Update README.md

README.md

@@ -16,11 +16,12 @@ Requirements:
 ```
     pip install zstandard python-chess datasets
 ```
+<br>
 
 # Quick Guide
 In the following, I explain the data format and how to use the dataset. At the end, you find a complete example script.
 
-### 1. Loading the dataset
+### 1. Loading The Dataset
 You can stream the data without storing it locally (~100 GB currently). The dataset requires `trust_remote_code=True` to execute the [custom data loading script](https://huggingface.co/datasets/mauricett/lichess_sf/blob/main/lichess_sf.py), which is necessary to decompress the files.
 See [HuggingFace's documentation](https://huggingface.co/docs/datasets/main/en/load_hub#remote-code) if you're unsure.
 ```py
@@ -48,21 +49,23 @@ A single sample from the dataset contains one complete chess game as a dictionar
 <br>
 
 Everything but Elos is stored as strings.
+<br>
 
-### 3. Shuffle and preprocess
+### 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).
 
 
 ```py
 # Shuffle and apply your own preprocessing.
 dataset = dataset.shuffle(seed=42)
-dataset = dataset.map(preprocess, fn_kwargs={'useful_fn': useful_fn})
+dataset = dataset.map(preprocess, fn_kwargs={'tokenizer': tokenizer})
 ```
 
-For a quick working example, you can try to use the following functions:
+For a quick working example, you can try to use the following:
 ```py
-def useful_fn(example):
-    return example
+class Tokenizer:
+    def __call__(self, example):
+        return example
 
 def preprocess(example, useful_fn):
     # Get number of moves made in the game.
@@ -79,4 +82,5 @@ def preprocess(example, useful_fn):
     example['moves'] = useful_fn(move)
     example['scores'] = useful_fn(score)
     return example
-```
+```
+<br>