Update README.md

README.md

@@ -9,47 +9,47 @@ tags:
 
 ## Dataset Description
 
-This dataset contains filtered and processed code samples from 10 popular programming languages: C, C++, C#, Go, Java, JavaScript, Python, Ruby, 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.
+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.
 
 ### 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
+- **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
 
 ### Dataset Size
 
-The complete dataset is approximately 12GB in size. Individual language files vary in size, with the largest being C++ (2GB) and the smallest being Scala (665MB).
+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).
 
 ### Dataset Statistics
 
 | Language   | Sample Count | Avg. Line Length | Avg. Line Count |
-|------------|--------------|-----------------|------------------|
-| C          | 1,752,078    | 28.07           | 61.85            |
-| C++        | 1,769,333    | 28.16           | 87.99            |
-| C#         | 1,763,508    | 29.53           | 44.29            |
-| Go         | 1,751,120    | 25.18           | 68.26            |
-| Java       | 1,779,659    | 30.84           | 54.35            |
-| JavaScript | 1,718,133    | 27.68           | 44.07            |
-| Python     | 1,764,099    | 32.68           | 54.66            |
-| Ruby       | 1,756,771    | 27.35           | 27.34            |
-| Scala      | 952,890      | 35.30           | 44.38            |
-| TypeScript | 1,738,885    | 29.17           | 36.84            |
+|------------|--------------|------------------|-----------------|
+| 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           |
 
 ## Dataset Structure
 
 The dataset is organized with separate Parquet files for each programming language:
-- `c.parquet` - C language samples
-- `cpp.parquet` - C++ language samples
-- `c-sharp.parquet` - C# language samples
-- `go.parquet` - Go language samples
-- `java.parquet` - Java language samples
-- `javascript.parquet` - JavaScript language samples
-- `python.parquet` - Python language samples
-- `ruby.parquet` - Ruby language samples
-- `scala.parquet` - Scala language samples
-- `typescript.parquet` - TypeScript language samples
+- `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
 
 Within each file, data is stored with the following schema:
 
@@ -58,6 +58,9 @@ Within each file, data is stored with the following schema:
 - 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)
 ```
 
 Each sample is stored as a row in the Parquet file with these four columns.
@@ -84,7 +87,7 @@ from datasets import load_dataset
 
 ```python
 dataset = load_dataset(
-    "jugalgajjar/Filtered-StarCoder-Dataset-Mini"
+    "jugalgajjar/MultiLang-Code-Parser-Dataset"
 )
 ```
 
@@ -92,8 +95,8 @@ dataset = load_dataset(
 
 ```python
 dataset = load_dataset(
-    "jugalgajjar/Filtered-StarCoder-Dataset-Mini",
-    data_files="scala.parquet"
+    "jugalgajjar/MultiLang-Code-Parser-Dataset",
+    data_files="python_parsed_1.parquet"
 )
 ```
 
@@ -101,8 +104,8 @@ dataset = load_dataset(
 
 ```python
 dataset = load_dataset(
-    "jugalgajjar/Filtered-StarCoder-Dataset-Mini",
-    data_files="scala.parquet",
+    "jugalgajjar/MultiLang-Code-Parser-Dataset",
+    data_files="python_parsed_1.parquet",
     streaming=True
 )
 ```
@@ -122,9 +125,9 @@ 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/Filtered-StarCoder-Dataset-Mini`
+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.parquet`)
+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
@@ -137,13 +140,10 @@ This dataset was created through the following process:
 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. Final data was serialized in the efficient Parquet format for optimal storage and access speed
-
-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
+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
 
@@ -155,7 +155,7 @@ If you use this dataset in your research or project, please cite it as follows:
   title = {Filtered CodeStar Dataset Mini},
   year = {2025},
   publisher = {HuggingFace},
-  howpublished = {\url{https://huggingface.co/datasets/jugalgajjar/Filtered-StarCoder-Dataset-Mini}}
+  howpublished = {\url{https://huggingface.co/datasets/jugalgajjar/MultiLang-Code-Parser-Dataset}}
 }
 ```