Python Code Instruction

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Python Code Instruction

Training Data with Instruction, Input, Output, and Prompt Columns

By Tarun Bisht (From Huggingface) [source]

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About this dataset

The python_code_instructions_18k_alpaca dataset is a comprehensive training dataset specifically curated for researchers and developers involved in the analysis and comprehension of Python code instructions. It contains a vast collection of Python code snippets along with their corresponding instruction, input, output, and prompt information. By utilizing this dataset, users can gain valuable insights into various Python programming concepts and techniques.

The dataset is organized into columns to facilitate easy access to the required information. The instruction column holds the specific task or instruction that the Python code snippet is designed to perform. This allows users to understand the purpose or requirement of each code snippet at a glance.

The input column contains all necessary input data or parameters that are required for executing the Python code snippet accurately. These inputs provide context and enable users to comprehend how different variables or values impact the overall functioning of each code snippet.

Likewise, the output column presents expected results or outcomes that should be produced when executing each Python code snippet with its specified input values. This allows for validation and verification purposes, ensuring that each code snippet performs as intended.

In addition to instruction, input, and output details, this dataset also includes prompts. The prompt column provides additional context or information intended to assist users in better understanding the purpose or requirements of each particular Python code snippet.

By leveraging this comprehensive python_code_instructions_18k_alpaca training dataset, researchers and developers can delve into numerous real-world examples of Python programming challenges - helping them enhance their coding skills while gaining invaluable knowledge about effective implementation techniques across various domains

Research Ideas

• Code Instruction Analysis: This dataset can be used to analyze different types of Python code instructions and identify patterns or common practices. Researchers or developers can use this dataset to gain insights into effective ways of writing code instructions.
• Code Output Prediction: With the given input and instruction, this dataset can be used to train models for predicting the expected output of a Python code snippet. This can be useful in automating the testing process or verifying the correctness of the code.
• Prompt Generation: Developers often struggle with providing clear and concise prompts for their code snippets. This dataset can serve as a resource for generating prompts by analyzing existing examples and extracting key information or requirements from them

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Tarun Bisht (From Huggingface).