxiv.org/pdf/2602.09003).  🔍🔍🔍
+## 💡 Highlights
+> **Abstract:** Code generation is a core capability of large language models, and code data is central to the pretraining process that develops it. As code corpora grow, their scale, diversity, and quality increasingly shape the capabilities learned during pretraining. Following a tiered data management perspective, we present **UltraData-Code** as a family of four connected data states: repository archival at L0, standardized natural code at L1, algorithmic selection at L2, and task-oriented synthesis at L3. L0 archives the latest revision on the default branch of each public GitHub repository with its file structure, relationships, and provenance. L1 applies scalable filtering, normalization, and near deduplication to obtain standardized natural code. L2 selects algorithmically relevant files from L1 using language-adaptive signals from file roles and heuristics, together with code-quality constraints, yielding approximately 400B tokens across 11 programming languages. L3 applies task-oriented synthesis to algorithmic files from L2, turning each implementation into a structured programming exercise with aligned task, analysis, solution, and test fields, and generating approximately 150B tokens in the same 11 languages. Under controlled 10B-token continual pretraining of a 1B model, training on L2 instead of L1 raises pass@1 on EvalPlus by **7.80 points** and on MultiPL-E by **5.13 points**, while exceeding Stack-Edu by 4.37 and 3.05 points, respectively. Replacing half of the L2 training tokens with L3 yields a further gain of **8.42 points** on EvalPlus and **8.07 points** on MultiPL-E over training on L2 alone, while exceeding the strongest synthetic data baseline by 5.57 and 7.80 points, respectively.
+- **A connected code data construction pipeline:** UltraData-Code links repository archival and standardized natural code with fine-grained selection and structured synthesis, yielding approximately 400B tokens at L2 and 150B tokens at L3 across 11 programming languages.
+- **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.
+### Python Results
+- **Algorithmic selection yields the strongest natural code result.** UltraData-Code-L2-py improves the average EvalPlus pass@1 by **17.91 points** over UltraData-Code-L1-py and by **8.46 points** over Stack-Edu-py. Ultra-Stack-v3-py reaches the second-highest natural code average, while UltraData-Code-L2-py records the strongest average overall among the natural code settings.
+- **Structured synthesis improves the matched mixture.** The equal-token UltraData-Code-L2-py + UltraData-Code-L3-py mixture reaches a **46.43** average pass@1, **3.30 points** above training on L2 alone and **4.46 points** above the matched L2 + SwallowCode-v2 mixture.
+<div align="center">
+  <img src="assets/python-training-dynamics.png" alt="Python training dynamics" width="760"/>
+</div>
+<div align="center">
+  <img src="assets/python_results.png" alt="Python main results table" width="900"/>
+</div>
+### Multilingual Results
+- **L2 selection improves over L1 baselines.** UltraData-Code-L2 exceeds UltraData-Code-L1 by **7.80 points** on EvalPlus and **5.13 points** on MultiPL-E, and exceeds Stack-Edu by **4.37** and **3.05 points**, respectively.
+- **L3 synthesis provides a further gain.** Replacing half of the L2 training tokens with L3 yields **8.42** and **8.07 points** over training on L2 alone on EvalPlus and MultiPL-E. Under the same 1:1 token mix, UltraData-Code-L2-L3 exceeds the strongest reported synthetic baseline by **5.57** and **7.80 points**, respectively.
+- **The advantage persists during scaling.** At 100B tokens, the L2-L3 mixture reaches 57.06 on EvalPlus and 39.54 on MultiPL-E, exceeding training on L2 alone by 10.11 and 12.39 points and the matched CodeAlchemy-code-qa mixture by 10.86 and 9.60 points.
+<div align="center">
+  <img src="assets/multilingual-training-dynamics.png" alt="Multilingual training dynamics" width="760"/>
+</div>
+<div align="center">
+  <img src="assets/multilingual_results.png" alt="Multilingual main results table" width="900"/>
+</div>
+## 🧾 Data Formats
+### UltraData-Code-L2
+Each row represents one selected source file. The fields are:
+- `uuid`: unique file identifier.
+- `repo_name`: source repository name.
+- `relative_path`: file path relative to the repository.
+- `content`: source code text.
+- `category`: predicted file role, such as `ALGO`, `WEB`, `TOOL`, `DATA`, `TEST`, `CONFIG`, or `EXCLUDE`.
+- `algo_rel_score`: algorithmic relevance score in `[0, 1]`.
+- `quality_score`: code quality score in `[0, 10]`.
