lint

data/ai-aktindsigt/create.py

@@ -17,6 +17,7 @@ from datasets import Dataset, load_dataset
 
 source = "ai-aktindsigt"
 
+
 def convert_sample(example):
     # {'text': 'Vallensbæk Stationstorv 100 2665 Vallensbæk Strand Telefon: +45 4797 4000',
     # 'id': '0_03fe7662f6d37df0ffbf5013907414f935350db9931043891a95ed830965a507a7bcb4df93741429bdfa4958cf25f6c273aa73146f2be80948f767eb5fa04645',

src/update_descriptive_statistics.py

@@ -32,6 +32,7 @@ repo_path = Path(__file__).parent.parent
 tokenizer_name = "AI-Sweden-Models/Llama-3-8B-instruct"
 tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, use_fast=True)
 
+
 def human_readable_large_int(value: int) -> str:
     thresholds = [
         (1_000_000_000, "B"),
@@ -249,7 +250,7 @@ def update_dataset(
     add_desc_statitics(markdown_path, ds, desc_stats_path, num_proc=num_proc)
     add_sample(markdown_path, ds)
     add_descriptive_statistics_plots(markdown_path, ds)
-    
+
 
 def create_parser():
     parser = argparse.ArgumentParser(