Update README.md

README.md

@@ -57,7 +57,7 @@ pretty_name: U3T Monte Carlo Tree Search Position Evaluations
 size_categories:
 - 1M<n<10M
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-# Dataset Card for Dataset Name
+# Dataset Card for U3T
 
 This dataset stores refactored data of [Monte Carlo Search Tree Evaluations](https://en.wikipedia.org/wiki/Monte_Carlo_tree_search) edited from arnowaczynski's [utttai](https://github.com/arnowaczynski/utttai), for the game [Ultimate-tic-tac-toe](https://en.wikipedia.org/wiki/Ultimate_tic-tac-toe) (U3T).
 One can use this data to train a model to evaluate positions or predict moves.
@@ -67,12 +67,12 @@ One can use this data to train a model to evaluate positions or predict moves.
 This dataset contains over 8 million evaluated positions at varying depth (via [utttai](https://github.com/arnowaczynski/utttai/tree/main/datasets)):  
 ![Depth Graph from UTTTAI](assets/depth_graph.png)  
 
-Each evaluated positions counts the number of wins, draws, and loses, giving an estimation as to how good a position is. Each position stores an array of moves, and their respective
+Each evaluated positions counts the number of wins, draws, and loses, giving an estimation as to how good a position is. Each position stores an array of legal moves, and their respective
 MCTS searches.
 
 ## Uses
 
-One can use this dataset in an way they want. But, it is mainly intended for making a deep learning model to play U3T.
+One can use this dataset in an way they want. But, it is mainly intended for making a deep learning model to play U3T, or evaluate a static position.
 
 ## Dataset Structure
 
@@ -118,7 +118,10 @@ This dataset stores evaluations for game positions in Ultimate Tic-Tac-Toe. Each
 
 ## Dataset Creation
 
-Documentation on the generation of this dataset can be found [here](https://github.com/markstanl/uttt-bots/tree/main/data/hugging_face).
+Documentation on the generation of this dataset can be found [here](https://github.com/markstanl/uttt-bots/tree/main/data/hugging_face). 
+Roughly, we converted the UTTTAI gamestate to the standardized version documented before. Processed the txt file into JSONL files for each depth.
+Converted each JSON line in the JSONL to a dictionary with the new key of depth. Appended a list with all of the dictionaries.
+Stored it as a large parquet file. Then split the data accordingly.
 
 ### Dataset Splitting
 
@@ -133,10 +136,10 @@ The dataset was split into a train, test, and validation set, with a distributio
 ### Curation Rationale
 
 The original dataset by arnowaczynski was not stored in a conventional big data format, and used some unconventional index storing [(detailed here)](https://github.com/markstanl/uttt-bots/blob/main/utttai_conversion/utttai.md).
-So, we decided to refactor it and upload it here, to Hugging Face. 
+So, we decided to refactor it and upload it here, to Hugging Face, in a dataset that is more efficient for NN training.
 
 
 ### Source Data
 
-arnowaczynski generated these datasets in their [GitHub Repository](https://github.com/arnowaczynski/utttai/tree/main/datasets). Further documentation on how specifically
+arnowaczynski generated these datasets in their [GitHub Repository](https://github.com/arnowaczynski/utttai/tree/main/datasets). Further documentation on how
 the dataset was generated is available at that link.