Update ACE-Bench dataset

README.md

@@ -0,0 +1,135 @@
+---
+language:
+- en
+license: mit
+size_categories:
+- n<100
+task_categories:
+- text-generation
+dataset_info:
+  features:
+  - name: instance_id
+    dtype: string
+  - name: patch
+    dtype: string
+  - name: test_patch
+    dtype: string
+  - name: FAIL_TO_PASS
+    list: string
+  - name: PASS_TO_PASS
+    list: string
+  - name: image_name
+    dtype: string
+  - name: repo
+    dtype: string
+  - name: base_commit
+    dtype: string
+  - name: problem_statement
+    dtype: string
+  splits:
+  - name: level1
+    num_bytes: 448251
+    num_examples: 8
+  - name: level2
+    num_bytes: 277869
+    num_examples: 7
+  download_size: 119867
+  dataset_size: 726120
+configs:
+- config_name: default
+  data_files:
+  - split: level1
+    path: data/level1-*
+  - split: level2
+    path: data/level2-*
+tags:
+- code
+- agents
+- software-engineering
+---
+
+# ACE-Bench: Agent Coding Evaluation Benchmark
+
+## Dataset Description
+
+ACE-Bench is a comprehensive benchmark designed to evaluate AI agents' capabilities in end-to-end feature-level code generation. Unlike traditional benchmarks that focus on function-level or algorithm-specific tasks, ACE-Bench challenges agents to implement complete features within real-world software projects.
+
+### Key Characteristics
+
+- **Feature-Level Tasks**: Each task requires implementing a complete feature, including multiple functions, classes, and their interactions
+- **Real-World Codebases**: Tasks are derived from actual open-source projects, preserving the complexity and context of production code
+- **End-to-End Evaluation**: Agents must understand requirements, generate code, and pass comprehensive test suites
+- **Two Difficulty Levels**:
+  - **Level 1**: Agents receive masked code with interface signatures and must implement the complete functionality
+  - **Level 2**: Agents receive only test files and must implement both the interface and functionality from scratch
+
+### Dataset Statistics
+
+- **Total Instances**: 15
+- **Level 1 Instances**: 8
+- **Level 2 Instances**: 7
+- **Total Size**: 709.10 KB
+- **Download Size**: 117.06 KB
+
+## Dataset Structure
+
+Each instance in ACE-Bench contains:
+
+- `instance_id`: Unique identifier for the task
+- `patch`: Git diff showing the implementation (Level 1) or empty string (Level 2)
+- `test_patch`: Git diff showing test file modifications
+- `FAIL_TO_PASS`: List of test files that must pass after implementation
+- `PASS_TO_PASS`: List of test files that must continue passing (Level 1 only)
+- `image_name`: Docker image containing the development environment
+- `repo`: Source repository (e.g., "owner/repo-name")
+- `base_commit`: Git commit hash of the base version
+- `problem_statement`: Detailed task description and requirements
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load Level 1 tasks
+dataset_lv1 = load_dataset("BamChil/ACE-Bench", split="level1")
+
+# Load Level 2 tasks
+dataset_lv2 = load_dataset("BamChil/ACE-Bench", split="level2")
+
+# Example: Access a task
+task = dataset_lv1[0]
+print(task['instance_id'])
+print(task['problem_statement'])
+```
+
+## Evaluation
+
+To evaluate an agent on ACE-Bench:
+
+1. **Setup Environment**: Pull the Docker image specified in `image_name`
+2. **Apply Patches**: Use `git apply` to apply test patches in the container
+3. **Generate Code**: Have the agent generate code based on `problem_statement`
+4. **Run Tests**: Execute the test suite specified in `FAIL_TO_PASS`
+5. **Verify Results**: Ensure all tests pass and no regressions occur in `PASS_TO_PASS` tests
+
+## Citation
+
+If you use ACE-Bench in your research, please cite:
+
+```bibtex
+@dataset{ace_bench_2025,
+  title={ACE-Bench: Agent Coding Evaluation Benchmark},
+  author={ACE-Bench Team},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/BamChil/ACE-Bench}
+}
+```
+
+## License
+
+This dataset is released under the MIT License. Individual code snippets retain their original repository licenses.
+
+## Contact
+
+For questions or feedback, please open an issue on the [GitHub repository](https://github.com/BamChil/ACE-Bench).