docs: update README to focus on data engineering tools

- Update description to emphasize systematic benchmarking for data engineering
- Remove Go-related content (web services, concurrency, HTTP handlers)
- Focus on Python data processing, ETL pipelines, data transformation
- Add documentation generation for technical docs and API documentation
- Align project structure with data engineering focus

README.md

@@ -19,12 +19,12 @@ tags:
 
 # DataEngEval
 
-A comprehensive evaluation platform for AI models across SQL generation and code generation. Compare model performance with standardized metrics on real-world datasets including NYC Taxi queries, Python algorithms, and Go web services.
+A comprehensive evaluation platform for systematically benchmarking performance across various models and programming languages, focusing on data engineering tools and technologies.
 
 ## 🚀 Features
 
-- **Multi-use-case evaluation**: SQL generation, Python code, Go services
-- **Real-world datasets**: NYC Taxi, sorting algorithms, HTTP handlers, concurrency patterns
+- **Multi-use-case evaluation**: SQL generation, Python data processing, documentation generation
+- **Real-world datasets**: NYC Taxi queries, data transformation algorithms, technical documentation
 - **Comprehensive metrics**: Correctness, execution success, syntax validation, performance
 - **Remote inference**: Uses Hugging Face Inference API (no local model downloads)
 - **Mock mode**: Works without API keys for demos
@@ -37,9 +37,12 @@ A comprehensive evaluation platform for AI models across SQL generation and code
 - **Metrics**: Correctness, execution, result matching, dialect compliance
 
 ### Code Generation
-- **Python**: Algorithms, data structures, object-oriented programming
-- **Go**: Web services, concurrency, HTTP handlers
-- **Metrics**: Syntax correctness, compilation success, execution success, code quality
+- **Python**: Data processing algorithms, ETL pipelines, data transformation functions
+- **Metrics**: Syntax correctness, execution success, data processing accuracy, code quality
+
+### Documentation Generation
+- **Technical Documentation**: API documentation, system architecture, data pipeline documentation
+- **Metrics**: Content accuracy, completeness, technical clarity, formatting quality
 
 ## 🏗️ Project Structure
 
@@ -59,8 +62,8 @@ dataeng-leaderboard/
 │   └── utils/                # Utility functions
 ├── tasks/                    # Multi-use-case datasets
 │   ├── sql_generation/      # SQL generation tasks
-│   ├── code_generation/      # Code generation tasks
-│   └── documentation/       # Documentation tasks
+│   ├── code_generation/     # Python data processing tasks
+│   └── documentation/       # Technical documentation tasks
 ├── prompts/                  # SQL generation templates
 └── test/                     # Test files
 ```