Update README.md

README.md

@@ -1,71 +1,91 @@
----
-license: mit
-language:
-- en
-tags:
-- code
----
 # MultiLang Code Parser Dataset (MLCPD)
 
-## Dataset Description
+[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![Python](https://img.shields.io/badge/python-3.11%2B-blue)](https://www.python.org/downloads/)
+[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-blue)](https://huggingface.co/datasets/jugalgajjar/MultiLang-Code-Parser-Dataset)
+
 
-The MultiLang Code Parser Dataset (MLCPD) is a comprehensive multi-language code dataset designed to benchmark language-agnostic AI code parsers. It currently offers a filtered version of the StarCoder dataset, parsed with language-specific parsers, with future plans to unify outputs into a standard JSON format for complete AST representation.
+**MultiLang-Code-Parser-Dataset (MLCPD)** provides a large-scale, unified dataset of parsed source code across 10 major programming languages, represented under a universal schema that captures syntax, semantics, and structure in a consistent format.
 
-### Key Features
+Each entry corresponds to one parsed source file and includes:
+- Language metadata
+- Code-level statistics (lines, errors, AST nodes)
+- Universal Schema JSON (normalized structural representation)
 
-- **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 of code along with AST node count and error count
-- **Multi-language Support**: 10 programming languages represented in separate subsets
-- **Consistent Format**: All samples follow the same Parquet structure for easy processing
+MLCPD enables robust cross-language analysis, code understanding, and representation learning by providing a consistent, language-agnostic data structure suitable for both traditional ML and modern LLM-based workflows.
+
+---
 
-### Dataset Size
+## 📂 Dataset Structure
 
-The complete dataset is approximately 35GB in size. Individual language files vary in size, with the largest being C++ (5.85GB) and the smallest being Ruby (1.71GB).
+```
+MultiLang-Code-Parser-Dataset/
+├── c_parsed_1.parquet
+├── c_parsed_2.parquet
+├── c_parsed_3.parquet
+├── c_parsed_4.parquet
+├── c_sharp_parsed_1.parquet
+├── ...
+└── typescript_parsed_4.parquet
+```
+Each file corresponds to one partition of a language (~175k rows each).
 
-### Dataset Statistics
+Each record contains:
 
-| Language   | Sample Count | Avg. Line Length | Avg. Line Count |
-|------------|--------------|------------------|-----------------|
-| C          | 700,821      | 28.08            | 61.76           |
-| C++        | 707,641      | 28.16            | 87.88           |
-| C#         | 705,203      | 29.53            | 44.26           |
-| Go         | 700,331      | 25.18            | 68.22           |
-| Java       | 711,922      | 30.85            | 54.40           |
-| JavaScript | 687,775      | 27.69            | 44.15           |
-| Python     | 706,126      | 32.67            | 54.70           |
-| Ruby       | 703,473      | 27.35            | 27.41           |
-| Scala      | 702,833      | 35.30            | 44.38           |
-| TypeScript | 695,597      | 29.18            | 36.89           |
+| Field | Type | Description |
+|--------|------|-------------|
+| `language` | `str` | Programming language name |
+| `code` | `str` | Raw source code |
+| `avg_line_length` | `float` | Average line length |
+| `line_count` | `int` | Number of lines |
+| `lang_specific_parse` | `str` | TreeSitter parse output |
+| `ast_node_count` | `int` | Number of AST nodes |
+| `num_errors` | `int` | Parse errors |
+| `universal_schema` | `str` | JSON-formatted unified schema |
 
-## Dataset Structure
+---
 
-The dataset is organized with separate Parquet files for each programming language:
-- `c_parsed_1.parquet` ... `c_parsed_4.parquet`  - C language samples
-- `cpp_parsed_1.parquet` ... `cpp_parsed_4.parquet` - C++ language samples
-- `c_sharp_parsed_1.parquet` ... `c_sharp_parsed_4.parquet` - C# language samples
-- `go_parsed_1.parquet` ... `go_parsed_4.parquet` - Go language samples
-- `java_parsed_1.parquet` ... `java_parsed_4.parquet` - Java language samples
-- `javascript_parsed_1.parquet` ... `javascript_parsed_4.parquet` - JavaScript language samples
-- `python_parsed_1.parquet` ... `python_parsed_4.parquet` - Python language samples
-- `ruby_parsed_1.parquet` ... `ruby_parsed_4.parquet` - Ruby language samples
-- `scala_parsed_1.parquet` ... `scala_parsed_4.parquet` - Scala language samples
-- `typescript_parsed_1.parquet` ... `typescript_parsed_4.parquet` - TypeScript language samples
+## 📊 Key Statistics
 
-Within each file, data is stored with the following schema:
+| Metric | Value |
+|--------|--------|
+| Total Languages | 10 |
+| Total Files | 40 |
+| Total Records | 7,021,722 |
+| Successful Conversions | 7,021,718 (99.9999%) |
+| Failed Conversions | 4 (3 in C, 1 in C++) |
+| Disk Size | ~105 GB (Parquet format) |
+| Memory Size | ~580 GB (Parquet format) |
 
-```
-- language: string (the programming language of the code sample)
-- code: string (the complete code content)
-- avg_line_length: float (average character count per line)
-- line_count: integer (total number of lines in the code)
-- lang_specific_parse: string (tree-sitter parsed output of the code sample)
-- ast_node_count: integer (total number of nodes in the AST)
-- num_errors: integer (total number of errors in the code)
-```
+The dataset is clean, lossless, and statistically balanced across languages.  
+It offers both per-language and combined cross-language representations.
 
