# Title: Code Generation and Completion Using Character-Level Transformers: A Feasibility Study
# Experiment description: ['1. Preprocess an existing codebase to fit the character-level format required by the model. This can include Python, JavaScript, or any other programming language.', '2. Modify the training script to include a new dataset of code snippets and ensure the model is trained on this dataset.', "3. Implement a function to evaluate the model's ability to generate complete code snippets from partial inputs (code completion).", "4. Evaluate the model's performance by comparing generated code snippets with human-written code in terms of syntax correctness and functionality.", '5. Analyze the results to determine the feasibility and accuracy of the model for code generation and completion tasks.', '6. Explore the potential of the model for simple code transformations, such as refactoring or error correction.']
## Run 0: Baseline
Results: {'shakespeare_char': {'final_train_loss_mean': 0.8173830509185791, 'best_val_loss_mean': 1.4637625217437744, 'total_train_time_mean': 92.05195260047913, 'avg_inference_tokens_per_second_mean': 697.3658396135052}}
Description: Baseline results.