Update README.md
Browse files
README.md
CHANGED
@@ -16,11 +16,12 @@ Requirements:
|
|
16 |
```
|
17 |
pip install zstandard python-chess datasets
|
18 |
```
|
|
|
19 |
|
20 |
# Quick Guide
|
21 |
In the following, I explain the data format and how to use the dataset. At the end, you find a complete example script.
|
22 |
|
23 |
-
### 1. Loading
|
24 |
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.
|
25 |
See [HuggingFace's documentation](https://huggingface.co/docs/datasets/main/en/load_hub#remote-code) if you're unsure.
|
26 |
```py
|
@@ -48,21 +49,23 @@ A single sample from the dataset contains one complete chess game as a dictionar
|
|
48 |
<br>
|
49 |
|
50 |
Everything but Elos is stored as strings.
|
|
|
51 |
|
52 |
-
### 3. Shuffle
|
53 |
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).
|
54 |
|
55 |
|
56 |
```py
|
57 |
# Shuffle and apply your own preprocessing.
|
58 |
dataset = dataset.shuffle(seed=42)
|
59 |
-
dataset = dataset.map(preprocess, fn_kwargs={'
|
60 |
```
|
61 |
|
62 |
-
For a quick working example, you can try to use the following
|
63 |
```py
|
64 |
-
|
65 |
-
|
|
|
66 |
|
67 |
def preprocess(example, useful_fn):
|
68 |
# Get number of moves made in the game.
|
@@ -79,4 +82,5 @@ def preprocess(example, useful_fn):
|
|
79 |
example['moves'] = useful_fn(move)
|
80 |
example['scores'] = useful_fn(score)
|
81 |
return example
|
82 |
-
```
|
|
|
|
16 |
```
|
17 |
pip install zstandard python-chess datasets
|
18 |
```
|
19 |
+
<br>
|
20 |
|
21 |
# Quick Guide
|
22 |
In the following, I explain the data format and how to use the dataset. At the end, you find a complete example script.
|
23 |
|
24 |
+
### 1. Loading The Dataset
|
25 |
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.
|
26 |
See [HuggingFace's documentation](https://huggingface.co/docs/datasets/main/en/load_hub#remote-code) if you're unsure.
|
27 |
```py
|
|
|
49 |
<br>
|
50 |
|
51 |
Everything but Elos is stored as strings.
|
52 |
+
<br>
|
53 |
|
54 |
+
### 3. Shuffle And Preprocess
|
55 |
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).
|
56 |
|
57 |
|
58 |
```py
|
59 |
# Shuffle and apply your own preprocessing.
|
60 |
dataset = dataset.shuffle(seed=42)
|
61 |
+
dataset = dataset.map(preprocess, fn_kwargs={'tokenizer': tokenizer})
|
62 |
```
|
63 |
|
64 |
+
For a quick working example, you can try to use the following:
|
65 |
```py
|
66 |
+
class Tokenizer:
|
67 |
+
def __call__(self, example):
|
68 |
+
return example
|
69 |
|
70 |
def preprocess(example, useful_fn):
|
71 |
# Get number of moves made in the game.
|
|
|
82 |
example['moves'] = useful_fn(move)
|
83 |
example['scores'] = useful_fn(score)
|
84 |
return example
|
85 |
+
```
|
86 |
+
<br>
|