Duplicate from basakdemirok/AIGCodeSet

Co-authored-by: Basak Demirok <basakdemirok@users.noreply.huggingface.co>

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+all_data_with_ada_embeddings_will_be_splitted_into_train_test_set.csv filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: cdla-permissive-2.0
+task_categories:
+- text-classification
+tags:
+- code-classification
+- code
+- machine-learning
+pretty_name: LLM vs Human Code Classification
+size_categories:
+- 10M<n<100M
+---
+
+
+# LLM vs Human Code Dataset  
+_A Benchmark Dataset for AI-generated and Human-written Code Classification_
+
+## Description
+This dataset contains code samples generated by various Large Language Models (LLMs), including CodeStral (Mistral AI), Gemini (Google DeepMind), and CodeLLaMA (Meta), along with human-written codes from CodeNet. The dataset is designed to support research on distinguishing LLM-generated code from human-written code.
+
+## Dataset Structure
+
+### 1. LLM-generated Dataset (`created_dataset_with_llms.csv`)
+| Column             | Description                                                                                  |
+|--------------------|----------------------------------------------------------------------------------------------|
+| problem_id          | Unique problem ID from CodeNet.                                                             |
+| submission_id       | Submission ID from CodeNet. `"submission_id == unrelated"` indicates purely LLM-generated code. |
+| LLM                 | Model used: `"CODESTRAL"`, `"GEMINI"`, or `"LLAMA"`.                                         |
+| status_in_folder    | Code status: `"wrong"`, `"runtime"`, `"generate"`.                                          |
+| code                | Code generated by the corresponding LLM.                                                    |
+| label               | Always `1` (LLM-generated code).                                                            |
+
+### 2. Human-written Dataset (`human_selected_dataset.csv`)
+| Column             | Description                                                                                    |
+|--------------------|------------------------------------------------------------------------------------------------|
+| problem_id          | Unique problem ID from CodeNet.                                                               |
+| submission_id       | Submission ID from CodeNet.                                                                   |
+| (12 CodeNet columns)| Metadata columns directly provided by CodeNet.                                                |
+| LLM                 | Always `"Human"`.                                                                             |
+| status_in_folder    | Submission status: `"wrong"`, `"runtime"`, `"accepted"`.                                      |
+| code                | Human-written code.                                                                           |
+| label               | Always `0` (Human-written code).                                                              |
+
+### 3. Final Dataset (`all_data_with_ada_embeddings_will_be_splitted_into_train_test_set.csv`)
+Merged dataset with the following additional columns:
+| Column             | Description                                       |
+|--------------------|---------------------------------------------------|
+| ada_embedding       | Ada embedding vectors of the code.               |
+| lines               | Total number of lines.                           |
+| code_lines          | Number of non-empty code lines.                  |
+| comments            | Number of comment lines.                         |
+| functions           | Number of functions in the code.                 |
+| blank_lines         | Number of blank lines.                           |
+
+*Feature extraction scripts are provided in the accompanying Python files.*
+
+## Generation Method
+- LLM outputs were generated as described in the paper: [arxiv.org/abs/2412.16594](https://arxiv.org/pdf/2412.16594)
+- Human codes were selected from CodeNet and annotated with metadata.
+- For code features, custom scripts calculated line counts, comments, functions, etc.
+
+## Licensing and Usage
+- **Dataset License:**  
+  - This dataset is shared under the CDLA Permissive v2.0 license. The accompanying source code is licensed under Apache 2.0, following
+    the licensing approach of the CodeNet Project.
+  - The code samples generated by LLMs (Meta, Google, Mistral AI) are not subject to any ownership claims by the respective model providers.
+- **Model Outputs:**  
+  - Generated code samples are considered public domain from an IP perspective.  
+  - However, no guarantees of correctness or fitness for purpose are provided.
+
+## Citation
+If you use this dataset, please cite:
+```
+@inproceedings{demirok2025aigcodeset,
+  title={Aigcodeset: A new annotated dataset for ai generated code detection},
+  author={Demirok, Basak and Kutlu, Mucahid},
+  booktitle={2025 33rd Signal Processing and Communications Applications Conference (SIU)},
+  pages={1--4},
+  year={2025},
+  organization={IEEE}
+}
+```
+
+> **Note:** This work has been accepted and presented in Turkish at [SİU 2025] (Türkiye) . 
+>  English preprint version available on arXiv (https://arxiv.org/abs/2412.16594).
+
+## Contact
+For questions or collaborations:
+- **Basak Gokce**  
+- **Email:** basakgokce15@gmail.com  

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