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Code Generation Project README

This README provides an overview of the Code Generation project, including its objectives, problem statement, lab requirements, and the structure of experiments and results.

1. Objectives

The objectives of this project are as follows:

1. Use HuggingFace to generate code for Natural Language Processing (NLP) tasks.
2. Gain an understanding of code generation and its applications in software development.
3. Apply knowledge of NLP concepts and techniques to generate code using HuggingFace models.

2. Problem Statement

In this project, you will learn about code generation, the process of automatically generating code from input data or instructions. Code generation is a valuable technique in software development for tasks such as creating boilerplate code, generating code from templates, and automating code creation based on user input. You will use the HuggingFace library, a popular open-source library for NLP tasks, to generate code for various NLP tasks.

3. Lab Requirements

Follow the provided Colab link for the code skeleton. Ensure the following requirements are met:

3.1 Data

The dataset contains Python code collected from public repositories like Github, filtered for keywords related to Python libraries. You can use a pre-filtered version available through HuggingFace.

3.2 Model

Utilize a small version of the GPT-2 model with the provided configuration.

3.3 Resources

• Use Google Colab GPU to accelerate your experiments.
• Install essential libraries using pip. You can do this within your Colab notebooks.

4. Experiments

You are required to run at least three different experiments and save their weights using various configurations of the following:

• Learning Rates
• Optimizers
• Gradient Accumulation Steps
• Number of Max Steps of Training

5. Results

Your results should be available within Colab, and it is recommended to run a separate Colab notebook for each experiment to save the outputs efficiently. Prepare a report that includes the following:

• Explanation of GPT-2 and its architecture.
• For each experiment:
  • Report perplexity and evaluation results variable values with each experiment.
  • Include any plots, scores, or similar data related to the experiment.
  • Report the output of the code prompts provided at the end of the notebook.

Conclusion

This README provides a structured overview of the Code Generation project. It outlines the objectives, requirements, and expectations for running experiments and reporting results. By following these guidelines, you can effectively explore code generation using HuggingFace and gain valuable insights into NLP-based code generation techniques.