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README.md

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-license: mit
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+# 🔧 Selected SWE-Gym Subset
+
+A curated subset of 100 program repair instances from the [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) dataset, selected for lightweight evaluation and rapid prototyping.
+
+## 📦 Dataset Description
+
+This dataset contains 100 program repair tasks selected from the full SWE-Gym benchmark. Each instance represents a realistic software bug scenario, including the following fields:
+
+* `instance_id`: Unique identifier
+* `repo`: GitHub repository
+* `commit`: Bug-inducing commit hash
+* `test_setup`: Test setup instructions
+* `test_commands`: How to run the test
+* `relevant_files`: Files to be considered
+* `expected_output`: Expected behavior
+* `language`: Programming language
+* `difficulty`: (if available) Estimated difficulty
+* `summary`: Natural language bug description
+
+All instances are formatted in JSONL (`.jsonl`) for compatibility with LLM pipelines and benchmarking scripts.
+
+## ✅ Usage
+
+You can load the dataset using the `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("dcloud347/Selected_SWE-Gym")
+print(dataset["train"])
+```
+
+## 💡 Motivation
+
+Evaluating automatic program repair systems on the full SWE-Gym benchmark can be resource-intensive. This curated 100-instance subset enables:
+
+* Fast debugging of repair pipelines
+* Lightweight academic comparisons
+* Evaluation of few-shot LLM repair models
+* Quick iteration on toolchain design
+
+## 📁 Dataset Structure
+
+```
+data.jsonl
+├─ {"instance_id": ..., "repo": ..., "commit": ..., ...}
+├─ ...
+```
+
+## 📜 License
+
+This subset follows the same license as the original SWE-Gym dataset (MIT). Please credit the original authors when using this dataset in your research.
+
+## 🙏 Acknowledgements
+
+* Original dataset: [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym)