Update README.md

README.md

@@ -34,12 +34,6 @@ dataset = load_dataset(path="mauricett/lichess_sf",
 <br>
 
 ### 2. Data Format
-After loading the dataset, you can check how the samples look like:
-```py
-example = next(iter(dataset))
-print(example)
-```
-
 A single sample from the dataset contains one complete chess game as a dictionary. The dictionary keys are as follows:
 
 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).
@@ -98,39 +92,6 @@ dataset = dataset.shuffle(seed=42)
 dataset = dataset.map(preprocess, fn_kwargs={'tokenizer': tokenizer,
                                              'score_fn': score_fn})
 ```
-
-In this example, we're passing two additional arguments to the preprocess function in dataset.map(). You can use the following mock examples for inspiration:
-```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>
 <br>
 <br>
@@ -188,7 +149,7 @@ dataset = dataset.map(preprocess, fn_kwargs={'tokenizer': tokenizer,
                                              'score_fn': score_fn})
 
 # PyTorch dataloader
-dataloader = DataLoader(dataset, batch_size=256, num_workers=4)
+dataloader = DataLoader(dataset, batch_size=1, num_workers=1)
 
 
 n = 0
@@ -196,8 +157,6 @@ for batch in dataloader:
 
     # do stuff
 
-    n += 1
-    print(n)
-    if n == 50:
-        break
+    print(batch)
+    break
 ```