add README

README.md

@@ -0,0 +1,129 @@
+---
+language:
+- en
+- zh
+license: odc-by
+size_categories:
+- n>100B
+task_categories:
+- text-generation
+pretty_name: UltraData-Stack-v3-L2
+tags:
+- llm
+- code
+- code-pretraining
+- data-selection
+- algorithmic-code
+configs:
+- config_name: default
+  data_files:
+  - split: cpp
+    path: "data/cpp/*.parquet"
+  - split: cs
+    path: "data/cs/*.parquet"
+  - split: go
+    path: "data/go/*.parquet"
+  - split: java
+    path: "data/java/*.parquet"
+  - split: js
+    path: "data/js/*.parquet"
+  - split: php
+    path: "data/php/*.parquet"
+  - split: py
+    path: "data/py/*.parquet"
+  - split: r
+    path: "data/r/*.parquet"
+  - split: rb
+    path: "data/rb/*.parquet"
+  - split: rust
+    path: "data/rust/*.parquet"
+  - split: sh
+    path: "data/sh/*.parquet"
+---
+
+# UltraData-Stack-v3-L2
+
+<div align="center">
+  <img src="assets/ultradata-stack-v3-l2-logo.png" width="600" alt="UltraData-Stack-v3-L2 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/datasets/openbmb/UltraData-Code">🤗 UltraData-Code</a> |
+<a href="https://huggingface.co/openbmb/UltraData-Code-L2-Classifier">🤗 UltraData-Code-L2 Classifier</a>
+</p>
+
+<p align="center">English | <a href="README_ZH.md">中文</a></p>
+
+## 📚 Introduction
+
+**UltraData-Stack-v3-L2** is a selected code corpus of approximately 240B tokens for the 11 programming languages in [The Stack v3](https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train), obtained through quality-control cleaning and fine-grained selection with [UltraData-Code-L2-Classifier](https://huggingface.co/openbmb/UltraData-Code-L2-Classifier). This dataset transfers the language-adaptive L2 selection framework proposed by **UltraData-Code** to another source corpus, in order to examine whether file-level algorithm-related relevance selection can generalize to data sources beyond UltraData-Code-L1.
+
+Construction has two stages. First, source records are cleaned by removing URL-only entries, overly short records, and records containing garbled or malformed text. Second, a language-specific selector combines file-role prediction, algorithmic-relevance scoring, an `EXCLUDE` veto, and a shared code-quality constraint. The relevance threshold is calibrated separately for each language. The resulting dataset retains **20.10%** of the cleaned 11-language candidate pool, and is intended to concentrate interpretable computation and problem-solving logic while preserving broad language coverage.
+
+## 💡 Highlights
+
+- **Transferable algorithmic-relevance selection:** The same selection objective and model interfaces used for UltraData-Code-L2 are adapted to Stack-v3-train, with language-specific evidence and thresholds.
+- **Strong performance gains:** Under the same 1B base model and 10B-token continual-pretraining setup, UltraData-Stack-v3-L2 reaches **30.00** average pass@1 on EvalPlus and **22.75** on MultiPL-E, compared with **25.37** and **17.30** for Stack-v3-train.
+
+## 📊 Dataset Statistics
+
+Token counts are estimated with the tokenizer used in the technical report.
