Upload folder using huggingface_hub

README.md

@@ -0,0 +1,27 @@
+# Nemotron-3-Nano-RL-Training-Blend (STEM & Math Translation)
+
+This dataset is a translated, parallel version of the STEM and Math subsets from the [nvidia/Nemotron-3-Nano-RL-Training-Blend](https://huggingface.co/datasets/nvidia/Nemotron-3-Nano-RL-Training-Blend) dataset.
+
+## Dataset Origin & Sampling
+The original NVIDIA dataset blends multiple sources. For this parallel corpus, we sampled **1/3** of the STEM and Math subsets:
+
+| Subset / Source Dataset | Target Filename | English (`en`) | Cantonese (`yue`) | Traditional Chinese (`zh-hk`) |
+| :--- | :--- | :---: | :---: | :---: |
+| **BytedTsinghua-SIA/DAPO-Math-17k** (0.10 ratio) | `nano_v3_sft_profiled_dapo17k.jsonl` | 2,572 | 2,534 | 2,542 |
+| **Skywork/Skywork-OR1-RL-Data** (0.12 ratio) | `nano_v3_sft_profiled_skywork_no_omni.jsonl` | 2,998 | 2,975 | 2,979 |
+| **nvidia/Nemotron-RL-knowledge-mcqa** (0.20 ratio) | `nano_v3_sft_profiled_stem_mcqa.jsonl` | 2,639 | 2,850 | 2,690 |
+
+## Languages
+For each subset, files are provided in three parallel directories:
+- `en/`: English (Source / Cleaned)
+- `yue/`: Cantonese (Translated)
+- `zh-hk/`: Traditional Chinese (Hong Kong) (Translated)
+
+## The Law Filter (STEM MCQA Cleaning)
+The original `nvidia/Nemotron-RL-knowledge-mcqa` dataset included multiple-choice questions from various general benchmarks. A significant number of these questions turned out to be related to US Law (e.g., Constitutional Law, Contracts, Civil Procedure, and Torts) rather than pure STEM knowledge.
+
+To clean the dataset:
+- We applied a keyword-based law filter (checking for terms like `刑事`, `法律`, `法例`, `憲法`, `州`, `合約`, `法院`, `聯邦`, `被告`, etc.) on the translations.
+- Any entry that triggered this filter was dropped from the Cantonese and Traditional Chinese translations.
+- To keep the three languages aligned, the corresponding English records for these law questions were also excluded.
+- This process successfully filtered out 2,333 law questions, leaving exactly 2,639 clean STEM multiple-choice questions in the final parallel release of `stem_mcqa`.