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README.md
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---
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language:
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- en
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license: cc-by-sa-3.0
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tags:
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- treecorpus
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- treecorpuscleaned
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- wikipedia
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- encyclopedia
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- knowledge-base
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- factual-knowledge
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- training-data
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- conversational-ai
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- nlp
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- language-model
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- text-corpus
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- qa-dataset
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- structured-data
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- clean-text
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pretty_name: 'TreeCorpusCleaned: Basic Wikipedia Text Cleanup for AI Models'
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size_categories:
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- 10M<n<100M
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---
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# TreeCorpusCleaned
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TreeCorpusCleaned is a modestly improved version of the TreeCorpus dataset with some additional basic cleaning to help reduce Wikipedia markup artifacts. This dataset provides a slightly cleaner version of Wikipedia content for training AI models.
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## Dataset Statistics
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- **Size**: 26.27 GB (26,272,580,250 bytes)
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- **Examples**: 2,882,766 articles
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- **Download Size**: 13.33 GB (13,326,529,312 bytes)
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- **Language**: English
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## Data Structure
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Each entry in the dataset contains:
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- `id` (string): Unique Wikipedia article identifier
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- `title` (string): Article title
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- `text` (string): Text with basic additional cleanup
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- `url` (string): Source Wikipedia URL
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- `timestamp` (string): Processing timestamp
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## Basic Additional Cleaning
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TreeCorpusCleaned includes some simple improvements over the original TreeCorpus:
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- **Basic Reference Cleanup**: Additional removal of common reference markers
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- **Simple Template Handling**: Some extra template cleanup
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- **Formatting Standardization**: Basic text formatting standardization
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## Usage
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This dataset is suitable for:
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- Training language models with slightly cleaner Wikipedia text
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- NLP tasks that benefit from reduced markup artifacts
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- Projects requiring basic preprocessing of Wikipedia content
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## License and Citation
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TreeCorpusCleaned is derived from Wikipedia content available under the CC BY-SA 3.0 license. When using this dataset, please provide appropriate attribution to both this dataset and Wikipedia.
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## Dataset Configuration
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The dataset is configured with a default split:
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- Split name: train
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- Data files pattern: data/train-*
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## Creation Process
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TreeCorpusCleaned was created by applying some additional basic cleaning steps to the original TreeCorpus dataset to remove common artifacts and improve text quality.
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