make style and update the example.

Author: ParagEkbote

transformers/transformers_simple.py

@@ -3,8 +3,8 @@
 with hyperparameter optimization using Optuna. In this example, we fine-tune a lightweight
 pre-trained BERT model on a small subset of the IMDb dataset to classify movie reviews as
 positive or negative. We optimize the validation accuracy by tuning the learning rate
-and batch size. To learn more about transformers' hyperparameter search, 
-you can check the following documentation: 
+and batch size. To learn more about transformers' hyperparameter search,
+you can check the following documentation:
 https://huggingface.co/docs/transformers/en/hpo_train.
 """
 
@@ -36,8 +36,12 @@ def tokenize(batch):
     return tokenizer(batch["text"], padding="max_length", truncation=True, max_length=512)
 
 
-tokenized_train = train_dataset.map(tokenize, batched=True).select_columns(["input_ids", "attention_mask", "label"])
-tokenized_valid = valid_dataset.map(tokenize, batched=True).select_columns(["input_ids", "attention_mask", "label"])
+tokenized_train = train_dataset.map(tokenize, batched=True).select_columns(
+    ["input_ids", "attention_mask", "label"]
+)
+tokenized_valid = valid_dataset.map(tokenize, batched=True).select_columns(
+    ["input_ids", "attention_mask", "label"]
+)
 
 
 metric = evaluate.load("accuracy")