{"id":22911382,"url":"https://github.com/shreypandit/llm-database-cleaning","last_synced_at":"2025-10-18T10:14:06.981Z","repository":{"id":267555720,"uuid":"901621505","full_name":"ShreyPandit/LLM-Database-Cleaning","owner":"ShreyPandit","description":"DBMS project for llm cleaning on a dataset","archived":false,"fork":false,"pushed_at":"2024-12-11T15:13:03.000Z","size":2536,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-07T06:13:50.996Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ShreyPandit.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-11T02:18:35.000Z","updated_at":"2024-12-11T15:13:07.000Z","dependencies_parsed_at":"2024-12-11T03:33:13.293Z","dependency_job_id":null,"html_url":"https://github.com/ShreyPandit/LLM-Database-Cleaning","commit_stats":null,"previous_names":["shreypandit/llm-database-cleaning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FLLM-Database-Cleaning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FLLM-Database-Cleaning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FLLM-Database-Cleaning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShreyPandit%2FLLM-Database-Cleaning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ShreyPandit","download_url":"https://codeload.github.com/ShreyPandit/LLM-Database-Cleaning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246628417,"owners_count":20808106,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-12-14T04:16:38.915Z","updated_at":"2025-10-07T09:19:11.676Z","avatar_url":"https://github.com/ShreyPandit.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Text Data Noise Generator and Cleaner\n\nA toolkit for generating and cleaning data quality issues in text, designed for educational and testing purposes.\n\n## Project Overview\n\nThis project provides tools to:\n1. Generate realistic data quality issues in clean text\n2. Clean noisy text data using prompt engineering\n3. Test and validate data cleaning approaches\n\n## Components\n\n### 1. Noise Generator (`noise_generator.py`)\nIntroduces controlled noise into clean text data:\n- Unicode corruption and invisible characters\n- Random string injection\n- Word duplication patterns\n- HTML/XML artifacts\n- Control characters and encoding issues\n- Whitespace corruption\n\n### 2. Cleaning Prompt (`cleaning_prompt.txt`)\nPrompt for Large Language Models to clean noisy text:\n- Detailed cleaning instructions\n- Two-shot examples\n- Specific noise type handling\n\n## Requirements\n\n```bash\nPython 3.7+\n```\n\n## Installation\n\n```bash\ngit clone https://github.com/yourusername/text-data-noise-toolkit.git\ncd text-data-noise-toolkit\npip install -r requirements.txt\n```\n\n## Usage\n\n### Adding Noise to Clean Data\n\n```python\nfrom noise_generator import add_noise_to_text\n\nadd_noise_to_text(\n    text_file='clean_data.txt',\n    output_file='noisy_data.txt',\n    noise_probability=0.3\n)\n```\n\n### Cleaning Noisy Data\n1. Copy the cleaning prompt from `cleaning_prompt.txt`\n2. Use it with your preferred LLM\n3. Input your noisy text for cleaning\n\n## Example\n\nInput (Clean):\n```\nThe quick brown fox jumps over the lazy dog.\n```\n\nWith Noise:\n```\nThe quick⁠ brown\\u200B fox‎ jumps jumps jumps jumps over the lazy dog \u0026nbsp; #R$T2k9pL@\n```\n\nCleaned:\n```\nThe quick brown fox jumps over the lazy dog\n```\n\n## Configuration\n\nAdjust noise parameters in `noise_generator.py`:\n```python\nnoise_probability = 0.3  # 30% chance of noise per line\nnoise_functions = [add_unicode_noise, add_random_string]  # Select noise types\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshreypandit%2Fllm-database-cleaning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshreypandit%2Fllm-database-cleaning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshreypandit%2Fllm-database-cleaning/lists"}