+### UltraData-Code-L3
+Each row is a programming exercise grounded in a real implementation:
+- `uuid`: unique file identifier.
+- `content`: serialized training content.
+- `content_format`: serialization format for `content`.
+- `raw_content`: original generated record before serialization.
+- `task`: standalone problem statement.
+- `analysis`: algorithm, edge cases, and complexity discussion.
+- `solution`: reference implementation that runs independently.
+- `test`: generated test candidates.
+- `full_content`: serialization containing all generated fields.
+- `full_content_format`: serialization format for `full_content`.
+## ❤️ Acknowledgements
+- The ***UltraData-Code-L2*** file-level semantic embeddings are computed with [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B).
+Thanks for their awesome work! Open source contributions make UltraData-Code possible! 🙌
+## 💳 License and Data Sources
+This project is released under the [Apache 2.0](./LICENSE) license. ***UltraData-Code*** is built from code in multiple public repositories (L2) and from model-generated task records derived from those files (L3). Users must also comply with the **LICENSE of each source repository**. Apache 2.0 does not override those terms.
+Public availability of a repository is not a grant of redistribution, commercial use, or training rights. Rights holders may request removal via the contact channel on the dataset page.
+The dataset should not contain plaintext secrets or unauthorized personal data. Please report sensitive content or takedown requests through the same channel.
+**No unauthorized unchanged redistribution:** Without prior written permission from the original authors (or this organization), any institution, organization, or third-party platform is strictly prohibited from directly reposting, mirroring, re-hosting, or commercially repackaging and republishing any artifacts of this project in any form.
+## 📖 Citation
+If you find **UltraData-Code** useful in your research, please consider citing:
+```bibtex
+@misc{ultradata_code,
+  title        = {{UltraData-Code}: From Raw Repositories to
+Algorithmically Dense and Task-Oriented Code Data},
+  author       = {Chengying Tu and Hengyu Zhao and Shuaikang Xue and Zhongming Qu and Jihao Zhou and Xinle Lin and Junshao Guo and Zixuan Fu and Qiang Ma and Jie Zhou and Chaojun Xiao and Hongfei Yan and Yudong Wang and Xu Han and Zhiyuan Liu and Maosong Sun},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/openbmb/UltraData-Code}}
+}
+```

README_ZH.md ADDED Viewed

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+# UltraData-Code
+<div align="center">
+  <img src="assets/ultradata-code-logo.png" width="600" alt="UltraData-Code 标志"/>
+</div>
+<p align="center">
+<a href="https://huggingface.co/collections/openbmb/ultradata">📦 UltraData 合集</a> |
+<a href="https://ultradata.openbmb.cn/">🌐 UltraData</a> |