+
+| Language | After cleaning | After selection | Retention | Token share | Tokens |
+|:--|--:|--:|--:|--:|--:|
+| Python | 96,961,008 | 24,671,724 | 25.44% | 15.86% | 38.94B |
+| Java | 165,346,828 | 41,963,467 | 25.38% | 28.76% | 70.60B |
+| JavaScript | 110,967,790 | 14,521,755 | 13.09% | 12.92% | 31.73B |
+| C++ | 67,981,897 | 17,990,102 | 26.46% | 12.78% | 31.38B |
+| PHP | 65,565,149 | 5,500,141 | 8.39% | 10.26% | 25.19B |
+| Ruby | 32,667,620 | 6,389,441 | 19.56% | 2.39% | 5.86B |
+| Shell | 26,704,436 | 5,045,813 | 18.90% | 8.83% | 21.67B |
+| C# | 17,309,141 | 3,548,274 | 20.50% | 3.64% | 8.93B |
+| Go | 29,683,899 | 3,138,105 | 10.57% | 1.65% | 4.04B |
+| Rust | 11,674,463 | 2,907,231 | 24.90% | 2.13% | 5.24B |
+| R | 7,640,182 | 1,456,974 | 19.07% | 0.78% | 1.92B |
+| **Total** | **632,502,413** | **127,133,027** | **20.10%** | **100%** | **245.50B** |
+
+## 📈 Evaluation Results
+
+The 10B-token comparisons use the same 1B foundation model, training settings, decontamination, and evaluation protocol.
+
+**Python results:** UltraData-Stack-v3-L2-py ranks second among the evaluated L2 datasets by average EvalPlus pass@1 and obtains the highest MBPP and MBPP+ scores at L2.
+
+<div align="center">
+  <img src="assets/python_results.png" alt="Python main results table" width="900"/>
+</div>
+
+**Multilingual results:** UltraData-Stack-v3-L2 exceeds Stack-v3-train by **4.63 points** on EvalPlus and **5.45 points** on MultiPL-E, respectively, demonstrating effective L2 language-adaptive selection. 
+
+<div align="center">
+  <img src="assets/multilingual_results.png" alt="Multilingual main results table" width="900"/>
+</div>
+
+
+## ❤️ Acknowledgements
+
+UltraData-Stack-v3-L2 is built from [The Stack v3](https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train). The selection framework [UltraData-Code-L2-Classifier](https://huggingface.co/openbmb/UltraData-Code-L2-Classifier) is developed as part of [UltraData-Code](https://huggingface.co/datasets/openbmb/UltraData-Code), and its file-level representations are computed with [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B).
+
+Thanks to the authors and contributors of these open-source projects.
+
+## 💳 License
+
+The dataset is released under the [Open Data Commons Attribution License (ODC-By) v1.0](https://opendatacommons.org/licenses/by/1-0/), consistent with the source dataset [The Stack v3](https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train). Any use of all or part of the code gathered in UltraData-Stack-v3-L2 must abide by the terms of the original licenses, including attribution clauses where relevant.
+
+
+## 📖 Citation
+
+If you find this dataset 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

@@ -0,0 +1,84 @@
+# UltraData-Stack-v3-L2
+
+<div align="center">
+  <img src="assets/ultradata-stack-v3-l2-logo.png" width="600" alt="UltraData-Stack-v3-L2 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/datasets/openbmb/UltraData-Code">🤗 UltraData-Code</a> | 
+<a href="https://huggingface.co/openbmb/UltraData-Code-L2-Classifier">🤗 UltraData-Code-L2 分类器</a>
