Finalized dataset card with English ranking logic explanation (v2).

README.md

@@ -0,0 +1,75 @@
+
+---
+license: cc-by-sa-4.0 
+tags:
+- competitive-programming
+- code-ranking
+- llm-benchmark
+- code-efficiency
+- aizu-online-judge
+---
+
+# AOJ-CodeRank-Benchmark: Hybrid Efficiency Ranking Benchmark Dataset
+
+## 1. Overview
+
+This dataset (AOJ-CodeRank-Benchmark) was created to evaluate the capability of **Large Language Models (LLMs)** in **code efficiency ranking tasks** using a high-quality, structured benchmark.
+
+The dataset is built entirely on code submission records from **Aizu Online Judge (AOJ)**, strictly adhering to the principle of **correctness first, efficiency second**.
+
+* **Problem Scope**: ALDS1 (Fundamental Algorithms), DSL/GRL/CGL (Advanced Data Structures/Graphs), and Volume 0000-3299 (Classic Contest Problems).
+* **Core Feature**: **Eliminates** 0ms submissions and low-quality/non-unique submissions, ensuring true time differentiation across all data groups.
+
+## 2. Data Structure
+
+The dataset uses the **JSON Lines (.jsonl)** format. Each line represents a single Task Group object.
+
+**Structure Preview (Candidates):**
+
+| Field Name | Type | Description |
+| :--- | :--- | :--- |
+| `submission_id` | string | Unique Submission ID. |
+| `code_snippet` | string | The complete C++ source code. |
+| **`accuracy`** | float | **Accuracy Score** (0.0 to 1.0). |
+| `time_ms` | integer | Actual Execution Time (in milliseconds). |
+| **`score_of_the_acc`** | float | **Normalized Efficiency Score** (Range -2.0 to 0.0). |
+| **`final_rank`** | integer | **Final Competition Rank** (1, 2, 3...). |
+
+## 3. Ground Truth (GT) Scoring and Ranking Logic 🏆
+
+The LLM's objective is to predict the `final_rank`. This ranking is derived from a unique two-tiered system:
+
+### Phase I: Efficiency Score (`score_of_the_acc`)
+
+This score is a purely performance-based metric, calculating the normalized inverse sum of Time and Memory costs within the task group.
+
+$$	ext{Score} = -(	ext{Norm\_Time} + 	ext{Norm\_Memory})$$
+
+*(Note: Score is between -2.0 and 0.0. A score closer to 0.0 is better.)*
+
+### Phase II: Final Ranking (`final_rank`) Mechanism
+
+The final rank is determined by a lexicographical sort (Standard Competition Ranking) using the following priority:
+
+1.  **Primary Sort Key (Accuracy)**: **`accuracy`** (Descending).
+2.  **Secondary Sort Key (Efficiency)**: **`score_of_the_acc`** (Descending).
+
+**Tie-Breaking**: Submissions with identical Accuracy and Efficiency Score receive the same rank (1-2-2-4 rule).
+
+---
+
+### 4. Usage Example
+
+```python
+from datasets import load_dataset
+
+# Load the dataset and access the candidates list
+dataset = load_dataset("Slime/AOJ-CodeRank-Benchmark", data_files="train.jsonl", split="train")
+
+# The LLM sorting algorithm will receive task['candidates'] for ranking
+for task in dataset:
+    candidates = task['candidates']
+    # Algorithm generates predicted_rank for candidates
+    # Evaluation compares predicted_rank against ground_truth['final_rank']
+5. Acknowledgments
+Original submission records and problem context are sourced from Aizu Online Judge (AOJ).