Update README.md

# 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.

## 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 millions of 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.

## Dataset Structure

Each entry in the dataset contains:
- `id`: Unique Wikipedia article identifier
- `title`: Article title
- `text`: Clean, processed text content
- `url`: Source Wikipedia URL
- `timestamp`: Processing timestamp

## 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

## Citation and License

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.

## 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 documentation.

README.md

@@ -1,26 +1,36 @@
----
-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
----
+---
+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'
+---