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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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- 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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- large-scale |
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pretty_name: 'TreeCorpus: Wikipedia Knowledge for AI Models' |
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size_categories: |
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- 10M<n<100M |
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--- |
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# TreeCorpus |
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TreeCorpus is a comprehensive, structured dataset derived from the latest Wikipedia dumps, specially processed to serve as high-quality training data for conversational AI models. This dataset transforms Wikipedia's encyclopedic knowledge into a format optimized for natural language understanding and generation tasks. |
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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): Clean, processed text content |
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- `url` (string): Source Wikipedia URL |
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- `timestamp` (string): Processing timestamp |
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## Key Features |
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- **Clean, Structured Content**: Meticulously processed to remove markup, templates, references, and other non-content elements while preserving the informational value of Wikipedia articles. |
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- **Rich Metadata**: Each entry includes article ID, title, clean text content, source URL, and timestamp. |
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- **Comprehensive Coverage**: Incorporates the full spectrum of Wikipedia's knowledge base, spanning nearly 3 million articles across countless topics. |
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- **Conversational Optimization**: Content is processed specifically to support training of dialogue systems, conversational agents, and knowledge-grounded language models. |
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- **Regular Updates**: Built from the latest Wikipedia dumps to ensure current information. |
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## Usage |
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This dataset is ideal for: |
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- Training large language models requiring broad knowledge bases |
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- Fine-tuning conversational agents for knowledge-intensive tasks |
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- Question-answering systems that need factual grounding |
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- Research in knowledge representation and retrieval in natural language |
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## License and Citation |
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TreeCorpus 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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TreeCorpus was created using a specialized pipeline that: |
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1. Downloads the latest Wikipedia dumps |
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2. Processes XML content to extract articles |
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3. Cleans and standardizes text by removing markup, templates, and non-content elements |
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4. Structures data in a consistent, machine-readable format |
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5. Filters out redirects, stubs, and non-article content |
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For more details on the methodology and processing pipeline, please see the accompanying code documentation. |
