Update README.md

README.md

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----
-license: cc-by-sa-3.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: title
-    dtype: string
-  - name: text
-    dtype: string
-  - name: url
-    dtype: string
-  - name: timestamp
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 26272580250
-    num_examples: 2882766
-  download_size: 13326529312
-  dataset_size: 26272580250
-language:
-- en
-tags:
-- treecorpus
-- wikipedia
-- encyclopedia
-- knowledge-base
-- factual-knowledge
-- training-data
-pretty_name: 'TreeCorpus: Wikipedia Knowledge for AI Models'
----
+# 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.