+<a href="https://huggingface.co/collections/openbmb/minicpm5">🤗 MiniCPM5 系列</a> |
+📖 技术报告（即将发布）
+</p>
+<p align="center"><a href="README.md">English</a> | 中文</p>
+## 📚 简介
+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。
+**L2：算法相关代码精选层。** 面向 11 种编程语言，从 L1 中筛选算法相关代码（**ALGO**）。系统为每个文件预计算语义 embedding。文件角色标注与语言相关启发式规则共同构成双重监督信号。基于 MLP 的算法相关性分类器学习连续相关性分数，再结合文件角色分类和代码质量阈值完成最终选择。该方法不仅覆盖主要实现算法和数据结构的 ALGO 文件，也能识别 DATA、WEB、TOOL 和 TEST 等其他文件角色中的计算过程与问题求解逻辑。L2 规模约为 **400B tokens**，保留 L1 中约 12.23% 的文件。在相同的 1B 基座模型和 10B-token 持续预训练预算下，L2 相比 L1 在 EvalPlus 和 MultiPL-E 上分别提升 7.80 和 5.13 个百分点，相比 Stack-Edu 分别提升 4.37 和 3.05 个百分点。
+**L3：任务导向合成层。** 以 L2 中 ALGO 类别数据为种子，进行任务导向的结构化合成，构造独立任务描述、解题分析、参考实现和测试代码。生成的任务脱离原始代码片段独立成立。分析部分解释算法、边界情况和复杂度。参考实现提供自包含的可执行代码。测试代码验证任务所要求的行为。每个真实实现由此转化为任务、推理、代码和验证相互对齐的结构化训练样本。L3 覆盖 11 种编程语言，规模约为 **150B tokens**。在相同的 1B 基座模型和 10B-token 持续预训练预算下，将一半 L2 训练 token 替换为 L3 后，模型在 EvalPlus 和 MultiPL-E 上较仅使用 L2 的训练分别进一步提升 8.42 和 8.07 个百分点。在相同的 1:1 等 token 配比下，L2-L3 混合训练模型相比 L2 与 CodeAlchemy-code-qa 的混合训练模型，在 EvalPlus 和 MultiPL-E 上分别提升 5.57 和 7.80 个百分点。
+## 📢 最新动态
+- **[2026.09.07]** [***UltraData-Code***](https://huggingface.co/datasets/openbmb/UltraData-Code) 数据集正式发布！这是 [UltraData](https://ultradata.openbmb.cn/) [L0-L4 分级治理体系](https://arxiv.org/pdf/2602.09003) 的完整实现，覆盖 L0 至 L3 四个代码数据状态，各层对应不同的数据构建阶段。本次开源 **L2**（约 400B tokens）和 **L3**（约 150B tokens），覆盖 11 种编程语言。🚀🚀🚀
+- **[2026.09.07]** ***[MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B) 正式发布！*** 作为 MiniCPM5 系列在 MiniCPM5-1B 之后的第二款模型，这是一款沿用同一套训练配方放大的 2B 稠密 Transformer，面向端侧、本地部署与资源受限场景，达到 **2B 级开源 SOTA**，整体可与 4B 级模型竞争，并在代码、数学、长上下文、工具调用与 Agent 任务上相对同规模模型更具优势。UltraData-RL-2609 是 MiniCPM5-2B 的核心 RL 数据。🚀🚀🚀
+- **[2026.02.08]** [***UltraData***](https://ultradata.openbmb.cn/) 平台正式上线，并发布 [L0-L4 分级数据管理框架](https://arxiv.org/pdf/2602.09003)。🔍🔍🔍
+## 💡 亮点
+> **摘要：** 代码生成是大语言模型的核心能力，代码数据是培养该能力的预训练过程的重要组成部分。随着代码语料规模不断增长，其规模、多��性和质量日益决定模型在预训练中学习到的能力。遵循分级数据治理视角，我们将 **UltraData-Code** 构建为四个相互连接的数据状态：L0 仓库归档、L1 规范化自然代码、L2 算法相关性精选和 L3 面向任务的合成。L0 保留每个公开 GitHub 仓库默认分支的最新版本及其文件结构、文件关系和来源信息。L1 通过规模化过滤、规范化和近重复去重得到标准化自然代码。L2 基于文件角色、语言自适应启发式信号和代码质量约束，从 L1 中筛选算法相关文件，覆盖 11 种编程语言、约 400B tokens。L3 以 L2 中的算法相关文件为基础，将每个实现转换为任务、分析、解答和测试字段对齐的结构化编程练习，覆盖相同的 11 种语言、约 150B tokens。在相同的 1B 模型和 10B-token 持续预训练设置下，使用 L2 替代 L1 后，EvalPlus 的 pass@1 提升 **7.80 个百分点**，MultiPL-E 提升 **5.13 个百分点**，相比 Stack-Edu 分别提升 4.37 和 3.05 个百分点。将一半 L2 训练 token 替换为 L3 后，相比仅使用 L2 的训练，EvalPlus 进一步提升 **8.42 个百分点**，MultiPL-E 进一步提升 **8.07 个百分点**，相比最强的合成数据基线分别提升 5.57 和 7.80 个百分点。
+- **完整的代码数据构建流水线：** 将仓库归档和规范化自然代码与细粒度精选、结构化合成连接起来，在 11 种编程语言上分别形成约 400B tokens 的 L2 和约 150B tokens 的 L3。
+- **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。
+### Python 结果
+- **算法相关性精选带来最强的自然代码结果。** UltraData-Code-L2-py 的 EvalPlus 平均 pass@1 比 UltraData-Code-L1-py 提升 **17.91 个百分点**，比 Stack-Edu-py 提升 **8.46 个百分点**。Ultra-Stack-v3-py 在自然代码设置中取得第二高的平均分，而 UltraData-Code-L2-py 在自然代码设置中取得最高平均分。
