Create README.md

README.md

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+# Code-Regression
+
+A unified regression dataset collated from three sources (APPS, KBSS, CDSS) along with our own custom profiling for training and evaluating regression models that map code strings to a target metric.
+
+## Schema
+- **identifier** *(string)*: Source key for the example, e.g. `APPS_0`, `KBSS_1`, `CDSS_42`.
+- **space** *(string)*: Logical dataset split/source (`APPS`, `KBSS`, or `CDSS`).
+- **input** *(string)*: The input string (`shortest_onnx`).
+- **target_metric** *(string)*: Always `val_accuracy`.
+- **val_accuracy** *(number | null)*: The regression target.
+- **metric_type** *(string)*: Auxiliary metric family for this row:
+  - `memory_bytes` for APPS and CDSS
+  - `latency_ms` for KBSS
+- **metadata** *(string)*: A Python-dict-like string with source-specific information:
+  - APPS: `problem_metainformation` cast to string.
+  - KBSS: `{'stddev_ms': <value>}`.
+  - CDSS: subset of fields `{s_id, p_id, u_id, date, language, original_language, filename_ext, status, cpu_time, memory, code_size}`.
+
+> Tip: turn `metadata` back into a dict with:
+> ```python
+> from ast import literal_eval
+> meta = literal_eval(row["metadata"])
+> ```
+
+## How to load with 🤗 Datasets
+```python
+from datasets import load_dataset
+
+# After you upload this folder to a dataset repo, e.g. your-username/Code-Regression
+ds = load_dataset("your-username/Code-Regression")
+
+# Or from a local clone:
+# ds = load_dataset("json", data_files="Code-Regression/data.jsonl", split="train")
+```
+
+# Credits
+
+This dataset was collated from several sources, along with our own latency and memory profiling. We thank the authors for their efforts.
+
+APPS:
+Hendrycks, D., Basart, S., Kadavath, S., Mazeika, M., Arora, A., Guo, E., Burns, C., Puranik, S., He, H., Song, D., & Steinhardt, J. (2021). Measuring Coding Challenge Competence With APPS. NeurIPS.
+
+CDSS (CodeNet):
+Puri, R., Kung, D. S., Janssen, G., Zhang, W., Domeniconi, G., Zolotov, V., Dolby, J., Chen, J., Choudhury, M., Decker, L., & others. (2021). Codenet: A large-scale ai for code dataset for learning a diversity of coding tasks. ArXiv Preprint ArXiv:2105.12655.
+
+KBSS (KernelBook):
+Paliskara, S., & Saroufim, M. (2025). KernelBook. https://huggingface.co/datasets/GPUMODE/KernelBook
+
+## Citations
+
+If you found this dataset useful for your research, please cite the original sources above as well as:
+
+```
+@article{akhauri2025performance,
+  title={Performance Prediction for Large Systems via Text-to-Text Regression},
+  author={Akhauri, Yash and Lewandowski, Bryan and Lin, Cheng-Hsi and Reyes, Adrian N and Forbes, Grant C and Wongpanich, Arissa and Yang, Bangding and Abdelfattah, Mohamed S and Perel, Sagi and Song, Xingyou},
+  journal={arXiv preprint arXiv:2506.21718},
+  year={2025}
+}
+```
+
+(Original Paper Coming Soon!)
+