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diff --git a/README.md b/README.md
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+---
+license: cc-by-4.0
+size_categories:
+- 100K<n<1M
+pretty_name: OpenCodeReasoning-2
+dataset_info:
+- config_name: train
+  features:
+    - name: question
+      dtype: string
+    - name: r1_generation
+      dtype: string
+    - name: qwq_critique
+      dtype: string
+    - name: solution
+      dtype: string
+    - name: judgement
+      dtype: string
+    - name: pass_rate
+      dtype: string
+    - name: source
+      dtype: string
+    - name: license
+      dtype: string
+    - name: dataset
+      dtype: string
+    - name: split
+      dtype: string
+    - name: difficulty
+      dtype: string
+    - name: index
+      dtype: string
+    - name: id
+      dtype: string
+    - name: question_id
+      dtype: string
+  splits:
+    - name: python
+      num_bytes: 72296224095
+      num_examples: 1398166
+    - name: cpp
+      num_bytes: 44739645805
+      num_examples: 1174475
+configs:
+- config_name: train
+  data_files:
+    - split: python
+      path: train/python/train-*
+    - split: cpp
+      path: train/cpp/train-*
+task_categories:
+- text-generation
+tags:
+- synthetic
+---
+
+# OpenCodeReasoning-2: A Large-scale Dataset for Reasoning in Code Generation and Critique
+
+## Dataset Description
+
+OpenCodeReasoning-2 is the largest reasoning-based synthetic dataset to date for coding, comprising 1.4M samples in Python and 1.1M samples in C++ across 34,799 unique competitive programming questions. 
+OpenCodeReasoning-2 is designed for supervised fine-tuning (SFT) tasks of code completion and code critique.
+
+- [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 solution responses are generated by R1 and critique responses are generated by QwQ.
+
+### Python
+| Source         | # Question | #Sample      |
+|:---------------|:-----------|:-------------|
+| AIZU           | 2150       | 71,681       |
+| AtCoder        | 2080       | 64,468       |
+| CodeChef       | 3869       | 120,040      |
+| CodeForces     | 15641      | 834,523      |
+| Codewars       | 2506       | 79,771       |
+| GeeksForGeeks  | 2670       | 58,154       |
+| HackerEarth    | 2285       | 73,559       |
+| HackerRank     | 912        | 26,106       |
+| Kattis         | 1235       | 39,938       |
+| LeetCode       | 777        | 29,926       |
+| Total          | 34,125     | 1,398,166    |
+
+### C++
+| Source         | # Question | #Sample      |
+|:---------------|:-----------|:-------------|
+| AIZU           | 2067       | 35,471       |
+| AtCoder        | 1988       | 62,493       |
+| CodeChef       | 3830       | 171,882      |
+| CodeForces     | 11887      | 355,180      |
+| Codewars       | 2492       | 155,162      |
+| GeeksForGeeks  | 2668       | 167,610      |
+| HackerEarth    | 2273       | 82,765       |
+| HackerRank     | 903        | 43,867       |
+| Kattis         | 1209       | 49,699       |
+| LeetCode       | 775        | 50,346       |
+| Total          | 30,092     | 1,174,475    |
+
+
+## Data Fields
+|Field|Type|Description|
+|:---|:---|:---|
+|id|string|A unique id for each data instance|
+|question_id|string|A unique id for each question|
+|question|string|The input competitive programming question. We leave it blank. Check the how-to-use-it section to get them.|
+|r1_generation|string|R1's response.|
+|qwq_critique|string|QwQ's response.|
+|solution|string|Only the code portion of R1's response.|
+|judgement|string|Only the judgement (right/wrong) from QwQ's response.|
+|pass_rate|float|Value in range [0, 1] or -1 (not enough unit tests or execution system couldn't validate).|
+|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 train-extra split).|
+
+
+## How to use it
+
+```
+from tqdm import tqdm
+from datasets import load_dataset
+
+hf_datasets = {
+    "taco": load_dataset("BAAI/TACO", trust_remote_code=True),
+    "apps": load_dataset("codeparrot/apps", trust_remote_code=True),
+    "code_contests": load_dataset("deepmind/code_contests"),
+    "open-r1/codeforces": load_dataset("open-r1/codeforces")
+}
+
+
+def get_question(ds_name, split, index):
+    benchmark = hf_datasets[ds_name][split][int(index)]
+    if ds_name == "code_contests":
+        if not benchmark["description"]:
+            return None
+        return benchmark["description"]
+    elif ds_name in ["taco", "apps"]:
+        return benchmark["question"]
+    elif ds_name == "open-r1/codeforces":
+        if not benchmark["description"]:
+            return None
+        question = benchmark["description"]
+        if benchmark["input_format"]:
+            question += "\n\nInput\n\n" + benchmark["input_format"]
+        if benchmark["output_format"]:
+            question += "\n\nOutput\n\n" + benchmark["output_format"]
+        if benchmark["examples"]:
+            question += "\n\nExamples"
+            for example in benchmark["examples"]:
+                if "input" in example:
+                    question += "\n\nInput\n\n" + example["input"]
+                if "output" in example:
+                    question += "\n\nOutput\n\n" + example["output"]
+        if benchmark["note"]:
+            question += "\n\nNote\n\n" + benchmark["note"]
+        return question
+
+    return None
+
+
+ocr2_dataset = load_dataset("nvidia/OpenCodeReasoning-2")
+for ocr2_ds in [ocr2_dataset["python"], ocr2_dataset["cpp"]]:
+    for ocr2_ds_item in tqdm(ocr2_ds):
+        assert ocr2_ds_item["dataset"] in ["taco", "apps", "code_contests", "open-r1/codeforces"]
+        ds_name, ds_split, ds_index = ocr2_ds_item["dataset"], ocr2_ds_item["split"], int(ocr2_ds_item["index"])
+        question = get_question(ds_name, ds_split, ds_index)
+        assert question is not None
+        assert ocr2_ds_item["question"] == "-"
+        ocr2_ds_item["question"] = question
+```
+
+## Dataset Owner(s)
+NVIDIA Corporation
+
+
+## Dataset Creation Date
+March 2025 - May 2025
+
+
+## License/Terms of Use
+
+GOVERNING TERMS: GOVERNING TERMS: 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.
+
+NOTICE FOR SCRIPTS: You may run the scripts below to pull datasets from their original source. The underlying datasets are available from the original sources subject to their own license terms.
+
+**Data Developer:** NVIDIA
+
+### Use Case: <br>
+Developers training Large Language Models (LLMs) to specialize LLMs in code generation and code critique. <br>
+
+### Release Date:  <br>
+05/15/2025 <br>
+
+## Data Version
+1.0 (05/15/2025)
+
+## Dataset Characterization
+
+** Data Collection Method<br>
+* [Hybrid: Automated, Synthetic] <br>
+
+** Labeling Method<be>
+* [Hybrid: Automated, Synthetic] <br>
+
+## Intended Usage
+
+The OpenCodeReasoning-2 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 a 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/).
+
+
+
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