Update README.md

README.md

@@ -10,16 +10,15 @@ Hello, everyone!
 <br>
 I will give you a quick overview of the data format and a guide on how to use the dataset.
 I always appreciate feedback and discussions. You can speak out [here](https://huggingface.co/datasets/mauricett/lichess_sf/discussions).
-<br>
 And now, enjoy...
 <br>
 <br>
 
 # Condensed Lichess Database
 This dataset is a condensed version of the Lichess database.
-It includes all games for which Stockfish evaluations were available.
-Games are stored in a format that is much faster to process than the original PGN data.
+It only includes games for which Stockfish evaluations were available.
 Currently, the dataset contains the entire year 2023, which consists of >100M games and >1B positions.
+Games are stored in a format that is much faster to process than the original PGN data.
 <br>
 <br>
 
@@ -27,13 +26,13 @@ Currently, the dataset contains the entire year 2023, which consists of >100M ga
 ```
     pip install zstandard python-chess
 ```
-<br>
 
-# Data Format
-A single sample from the dataset contains an entire chess game as a dictionary. The dictionary has the k
+
+
 
 # Quick Quide
-Using this dataset should be straightforward, but let me give you a quick tour.
+Using this dataset should be straightforward, but let me give you a quick tour. At the end, you find a complete example script.
+
 ### 1. Loading the dataset
 I recommend streaming the data, because the dataset is rather large (~100 GB) and I will expand it in the future.
 Note, `trust_remote_code=True` is needed to execute my [custom data loading script](https://huggingface.co/datasets/mauricett/lichess_sf/blob/main/lichess_sf.py), which is necessary to decompress the files.
@@ -45,6 +44,15 @@ See [HuggingFace's documentation](https://huggingface.co/docs/datasets/main/en/l
                            streaming=True,
                            trust_remote_code=True)
 ```
+<br>
+
+# Data Format
+After loading the dataset, you can already check out how the samples look like:
+```py
+example = next(iter(dataset))
+```
+A single sample from the dataset contains an entire chess game as a dictionary. The dictionary has the k
+
 
 ### Usage
 To use the dataset, apply `datasets.shuffle()` and your own transformations (e.g. tokenizer) using `datasets.map()`. The latter will process individual samples in parallel if you're using multiprocessing (e.g. with PyTorch dataloader).