Duplicate from nvidia/OpenCodeReasoning

Co-authored-by: Wasi Uddin Ahmad <[email protected]>

.gitattributes

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README.md

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+---
+license: cc-by-4.0
+size_categories:
+- 100K<n<1M
+pretty_name: OpenCodeReasoning
+dataset_info:
+- config_name: split_0
+  features:
+  - name: id
+    dtype: string
+  - name: input
+    dtype: string
+  - name: output
+    dtype: string
+  - name: source
+    dtype: string
+  - name: license
+    dtype: string
+  - name: dataset
+    dtype: string
+  - name: split
+    dtype: string
+  - name: difficulty
+    dtype: string
+  - name: solution
+    dtype: string
+  splits:
+  - name: split_0
+    num_bytes: 28108469190
+    num_examples: 567850
+- config_name: split_1
+  features:
+  - name: id
+    dtype: string
+  - name: input
+    dtype: string
+  - name: output
+    dtype: string
+  - name: source
+    dtype: string
+  - name: license
+    dtype: string
+  - name: dataset
+    dtype: string
+  - name: split
+    dtype: string
+  - name: difficulty
+    dtype: string
+  - name: solution
+    dtype: string
+  - name: index
+    dtype: string
+  splits:
+  - name: split_1
+    num_bytes: 4722811278
+    num_examples: 167405
+configs:
+- config_name: split_0
+  data_files:
+  - split: split_0
+    path: split_0/train-*
+- config_name: split_1
+  data_files:
+  - split: split_1
+    path: split_1/train-*
+task_categories:
+- text-generation
+tags:
+- synthetic
+---
+
+# OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
+
+## Data Overview
+
+OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming 
+questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
+
+- [Technical Report](https://arxiv.org/abs/2504.01943) - Discover the methodology and technical details behind OpenCodeReasoning.
+- [Github Repo](https://github.com/NVIDIA/NeMo-Skills) - Access the complete pipeline used to perform SFT.
+
+This dataset is ready for commercial/non-commercial use.
+
+## Data distribution
+
+- The CodeForces problems are sourced from http://codeforces.com.
+- The question collections are gathered from TACO (https://huggingface.co/datasets/BAAI/TACO), APPS (https://huggingface.co/datasets/codeparrot/apps), CodeContests 
+(https://huggingface.co/datasets/deepmind/code_contests), and open-r1/codeforces (https://huggingface.co/datasets/open-r1/codeforces). 
+- We do not include the test split of CodeContests and open-r1/codeforces.
+- The output responses are generated by R1.
+
+| Source         | # Question | # Sample   |
+|:---------------|:-----------|:-----------|
+| AIZU           | 2123       | 62,476     |
+| AtCoder        | 2043       | 47,222     |
+| CodeChef       | 3796       | 72,925     |
+| CodeForces     | 10069      | 386,948    |
+| Codewars       | 2493       | 34,326     |
+| GeeksForGeeks  | 2667       | 37,602     |
+| HackerEarth    | 2269       | 59,181     |
+| HackerRank     | 895        | 10,955     |
+| Kattis         | 1187       | 13,095     |
+| LeetCode       | 777        | 10,525     |
+| Total          | 28,319     | 735,255    |
+
+
+## Data Fields
+|Field|Type|Description|
+|:---|:---|:---|
+|id|string|A unique id for each question|
+|input|string|The input competitive programming question (split_0 only). For split_1, user needs to get the question based on the dataset/split/index fields.|
+|output|string|R1's response.|
+|solution|string|Only the code portion of R1's response.|
+|dataset|string|The name of the dataset from which this question is collected from (e.g., "apps", "taco", "code_contests")|
+|license|string|The license associated with the dataset (e.g., "mit", "apache-2.0", "cc-by-4.0")|
+|split|string|The name of the split of the dataset from which this question is collected from (e.g., "train", "valid", "test")|
+|source|string|The name of the competitive programming platform (e.g., CodeForces, CodeChef)|
+|difficulty|string|A difficulty label for the input question.|
+|index|string|An index to retrieve the input question from APPS/TACO dataset (only available for split_1).|
+
+
+## How to use it
+
+You can load the dataset with the following lines of code.
+
+```python
+from datasets import load_dataset
+
+ocr_ds_split_0 = load_dataset("nvidia/OpenCodeReasoning", "split_0")
+print(ocr_ds_split_0)
+DatasetDict({
+    split_0: Dataset({
+        features: ['id', 'input', 'output', 'source', 'license', 'dataset', 'split', 'difficulty', 'solution'],
+        num_rows: 567850
+    })
+})
+
+ocr_ds_split_1 = load_dataset("nvidia/OpenCodeReasoning", "split_1")
+print(ocr_ds_split_1)
+DatasetDict({
+    split_1: Dataset({
+        features: ['id', 'index', 'input', 'output', 'source', 'license', 'dataset', 'split', 'difficulty', 'solution'],
+        num_rows: 167405
+    })
+})
+
+datasets = {
+    "taco": load_dataset("BAAI/TACO"),
+    "apps": load_dataset("codeparrot/apps")
+}
+for item in tqdm(ocr_ds_split_1["train"]):
+    assert item["input"] == "-"
+    assert item["dataset"] in ["taco", "apps"]
+    item["input"] = datasets[item["dataset"]][item["split"]][int(item["index"])]["question"]
+```
+
+
+## Dataset Characterization
+
+** Data Collection Method<br>
+* [Hybrid: Automated, Synthetic] <br>
+
+** Labeling Method<be>
+* [Hybrid: Automated, Synthetic] <br>
+
+
+## License/Terms of Use
+
+This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0) available at https://creativecommons.org/licenses/by/4.0/legalcode.
+
+**Data Developer:** NVIDIA
+
+
+### Use Case: <br>
+Developers training LLMs to distill reasoning capabilities for code generation. <br>
+
+### Release Date:  <br>
+04/04/2025 <br>
+
+
+## Data Version
+1.0 (04/04/2025)
+
+
+## Intended use
+
+The OpenCodeReasoning Dataset is intended to be used by the community to continue to improve open models. The data may be freely used to train models. **However, for 
+each dataset an user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose**.
+
+
+## Ethical Considerations:
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. 
+
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+
+## Citation
+
+If you find the data useful, please cite:
+```
+@article{ahmad2025opencodereasoning,
+      title={OpenCodeReasoning: Advancing Data Distillation for Competitive Coding}, 
+      author={Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, Aleksander Ficek, Siddhartha Jain, Jocelyn Huang, Vahid Noroozi, Boris Ginsburg},
+      year={2025},
+      eprint={2504.01943},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2504.01943}, 
+}
+```

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