+- **结构化合成提升等 token 混合训练效果。** UltraData-Code-L2-py 与 UltraData-Code-L3-py 的等 token 混合训练平均 pass@1 达到 **46.43**，比仅使用 L2 高 **3.30 个百分点**，比对应的 L2 + SwallowCode-v2 混合训练高 **4.46 个百分点**。
+<div align="center">
+  <img src="assets/python-training-dynamics.png" alt="Python 训练动态" width="760"/>
+</div>
+<div align="center">
+  <img src="assets/python_results.png" alt="Python 主结果表" width="900"/>
+</div>
+### 多语言结果
+- **L2 精选优于自然代码基线。** UltraData-Code-L2 相比 UltraData-Code-L1 在 EvalPlus 和 MultiPL-E 上分别提升 **7.80** 和 **5.13 个百分点**，相比 Stack-Edu 分别提升 **4.37** 和 **3.05 个百分点**。
+- **L3 合成带来进一步提升。** 将一半 L2 训练 token 替换为 L3 后，相比仅使用 L2 的训练，EvalPlus 和 MultiPL-E 分别提升 **8.42** 和 **8.07 个百分点**。在相同的 1:1 token 配比下，UltraData-Code-L2-L3 混合训练相比最强的已报告合成数据基线，两个基准分别提升 **5.57** 和 **7.80 个百分点**。
+- **优势在规模扩展中持续存在。** 在 100B tokens 训练规模下，L2-L3 混合训练在 EvalPlus 和 MultiPL-E 上分别达到 57.06 和 39.54，相比仅使用 L2 的训练分别高 10.11 和 12.39 个百分点，相比对应的 CodeAlchemy-code-qa 混合训练分别高 10.86 和 9.60 个百分点。
+<div align="center">
+  <img src="assets/multilingual-training-dynamics.png" alt="多语言训练动态" width="760"/>
+</div>
+<div align="center">
+  <img src="assets/multilingual_results.png" alt="多语言主结果表" width="900"/>
+</div>
+## 🧾 数据格式
+### UltraData-Code-L2
+每行对应一个精选源代码文件，字段包括：
+- `uuid`：文件唯一标识。
+- `repo_name`：来源仓库名称。
+- `relative_path`：相对仓库根目录的文件路径。
+- `content`：代码文本。
+- `category`：预测的文件角色，如 `ALGO`、`WEB`、`TOOL`、`DATA`、`TEST`、`CONFIG` 或 `EXCLUDE`。
+- `algo_rel_score`：算法相关性分数，范围为 `[0, 1]`。
+- `quality_score`：代码质量分数，范围为 `[0, 10]`。
+### UltraData-Code-L3
+每行是一个基于真实实现构建的编程练习，字段包括：
+- `uuid`：文件唯一标识。
+- `content`：序列化后的训练文本。
+- `content_format`：`content` 的序列化格式。
+- `raw_content`：序列化前的原始生成记录。
+- `task`：独立任务描述。
+- `analysis`：算法、边界情况与复杂度分析。
+- `solution`：自包含参考实现。
+- `test`：生成的测试候选。
+- `full_content`：包含全部生成字段的序列化文本。
+- `full_content_format`：`full_content` 的序列化格式。
+## ❤️ 致谢
+- ***UltraData-Code-L2*** 使用 [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) 计算文件级语义 embedding。
+感谢这些优秀开源工作，正是开源社区的贡献让 UltraData-Code 成为可能！🙌
+## 💳 许可与数据来源
+本项目按 [Apache 2.0](./LICENSE) 许可证发布。***UltraData-Code*** 来自多个公开代码仓库中的代码（L2），以及基于这些文件生成的任务记录（L3）。使用者还须遵守各源仓库的 **LICENSE**。Apache 2.0 不会覆盖或取代这些条款。
+仓库公开可访问不等于授予再分发、商用或训练许可。权利人可通过数据集页面的联系方式申请移除。
+本数据集不应包含明文密钥或未经授权的个人数据。如发现敏感内容或需要下架，请通过同一渠道报告。
+**禁止未经授权的原样转载：** 未经原作者（或本组织）事先书面许可，任何机构、组织或第三方平台严禁以任何形式直接转载、镜像、重新托管，或对项目产物进行商业化重新包装后再发布。
+## 📖 引用
+如果 **UltraData-Code** 对您的研究有帮助，请考虑引用：
+```bibtex
+@misc{ultradata_code,
+  title        = {{UltraData-Code}: From Raw Repositories to
+Algorithmically Dense and Task-Oriented Code Data},
+  author       = {Chengying Tu and Hengyu Zhao and Shuaikang Xue and Zhongming Qu and Jihao Zhou and Xinle Lin and Junshao Guo and Zixuan Fu and Qiang Ma and Jie Zhou and Chaojun Xiao and Hongfei Yan and Yudong Wang and Xu Han and Zhiyuan Liu and Maosong Sun},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/openbmb/UltraData-Code}}
+}
+```