Update README.md

README.md

@@ -55,30 +55,22 @@ task_categories:
 This dataset was created using the synthetic AI vs AI training app found in /generator/. The app simulates games 
 of chess between 2 web workers in a front-end page to train RL datasets. Download it to train your own similar datasets.
 
-This dataset is for testing and *UNDER DEVELOPMENT* 
-
-## Export Information
-
 - Export Date: 2026-01-02T22:48:02.105Z
 - Total Training Games: 0
 - Total Moves: 11
 - Training Time: 00:00:12
 
-## Files Included
-
-### 1. training_games.json
-
 Complete dataset of all chess games played during training. Each game includes:
 - Full PGN notation
 - Move-by-move records
 - Game result and metadata
 - Agent parameters for each game
 
-### 2. training_games.csv
+### training_games.csv
 
 Same data as JSON but in CSV format for easy import into spreadsheets or databases.
 
-### 3. black_agent_model.json
+### black_agent_model.json
 
 Black Agent (Policy Network) configuration and statistics:
 - Neural network architecture
@@ -86,7 +78,7 @@ Black Agent (Policy Network) configuration and statistics:
 - Training statistics (wins, losses, draws)
 - Model metadata
 
-### 4. green_agent_model.json
+### green_agent_model.json
 
 Green Agent (Value Network) configuration and statistics:
 - Neural network architecture
@@ -94,7 +86,7 @@ Green Agent (Value Network) configuration and statistics:
 - Training statistics
 - Model metadata
 
-### 5. training_statistics.json
+### training_statistics.json
 
 Overall training summary and statistics including:
 - Training duration
@@ -102,20 +94,12 @@ Overall training summary and statistics including:
 - System information
 - Export metadata
 
-## Training System
-
-Generated by ANN Chess RL Trainer v3.0 by webXOS - A web-based reinforcement learning system for chess AI development.
-
 ## Usage
 
-These files can be used to:
 - Continue training from this point
 - Analyze the learning progress
 - Import into other machine learning frameworks
 - Share with the research community
-
-## Notes
-
 - All data is in standard JSON/CSV formats
 - Compatible with Hugging Face datasets
 - Can be compressed with GZIP, ZSTD, BZ2, LZ4, or LZMA for upload