CODEGEEX: A PRE-TRAINED MODEL FOR GENERATION WITH MULTILINGUAL EVALUATIONS ON HUMANEVAL-X

Abstract

CODEGEEX is an advanced AI-driven project focused on developing trained model projects capable of generating code across multiple programming languages. This model aims to evaluate its performance using a new benchmark, HumanEval-X, designed for multilingual code generation. The project leverages deep learning techniques and large-scale datasets to train a model that not only understands and generates code but also evaluates its accuracy and efficiency in various languages, enhancing global accessibility and usability.

Existing System

Current code generation models are primarily monolingual and focus on specific programming languages, which limits their applicability in a globalized market where software development often involves multiple languages.

Proposed System

CODEGEEX introduces a robust, scalable, and efficient framework for multilingual code generation. By training on diverse datasets from various programming languages, the model will not only perform code completion tasks but also adapt to syntax variations and semantic nuances across languages, using HumanEval-X for comprehensive evaluation.

Module Description

1. Data Collection Module: Gathers diverse code datasets from multiple programming languages.
2. Pre-processing Module: Cleans and prepares the data for training, including tokenization and normalization.
3. Model Training Module: Utilizes advanced machine learning techniques to train the model on the processed data.
4. Evaluation Module: Employs HumanEval-X to test the model’s code generation capabilities across different languages.
5. User Interface Module: Provides an interactive interface for developers to use the model for code generation tasks.

Functional Requirements

• Multilingual Support: Ability to understand and generate code in multiple programming languages.
• High Accuracy and Efficiency: Generate syntactically correct and logically functional code.
• Scalability: Handle increasing amounts of data and complexity in training and operations.
• User-Friendly Interface: Easy-to-use interface for developers of varying skill levels.

Non-Functional Requirements

• Performance: Fast response times in code generation tasks.
• Reliability: Consistently produce accurate and efficient code.
• Security: Ensure the integrity and confidentiality of the code generated and data used.
• Maintainability: The system should be easy to maintain and update with new languages and features.

Software and Hardware Requirements

Software:

• Programming Languages: Python
• Libraries/Frameworks: PyTorch, TensorFlow (for model training and execution); Hugging Face’s Transformers (for NLP tasks and pre-trained model components).
• Development Environment: Google Colab, Jupyter Notebook for iterative testing and development.
• Tools: Git for version control, Docker for creating consistent development environments.

Hardware:

• GPUs: High-performance NVIDIA GPUs for efficient model training and execution.
• CPUs: Multi-core processors to support parallel processing and data handling.
• RAM: Minimum 32 GB for handling large datasets and training processes.
• Storage: High-capacity SSDs for storing extensive training datasets and model checkpoints.