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---
language:
- en
license: cc-by-sa-3.0
tags:
- treecorpus
- wikipedia
- encyclopedia
- knowledge-base
- factual-knowledge
- training-data
- conversational-ai
- nlp
- language-model
- text-corpus
- qa-dataset
- structured-data
- large-scale
pretty_name: 'TreeCorpus: Wikipedia Knowledge for AI Models'
size_categories:
- 10M<n<100M
---
# TreeCorpus
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.
## Dataset Statistics
- **Size**: 26.27 GB (26,272,580,250 bytes)
- **Examples**: 2,882,766 articles
- **Download Size**: 13.33 GB (13,326,529,312 bytes)
- **Language**: English
## Data Structure
Each entry in the dataset contains:
- `id` (string): Unique Wikipedia article identifier
- `title` (string): Article title
- `text` (string): Clean, processed text content
- `url` (string): Source Wikipedia URL
- `timestamp` (string): Processing timestamp
## Key Features
- **Clean, Structured Content**: Meticulously processed to remove markup, templates, references, and other non-content elements while preserving the informational value of Wikipedia articles.
- **Rich Metadata**: Each entry includes article ID, title, clean text content, source URL, and timestamp.
- **Comprehensive Coverage**: Incorporates the full spectrum of Wikipedia's knowledge base, spanning nearly 3 million articles across countless topics.
- **Conversational Optimization**: Content is processed specifically to support training of dialogue systems, conversational agents, and knowledge-grounded language models.
- **Regular Updates**: Built from the latest Wikipedia dumps to ensure current information.
## Usage
This dataset is ideal for:
- Training large language models requiring broad knowledge bases
- Fine-tuning conversational agents for knowledge-intensive tasks
- Question-answering systems that need factual grounding
- Research in knowledge representation and retrieval in natural language
## License and Citation
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.
## Dataset Configuration
The dataset is configured with a default split:
- Split name: train
- Data files pattern: data/train-*
## Creation Process
TreeCorpus was created using a specialized pipeline that:
1. Downloads the latest Wikipedia dumps
2. Processes XML content to extract articles
3. Cleans and standardizes text by removing markup, templates, and non-content elements
4. Structures data in a consistent, machine-readable format
5. Filters out redirects, stubs, and non-article content
For more details on the methodology and processing pipeline, please see the accompanying code documentation. |