diff --git a/README.md b/README.md
index 3d0e529..b0803ec 100644
--- a/README.md
+++ b/README.md
@@ -183,7 +183,7 @@ Several folders contain optional materials as a bonus for interested readers:
   - [PyTorch Performance Tips for Faster LLM Training](ch05/10_llm-training-speed)
 - **Chapter 6: Finetuning for classification**
   - [Additional experiments finetuning different layers and using larger models](ch06/02_bonus_additional-experiments)
-  - [Finetuning different models on 50k IMDB movie review dataset](ch06/03_bonus_imdb-classification)
+  - [Finetuning different models on 50k IMDb movie review dataset](ch06/03_bonus_imdb-classification)
   - [Building a User Interface to Interact With the GPT-based Spam Classifier](ch06/04_user_interface)
 - **Chapter 7: Finetuning to follow instructions**
   - [Dataset Utilities for Finding Near Duplicates and Creating Passive Voice Entries](ch07/02_dataset-utilities)
diff --git a/ch06/03_bonus_imdb-classification/README.md b/ch06/03_bonus_imdb-classification/README.md
index 08bd939..6cfdcd5 100644
--- a/ch06/03_bonus_imdb-classification/README.md
+++ b/ch06/03_bonus_imdb-classification/README.md
@@ -1,4 +1,4 @@
-# Additional Experiments Classifying the Sentiment of 50k IMDB Movie Reviews
+# Additional Experiments Classifying the Sentiment of 50k IMDb Movie Reviews
 
 ## Overview
 
diff --git a/ch06/03_bonus_imdb-classification/train_bert_hf.py b/ch06/03_bonus_imdb-classification/train_bert_hf.py
index df359c0..037cfb7 100644
--- a/ch06/03_bonus_imdb-classification/train_bert_hf.py
+++ b/ch06/03_bonus_imdb-classification/train_bert_hf.py
@@ -15,7 +15,7 @@ from torch.utils.data import Dataset
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 
-class IMDBDataset(Dataset):
+class IMDbDataset(Dataset):
     def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256, use_attention_mask=False):
         self.data = pd.read_csv(csv_file)
         self.max_length = max_length if max_length is not None else self._longest_encoded_length(tokenizer)
@@ -375,21 +375,21 @@ if __name__ == "__main__":
     else:
         raise ValueError("Invalid argument for `use_attention_mask`.")
 
-    train_dataset = IMDBDataset(
+    train_dataset = IMDbDataset(
         base_path / "train.csv",
         max_length=256,
         tokenizer=tokenizer,
         pad_token_id=tokenizer.pad_token_id,
         use_attention_mask=use_attention_mask
     )
-    val_dataset = IMDBDataset(
+    val_dataset = IMDbDataset(
         base_path / "validation.csv",
         max_length=256,
         tokenizer=tokenizer,
         pad_token_id=tokenizer.pad_token_id,
         use_attention_mask=use_attention_mask
     )
-    test_dataset = IMDBDataset(
+    test_dataset = IMDbDataset(
         base_path / "test.csv",
         max_length=256,
         tokenizer=tokenizer,
diff --git a/ch06/03_bonus_imdb-classification/train_gpt.py b/ch06/03_bonus_imdb-classification/train_gpt.py
index 99091d3..b650634 100644
--- a/ch06/03_bonus_imdb-classification/train_gpt.py
+++ b/ch06/03_bonus_imdb-classification/train_gpt.py
@@ -17,7 +17,7 @@ from gpt_download import download_and_load_gpt2
 from previous_chapters import GPTModel, load_weights_into_gpt
 
 
-class IMDBDataset(Dataset):
+class IMDbDataset(Dataset):
     def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256):
         self.data = pd.read_csv(csv_file)
         self.max_length = max_length if max_length is not None else self._longest_encoded_length(tokenizer)
@@ -368,7 +368,7 @@ if __name__ == "__main__":
     if args.context_length == "model_context_length":
         max_length = model.pos_emb.weight.shape[0]
     elif args.context_length == "longest_training_example":
-        train_dataset = IMDBDataset(base_path / "train.csv", max_length=None, tokenizer=tokenizer)
+        train_dataset = IMDbDataset(base_path / "train.csv", max_length=None, tokenizer=tokenizer)
         max_length = train_dataset.max_length
     else:
         try:
@@ -377,9 +377,9 @@ if __name__ == "__main__":
             raise ValueError("Invalid --context_length argument")
 
     if train_dataset is None:
-        train_dataset = IMDBDataset(base_path / "train.csv", max_length=max_length, tokenizer=tokenizer)
-    val_dataset = IMDBDataset(base_path / "validation.csv", max_length=max_length, tokenizer=tokenizer)
-    test_dataset = IMDBDataset(base_path / "test.csv", max_length=max_length, tokenizer=tokenizer)
+        train_dataset = IMDbDataset(base_path / "train.csv", max_length=max_length, tokenizer=tokenizer)
+    val_dataset = IMDbDataset(base_path / "validation.csv", max_length=max_length, tokenizer=tokenizer)
+    test_dataset = IMDbDataset(base_path / "test.csv", max_length=max_length, tokenizer=tokenizer)
 
     num_workers = 0
     batch_size = 8
diff --git a/ch06/README.md b/ch06/README.md
index 65be918..26452f3 100644
--- a/ch06/README.md
+++ b/ch06/README.md
@@ -9,7 +9,7 @@
 ## Bonus Materials
 
 - [02_bonus_additional-experiments](02_bonus_additional-experiments) includes additional experiments (e.g., training the last vs first token, extending the input length, etc.)
-- [03_bonus_imdb-classification](03_bonus_imdb-classification) compares the LLM from chapter 6 with other models on a 50k IMDB movie review sentiment classification dataset
+- [03_bonus_imdb-classification](03_bonus_imdb-classification) compares the LLM from chapter 6 with other models on a 50k IMDb movie review sentiment classification dataset
 - [04_user_interface](04_user_interface) implements an interactive user interface to interact with the pretrained LLM