Update README.md

README.md

@@ -7,17 +7,27 @@ pretty_name: Lichess Games With Stockfish Analysis
 ---
 # Condensed Lichess Database
 This dataset is a condensed version of the Lichess database.
-It only includes games for which Stockfish evaluations were available, and it does not include game headers.
-The resulting files are much smaller and faster to parse.
+It only includes games for which Stockfish evaluations were available, and games are stored in a format that is faster to parse than the original PGN data.
+Currently, the dataset contains the entire year 2023, which consists of >100M games and >1B positions.
 
 # Requirements
-The dataset is compressed with `zstandard` and requires the `python-chess` library.
 ```
     pip install zstandard python-chess
 ```
 
 # Quick guide
-Hello there! 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.
+### 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.
+See [HuggingFace's documentation](https://huggingface.co/docs/datasets/main/en/load_hub#remote-code) if you're unsure.
+```py
+    # Load dataset.
+    dataset = load_dataset(path="../FishData/lichess_sf_test.py",
+                           split="train",
+                           streaming=True,
+                           trust_remote_code=True)
+```
 
 ### 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).