| # AweAgent-Meta-NL2Repo |
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| This dataset provides the **metadata used by [AweAgent](https://github.com/AweAI-Team/AweAgent) to run the NL2RepoBench evaluation**. |
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| If you are looking for the underlying benchmark itself (task design, repositories, test suites), please refer to the original project: [multimodal-art-projection/NL2RepoBench](https://github.com/multimodal-art-projection/NL2RepoBench). |
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| ## Purpose |
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| The AweAgent repository evaluates end-to-end repo-level code generation: given a natural-language project specification, the agent must produce a working Python package that passes the project's tests. To run that evaluation reproducibly, the harness needs a compact, machine-readable manifest of every instance — that is exactly what this dataset provides. |
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| Concretely, each row tells AweAgent: |
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| - which prebuilt evaluation Docker image to launch, |
| - the NL prompt that defines the target repository, |
| - the verification command and the files that must be produced, |
| - the number of test cases the generated repo will be scored against. |
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| ## Files |
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| - `nl2repo_aweagent.jsonl` — one JSON object per NL2RepoBench instance (104 instances). |
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| ## Schema |
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| | Field | Type | Description | |
| |---|---|---| |
| | `instance_id` | `str` | Unique identifier for the instance (typically the target package name). | |
| | `package_name` | `str` | The Python package the agent is expected to generate. | |
| | `evaluation_image` | `str` | Docker image (hosted under `ghcr.io/multimodal-art-projection/nl2repobench`) used to evaluate the generated repository. | |
| | `start_instruction` | `str` | The natural-language task description handed to the agent as the starting prompt. | |
| | `verify_files` | `list[str]` | Files that must be produced by the agent and are checked during verification. | |
| | `verify_cmd` | `str` | The command executed inside the evaluation image to verify the generated repository. | |
| | `test_cases_num` | `int` | Number of test cases used to score the instance. | |
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| ## Usage |
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| ```python |
| from datasets import load_dataset |
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| ds = load_dataset("AweAI-Team/AweAgent-Meta-NL2Repo", split="train") |
| print(ds[0]) |
| ``` |
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| This manifest is consumed by the evaluation pipeline in [AweAgent](https://github.com/AweAI-Team/AweAgent); see that repository for the full runner, scoring logic, and reproduction instructions. |
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| ## Acknowledgements |
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| This dataset is built on top of, and would not exist without, the excellent [NL2RepoBench](https://github.com/multimodal-art-projection/NL2RepoBench) project by the Multimodal Art Projection team. All benchmark instances, evaluation images, and test cases originate from their work; this dataset only repackages the per-instance metadata in the form AweAgent's evaluation harness expects. Huge thanks to the NL2RepoBench authors for releasing such a high-quality repository-level code-generation benchmark. |
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| ## License |
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| Released under CC BY 4.0. When using this dataset, please also cite and credit the upstream [NL2RepoBench](https://github.com/multimodal-art-projection/NL2RepoBench) project. |
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