-Each sample is stored as a row in the Parquet file with these four columns.
+---
+
+## 🚀 Use Cases
+
+MLCPD can be directly used for:
+- Cross-language code representation learning
+- Program understanding and code similarity tasks
+- Syntax-aware pretraining for LLMs
+- Code summarization, clone detection, and bug prediction
+- Graph-based learning on universal ASTs
+- Benchmark creation for cross-language code reasoning
+
+---
 
-## How to Access the Dataset
+## 🔍 Features
+
+- **Universal Schema:** A unified structural representation harmonizing AST node types across languages.  
+- **Compact Format:** Stored in Apache Parquet, allowing fast access and efficient querying.  
+- **Cross-Language Compatibility:** Enables comparative code structure analysis across multiple programming ecosystems.  
+- **Error-Free Parsing:** 99.9999% successful schema conversions across ~7M code files.  
+- **Statistical Richness:** Includes per-language metrics such as mean line count, AST size, and error ratios.  
+- **Ready for ML Pipelines:** Compatible with PyTorch, TensorFlow, Hugging Face Transformers, and graph-based models.
+
+---
+
+## 📥 How to Access the Dataset
 
 ### Using the Hugging Face `datasets` Library
 
@@ -79,21 +99,21 @@ pip install datasets
 
 #### Import Library
 
-```python
+```bash
 from datasets import load_dataset
 ```
 
 #### Load the Entire Dataset
 
-```python
+```bash
 dataset = load_dataset(
     "jugalgajjar/MultiLang-Code-Parser-Dataset"
 )
 ```
 
-#### Load a Specific Language
+#### Load a Specific Language File
 
-```python
+```bash
 dataset = load_dataset(
     "jugalgajjar/MultiLang-Code-Parser-Dataset",
     data_files="python_parsed_1.parquet"
@@ -102,7 +122,7 @@ dataset = load_dataset(
 
 #### Stream Data
 
-```python
+```bash
 dataset = load_dataset(
     "jugalgajjar/MultiLang-Code-Parser-Dataset",
     data_files="python_parsed_1.parquet",
@@ -112,7 +132,7 @@ dataset = load_dataset(
 
 #### Access Data Content (After Downloading)
 
-```python
+```bash
 try:
     for example in dataset["train"].take(5):
         print(example)
@@ -125,40 +145,36 @@ except Exception as e:
 
 You can also manually download specific language files from the Hugging Face repository page:
 
-1. Visit `https://huggingface.co/datasets/jugalgajjar/MultiLang-Code-Parser-Dataset`
-2. Navigate to the "Files" tab
-3. Click on the language file you want to download (e.g., `python_parsed_1.parquet`)
-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
-7. Data was serialized in the efficient Parquet format for optimal storage and access speed
-8. Code samples from each language were parsed using language-specific tree-sitter parsers
-9. Metadata (AST node count and number of errors in the code) were recorded for each sample
-10. Final data was split into four files and stored in the Parquet format
-
-## Citation
-
-If you use this dataset in your research or project, please cite it as follows:
-
-```bibtex
-@misc{mlcpd2025,
-  author = {Jugal Gajjar, Kamalasankari Subramaniakuppusamy, Kaustik Ranaware},
-  title = {Filtered CodeStar Dataset Mini},
-  year = {2025},
-  publisher = {HuggingFace},
-  howpublished = {\url{https://huggingface.co/datasets/jugalgajjar/MultiLang-Code-Parser-Dataset}}
-}
-```
+1. Visit https://huggingface.co/datasets/jugalgajjar/MultiLang-Code-Parser-Dataset
+2. Navigate to the Files tab
+3. Click on the language file you want (e.g., `python_parsed_1.parquet`)
+4. Use the Download button to save locally
 
-## License
+---
+
+## 📜 License
+
+This dataset is released under the MIT License.<br>
+You are free to use, modify, and redistribute it for research and educational purposes, with proper attribution.
+
+---
+
+## 🙏 Acknowledgements
+
+- [StarCoder Dataset](https://huggingface.co/datasets/bigcode/starcoderdata) for source code samples
+- [TreeSitter](https://tree-sitter.github.io/tree-sitter/) for parsing
+- [Hugging Face](https://huggingface.co/) for dataset hosting
+
+---
+
+## 📧 Contact
+
+For questions, collaborations, or feedback:
+
+- **Primary Author**: Jugal Gajjar
+- **Email**: [812jugalgajjar@gmail.com](mailto:812jugalgajjar@gmail.com)
+- **LinkedIn**: [linkedin.com/in/jugal-gajjar/](https://www.linkedin.com/in/jugal-gajjar/)
+
+---
 
-This dataset is released under the MIT License. See the LICENSE file for more details.
+⭐ If you find this dataset useful, consider liking the dataset and the [GitHub repository](https://github.com/JugalGajjar/MultiLang-Code-Parser-Dataset) and sharing your work that uses it.