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README.md

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-license: mit
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+# Filtered StarCoder Dataset Mini
+
+## Dataset Description
+
+This dataset contains filtered and processed code samples from 12 popular programming languages: C, C++, C#, Go, Java, JavaScript, Kotlin, Python, Ruby, Rust, Scala, and TypeScript. The dataset was created by filtering source code based on quality metrics, removing outliers, and standardizing the format for machine learning and code analysis applications.
+
+### Key Features
+
+- **Cleaned and Filtered Code**: Samples have been processed to remove outliers in terms of line length and code size
+- **Quality Metrics**: Each sample includes metadata about average line length and line count
+- **Multi-language Support**: 12 programming languages represented in separate subsets
+- **Consistent Format**: All samples follow the same JSON structure for easy processing
+
+### Dataset Statistics
+
+| Language   | Sample Count | Avg. Line Length | Avg. Line Count |
+|------------|--------------|------------------|-----------------|
+| C          | 1,751,894    | 22.55           | 74.48            |
+| C++        | 1,769,514    | 23.50           | 103.59           |
+| C#         | 1,762,960    | 25.76           | 51.50            |
+| Go         | 1,750,873    | 20.68           | 81.76            |
+| Java       | 1,778,689    | 25.48           | 64.56            |
+| JavaScript | 1,719,140    | 23.31           | 51.29            |
+| Kotlin     | 1,589,735    | 27.39           | 42.31            |
+| Python     | 1,764,481    | 26.52           | 66.15            |
+| Ruby       | 1,758,558    | 22.31           | 33.95            |
+| Rust       | 1,341,806    | 27.36           | 190.37           |
+| Scala      | 1,322,906    | 31.48           | 94.89            |
+| TypeScript | 1,738,950    | 24.15           | 43.46            |
+
+## Dataset Structure
+
+The dataset is organized with separate JSON files for each programming language:
+- `c.json` - C language samples
+- `cpp.json` - C++ language samples
+- `c-sharp.json` - C# language samples
+- `go.json` - Go language samples
+- `java.json` - Java language samples
+- `javascript.json` - JavaScript language samples
+- `kotlin.json` - Kotlin language samples
+- `python.json` - Python language samples
+- `ruby.json` - Ruby language samples
+- `rust.json` - Rust language samples
+- `scala.json` - Scala language samples
+- `typescript.json` - TypeScript language samples
+
+Within each file, data is stored in the following format:
+
+```json
+{
+    "lang_0": {
+        "code": "ENTIRE CODE",
+        "avg_line_length": 35.7,
+        "line_count": 120
+    },
+    "lang_1": {
+        "code": "ANOTHER CODE EXAMPLE",
+        "avg_line_length": 42.3,
+        "line_count": 85
+    },
+    ...
+    "lang_N": {
+        "code": "ENTIRE CODE",
+        "avg_line_length": 28.9,
+        "line_count": 67
+    }
+}
+```
+
+Each sample is assigned a unique integer ID (0 to N) prefixed with the language identifier.
+
+## How to Access the Dataset
+
+### Using the Hugging Face `datasets` Library
+
+This dataset is hosted on the Hugging Face Hub and can be easily accessed using the `datasets` library.
+
+#### Install the Required Library
+
+```bash
+pip install datasets
+```
+
+#### Load the Entire Dataset
+
+```python
+TODO
+```
+
+#### Load a Specific Language
+
+```python
+TODO
+```
+
+### Manual Download
+
+You can also manually download specific language files from the Hugging Face repository page:
+
+1. Visit `https://huggingface.co/datasets/jugalgajjar/Filtered-StarCoder-Dataset-Mini`
+2. Navigate to the "Files" tab
+3. Click on the language file you want to download (e.g., `python.json`)
+4. Use the download button to save the file locally
+
+## Dataset Creation
+
+This dataset was created through the following process:
+
+1. Original code samples were collected from the StarCoder dataset ([URL](https://huggingface.co/datasets/bigcode/starcoderdata))
+2. Statistical analysis was performed to identify quality metrics
+3. Outliers were removed using IQR (Interquartile Range) method
+4. Samples were filtered to remove excessively long or short code examples
+5. Data was normalized and standardized across languages
+6. Metadata (average line length and line count) was calculated for each sample
+
+The processing pipeline included steps to:
+- Remove code samples with abnormal line lengths (potential formatting issues)
+- Filter out extremely long files (exceeding the 90th percentile)
+- Ensure consistent formatting and structure
+- Generate useful metadata for each example
+
+## Citation
+
+If you use this dataset in your research or project, please cite it as follows:
+
+```bibtex
+@misc{fscdmini2025,
+  author = {Jugal Gajjar, Kamalasankari Subramaniakuppusamy, Kaustik Ranaware},
+  title = {Filtered CodeStar Dataset Mini},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/datasets/jugalgajjar/Filtered-StarCoder-Dataset-Mini}}
+}
+```
+
+## License
+
+This dataset is released under the MIT License. See the LICENSE file for more details.