Update README.md

README.md

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 # OnlySports Dataset
 
-> **Note:** This README will be updated shortly. For now, please refer to [OnlySportsLM](https://github.com/chrischenhub/OnlySportsLM) for the most up-to-date information.
+## Overview
 
-## About
+OnlySports Dataset is a comprehensive collection of English sports documents, comprising a diverse range of content including news articles, blogs, match reports, interviews, and tutorials. This dataset is part of the larger OnlySports collection, which includes:
 
-OnlySports Dataset is a comprehensive collection of English sports documents. This dataset comprises a diverse range of content including news articles, blogs, match reports, interviews, and tutorials, all extracted from the FineWeb dataset.
+1. [OnlySportsLM](https://huggingface.co/Chrisneverdie/OnlySportsLM_196M): A 196M parameter sports-domain language model
+2. [OnlySports Dataset](https://huggingface.co/collections/Chrisneverdie/onlysports-66b3e5cf595eb81220cc27a6): The dataset described in this README
+3. OnlySports Benchmark: A novel evaluation method for assessing sports knowledge generation
 
-## Dataset Information
+## Dataset Specifications
 
-- **Source:** FineWeb dataset (a cleaned and deduplicated subset of CommonCrawl)
-- **Time Span:** 2013 to present
 - **Size:** 1.2 TB of text
 - **Token Count:** Approximately 600 billion RWKV/GPT2 tokens
+- **Time Span:** 2013 to present
+- **Source:** Extracted from the FineWeb dataset, a cleaned and deduplicated subset of CommonCrawl
+
+## Data Pipeline
 
-## Extraction Process
+The creation of the OnlySports Dataset involved a two-step process:
+
+1. **URL Filtering:**
+   - Applied a list of sports-related keywords to efficiently identify potentially relevant content
+   - Keywords included general sports terms, major leagues, organizations, events, brands, and media
+   - This step reduced the dataset size by approximately 85%
+
+2. **Custom Sports Text Classifier:**
+   - Developed a [specialized classifier](https://huggingface.co/Chrisneverdie/OnlySports_Classifier) to accurately identify and extract sports-related documents
+   - Based on the Snowflake-arctic-embed-xs model with an added binary classification layer
+   - Achieved 99% accuracy in distinguishing between sports and non-sports documents
 
-1. **URL Filtering:** Applied to identify potentially relevant content
-2. **[OnlySports Classifier](https://huggingface.co/Chrisneverdie/OnlySports_Classifier):** Developed to accurately identify and extract sports-related documents
 
 ## Significance
 
-The OnlySports Dataset is currently the largest sport domain dataset, surpassing previous collections in both scale and comprehensiveness. It represents one of the best open-source datasets for LLM training in the sports domain.
+The OnlySports Dataset represents a major advancement in sports-related text data:
+
+- Largest sport domain dataset to date
+- Significantly surpasses previous collections in both scale and comprehensiveness
+- Offers researchers and developers an unprecedented resource for training language models and conducting sports-related NLP tasks
+
+## Usage and Applications
 
-## Future Updates
+The OnlySports Dataset can be used for various purposes, including:
 
-This README will be updated with more detailed information. Please check back later or refer to the link provided above for the most current details.
+- Training domain-specific language models for sports
+- Conducting research on sports-related natural language processing tasks
+- Developing applications for sports content analysis and generation
+
+## OnlySportsLM
+
+As part of the OnlySports collection, the [OnlySportsLM](https://huggingface.co/Chrisneverdie/OnlySportsLM_196M) was trained on this dataset. Key features of the model include:
+
+- 196M parameters
+- Based on the RWKV-v6 architecture
+- 20-layer, 640-dimension structure
+- Trained on approximately half of the OnlySports Dataset (315B tokens)
+
+For more information on the model and its performance, please refer to the [OnlySportsLM GitHub repository](https://github.com/chrischenhub/OnlySportsLM).
+
+## Citation
+
+If you use the OnlySports Dataset in your research, please cite our paper (citation details to be added upon publication).
 
 ## Contact
 
-For more information, visit [https://github.com/chrischenhub/OnlySportsLM](https://github.com/chrischenhub/OnlySportsLM).
+For more information or inquiries about the OnlySports Dataset, please visit our [GitHub repository](https://github.com/chrischenhub/OnlySportsLM) or contact Chris Zexin Chen (zc2404@nyu.edu).
+
+---