Update README.md

README.md

@@ -44,7 +44,7 @@ A single sample from the dataset contains one complete chess game as a dictionar
 
 1. `example['fens']` --- A list of FENs in a slightly stripped format, missing the halfmove clock and fullmove number (see [definitions on wiki](https://en.wikipedia.org/wiki/Forsyth%E2%80%93Edwards_Notation#Definition)). The starting positions have been excluded (no player made a move yet).
 2. `example['moves']` --- A list of moves in [UCI format](https://en.wikipedia.org/wiki/Universal_Chess_Interface). `example['moves'][42]` is the move that led to position `example['fens'][42]`, etc.
-3. `example['scores']` --- A list of Stockfish evaluations (in centipawns) from the perspective of the player who is next to move. If `example['fens'][42]` is black's move, `example['scores'][42]` will be from black's perspective. If the game ended with a terminal condition, the last element of the list is a string 'C' (checkmate), 'S' (stalemate) or 'I' (insufficient material). Games with other outcome conditions have been excluded.
+3. `example['scores']` --- A list of Stockfish evaluations (in centipawns) from the perspective of the player who is next to move. If `example['fens'][42]` is black's turn, `example['scores'][42]` will be from black's perspective. If the game ended with a terminal condition, the last element of the list is a string 'C' (checkmate), 'S' (stalemate) or 'I' (insufficient material). Games with other outcome conditions have been excluded.
 4. `example['WhiteElo'], example['BlackElo']` --- Player's Elos.
 <br>
 
@@ -83,4 +83,56 @@ def preprocess(example, useful_fn):
     example['scores'] = useful_fn(score)
     return example
 ```
-<br>
+<br>
+<br>
+<br>
+# Complete Example
+
+```py
+import random
+import datasets
+
+# Shuffle and apply your own preprocessing.
+dataset = dataset.shuffle(seed=42)
+dataset = dataset.map(preprocess, fn_kwargs={'tokenizer': tokenizer})
+```
+
+For a quick working example, you can try to use the following:
+```py
+# A mock tokenizer and preprocess function for demonstration.
+class Tokenizer:
+    def __call__(self, example):
+        return example
+
+def preprocess(example, useful_fn):
+    # Get number of moves made in the game.
+    max_ply = len(example['moves']) 
+    pick_random_move = random.randint(0, max_ply)
+
+    # 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'] = useful_fn(fen)
+    example['moves'] = useful_fn(move)
+    example['scores'] = useful_fn(score)
+    return example
+
+tokenizer = Tokenizer()
+
+# Load dataset.
+dataset = load_dataset(path="mauricett/lichess_sf",
+                       split="train",
+                       streaming=True,
+                       trust_remote_code=True)
+
+# Shuffle and apply your own preprocessing.
+dataset = dataset.shuffle(seed=42)
+dataset = dataset.map(preprocess, fn_kwargs={'tokenizer': tokenizer})
+
+for batch in dataset:
+  # do stuff
+  break
+```