+</p>
+
+<p align="center"><a href="README.md">English</a> | 中文</p>
+
+## 📚 简介
+
+**UltraData-Stack-v3-L2** 是针对 [The Stack v3](https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train) 中的 11 种编程语言，经过质检清洗和 [UltraData-Code-L2-Classifier](https://huggingface.co/openbmb/UltraData-Code-L2-Classifier) 精筛得到的代码预训练语料，规模约为 **240B tokens**。该数据集将 *UltraData-Code* 提出的语言自适应 L2 精筛框架迁移到另一套源语料，用于检验文件级算法相关性筛选能否泛化到 UltraData-Code-L1 之外的数据来源。
+
+数据构建分为两个阶段。首先，基础清洗会移除全文仅包含 URL、长度过短以及含乱码或格式异常的记录。随后，针对每种语言分别运行 L2 选择器，综合文件角色预测、算法相关性评分、`EXCLUDE` 否决规则与统一的代码质量约束，并为每种语言单独校准算法相关性阈值。最终数据集保留清洗后 11 语言候选池的约 **20.10%**，旨在提高具有可解释计算过程和问题求解逻辑的代码密度，同时保持广泛的语言覆盖。
+
+## 💡 亮点
+
+- **可迁移的算法相关性精筛：** 将 UltraData-Code-L2 使用的统一选择目标和模型接口迁移至 Stack-v3-train，并针对不同语言适配识别证据与筛选阈值。
+- **实验结果表现突出：** 在相同的 1B 基座模型与 10B-token 持续预训练设置下，UltraData-Stack-v3-L2 在 EvalPlus 和 MultiPL-E 上的平均 pass@1 分别达到 **30.00** 和 **22.75**，而 Stack-v3-train 对应结果为 **25.37** 和 **17.30**。
+
+## 📊 数据集统计
+
+Token 数量使用技术报告实验所采用的 tokenizer 估算。
+
+| 语言 | 清洗后文件数 | 筛选后文件数 | 保留率 | Token 占比 | Tokens |
+|:--|--:|--:|--:|--:|--:|
+| Python | 96,961,008 | 24,671,724 | 25.44% | 15.86% | 38.94B |
+| Java | 165,346,828 | 41,963,467 | 25.38% | 28.76% | 70.60B |
+| JavaScript | 110,967,790 | 14,521,755 | 13.09% | 12.92% | 31.73B |
+| C++ | 67,981,897 | 17,990,102 | 26.46% | 12.78% | 31.38B |
+| PHP | 65,565,149 | 5,500,141 | 8.39% | 10.26% | 25.19B |
+| Ruby | 32,667,620 | 6,389,441 | 19.56% | 2.39% | 5.86B |
+| Shell | 26,704,436 | 5,045,813 | 18.90% | 8.83% | 21.67B |
+| C# | 17,309,141 | 3,548,274 | 20.50% | 3.64% | 8.93B |
+| Go | 29,683,899 | 3,138,105 | 10.57% | 1.65% | 4.04B |
+| Rust | 11,674,463 | 2,907,231 | 24.90% | 2.13% | 5.24B |
+| R | 7,640,182 | 1,456,974 | 19.07% | 0.78% | 1.92B |
+| **总计** | **632,502,413** | **127,133,027** | **20.10%** | **100%** | **245.50B** |
+
+## 📈 评测结果
+
+10B-token 对比均使用相同的 1B 基座模型、训练设置、去污流程和评测协议。
+
+**Python 结果：**在参与评测的 L2 数据中，UltraData-Stack-v3-L2-py 的 EvalPlus 平均 pass@1 位列第二，并在 L2 中取得最高的 MBPP 和 MBPP+ 分数。
+
+<div align="center">
+  <img src="assets/python_results.png" alt="Python main results table" width="900"/>
+</div>
+
+**多语言结果：**UltraData-Stack-v3-L2 在 EvalPlus 和 MultiPL-E 上分别比 Stack-v3-train 高 4.63 和 5.45 个百分点，验证了 L2 语言自适应筛选方法的有效性。
+
+<div align="center">
+  <img src="assets/multilingual_results.png" alt="Multilingual main results table" width="900"/>
+</div>
+
+## ❤️ 致谢
+
+UltraData-Stack-v3-L2 基于 [The Stack v3](https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train) 构建。选择框架来自 [UltraData-Code](https://huggingface.co/datasets/openbmb/UltraData-Code) 的 [UltraData-Code-L2-Classifier](https://huggingface.co/openbmb/UltraData-Code-L2-Classifier)，文件级表示使用 [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) 计算。
+
+感谢上述开源项目的作者与贡献者。
+
+## 💳 许可证
+
+本数据集基于 [Open Data Commons Attribution License (ODC-By) v1.0](https://opendatacommons.org/licenses/by/1-0/) 发布，与源数据集 [The Stack v3](https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train) 保持一致。使用 UltraData-Stack-v3-L2 中收录的全部或部分代码时，必须遵守其原始许可证的相关条款，包括适用的署名要求。
+
+## 📖 引用
+
+如果本数据集对您的研究有帮助，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}}
+}
+```