Update README.md

README.md

@@ -51,8 +51,45 @@ A single sample from the dataset contains one complete chess game as a dictionar
 Everything but Elos is stored as strings.
 <br>
 
-### 3. Shuffle And Preprocess
-Use `datasets.shuffle()` to properly shuffle the dataset. Use `datasets.map()` to transform the data to the format you require. This will process individual samples in parallel if you're using multiprocessing (e.g. with PyTorch dataloader).
+### 3. Define Functions for Preprocessing
+To use the data, you will require to define your own functions for transforming the data into your desired format.
+For this guide, let's define a few mock functions so I can show you how to use them.
+
+```py
+# A mock tokenizer and functions for demonstration.
+class Tokenizer:
+    def __init__(self):
+        pass
+    def __call__(self, example):
+        return example
+
+
+# Transform Stockfish score and terminal outcomes.
+def score_fn(score):
+    return score
+
+def preprocess(example, tokenizer, score_fn):
+    # Get number of moves made in the game.
+    max_ply = len(example['moves']) 
+    pick_random_move = random.randint(0, max_ply-1)
+
+    # Get the FEN, move and score for our random choice.
+    fen = example['fens'][pick_random_move]
+    move = example['moves'][pick_random_move]
+    score = example['scores'][pick_random_move]
+
+    # Transform data into the format of your choice.
+    example['fens'] = tokenizer(fen)
+    example['moves'] = tokenizer(move)
+    example['scores'] = score_fn(score)
+    return example
+
+tokenizer = Tokenizer()
+```
+<br>
+
+### 4. Shuffle And Preprocess
+Use `datasets.shuffle()` to properly shuffle the dataset. Use `datasets.map()` to apply our preprocessors. This will process individual samples in parallel if you're using multiprocessing (e.g. with PyTorch dataloader).
 
 
 ```py
@@ -98,7 +135,7 @@ tokenizer = Tokenizer()
 <br>
 <br>
 
-# Complete Example
+# COMPLETE EXAMPLE
 You can try pasting this into Colab and it should work fine. Have fun!
 
 ```py