diff --git a/README.md b/README.md
index bc2bf21..b6a8057 100644
--- a/README.md
+++ b/README.md
@@ -35,16 +35,13 @@ end
 ### Load a Dataset from Hugging Face
 
 ```elixir
-# Load the IMDB dataset
 {:ok, dataset} = ElixirDatasets.load_dataset({:hf, "imdb"})
 
-# Load a specific split
 {:ok, train_data} = ElixirDatasets.load_dataset(
   {:hf, "imdb"},
   split: "train"
 )
 
-# Load a specific configuration
 {:ok, dataset} = ElixirDatasets.load_dataset(
   {:hf, "glue"},
   name: "sst2",
@@ -55,14 +52,12 @@ end
 ### Stream Large Datasets
 
 ```elixir
-# Stream data without loading everything into memory
 {:ok, stream} = ElixirDatasets.load_dataset(
   {:hf, "c4"},
   split: "train",
   streaming: true
 )
 
-# Process first 1000 rows
 stream
 |> Enum.take(1000)
 |> Enum.each(&process_row/1)
@@ -71,7 +66,6 @@ stream
 ### Parallel Loading for Performance
 
 ```elixir
-# Use all CPU cores for faster loading
 {:ok, dataset} = ElixirDatasets.load_dataset(
   {:hf, "multi-file-dataset"},
   num_proc: System.schedulers_online()
@@ -81,14 +75,12 @@ stream
 ### Upload Your Own Dataset
 
 ```elixir
-# Create a DataFrame
 df = Explorer.DataFrame.new(%{
   id: [1, 2, 3],
   text: ["Hello", "World", "!"],
   label: [0, 1, 0]
 })
 
-# Upload to Hugging Face
 {:ok, _response} = ElixirDatasets.upload_dataset(
   df,
   "username/my-dataset",
@@ -101,7 +93,6 @@ df = Explorer.DataFrame.new(%{
 ### Work with Local Files
 
 ```elixir
-# Load from local directory
 {:ok, dataset} = ElixirDatasets.load_dataset(
   {:local, "./data"},
   split: "train"
@@ -113,20 +104,16 @@ df = Explorer.DataFrame.new(%{
 ### Example 1: Text Classification with GLUE
 
 ```elixir
-# Load SST-2 sentiment classification dataset
 {:ok, train} = ElixirDatasets.load_dataset(
   {:hf, "glue"},
   name: "sst2",
   split: "train"
 )
 
-# Explore the data
 IO.inspect(Explorer.DataFrame.head(train, 5))
 
-# Filter positive examples
 positive = Explorer.DataFrame.filter(train, label == 1)
 
-# Get statistics
 stats = Explorer.DataFrame.summarise(train,
   total: count(label),
   positive: sum(label)
@@ -136,7 +123,6 @@ stats = Explorer.DataFrame.summarise(train,
 ### Example 2: Streaming Large Dataset
 
 ```elixir
-# Stream Wikipedia dataset
 {:ok, stream} = ElixirDatasets.load_dataset(
   {:hf, "wikipedia"},
   name: "20220301.en",
@@ -144,11 +130,9 @@ stats = Explorer.DataFrame.summarise(train,
   streaming: true
 )
 
-# Process in batches
 stream
 |> Stream.chunk_every(100)
 |> Stream.each(fn batch ->
-  # Process batch
   batch |> Enum.each(&analyze_text/1)
 end)
 |> Stream.run()
@@ -157,10 +141,8 @@ end)
 ### Example 3: Offline Mode
 
 ```elixir
-# First, download the dataset
 {:ok, _} = ElixirDatasets.load_dataset({:hf, "imdb"})
 
-# Later, work offline
 System.put_env("ELIXIR_DATASETS_OFFLINE", "1")
 
 {:ok, dataset} = ElixirDatasets.load_dataset(
@@ -180,16 +162,13 @@ System.put_env("ELIXIR_DATASETS_OFFLINE", "1")
 ### Cache Management
 
 ```elixir
-# Get cache directory
 cache_dir = ElixirDatasets.cache_dir()
 
-# Force redownload
 {:ok, dataset} = ElixirDatasets.load_dataset(
   {:hf, "dataset_name"},
   download_mode: :force_redownload
 )
 
-# Skip verification for faster loading
 {:ok, dataset} = ElixirDatasets.load_dataset(
   {:hf, "dataset_name"},
   verification_mode: :no_checks
@@ -239,17 +218,14 @@ ElixirDatasets focuses on core dataset loading and management features:
 ### Axon (Neural Networks)
 
 ```elixir
-# Load dataset
 {:ok, train} = ElixirDatasets.load_dataset({:hf, "mnist"})
 
-# Convert to Nx tensors for Axon
 train_tensors = train
 |> Explorer.DataFrame.to_rows()
 |> Enum.map(fn row ->
   {Nx.tensor(row["image"]), Nx.tensor(row["label"])}
 end)
 
-# Train with Axon
 model = Axon.input("input", shape: {nil, 784})
 |> Axon.dense(128, activation: :relu)
 |> Axon.dense(10, activation: :softmax)
@@ -258,14 +234,11 @@ model = Axon.input("input", shape: {nil, 784})
 ### Bumblebee (Transformers)
 
 ```elixir
-# Load text dataset
 {:ok, dataset} = ElixirDatasets.load_dataset({:hf, "imdb"}, split: "train")
 
-# Load Bumblebee model
 {:ok, model_info} = Bumblebee.load_model({:hf, "bert-base-uncased"})
 {:ok, tokenizer} = Bumblebee.load_tokenizer({:hf, "bert-base-uncased"})
 
-# Process dataset
 texts = Explorer.DataFrame.pull(dataset, "text")
 inputs = Bumblebee.apply_tokenizer(tokenizer, texts)
 ```
@@ -273,10 +246,8 @@ inputs = Bumblebee.apply_tokenizer(tokenizer, texts)
 ### Nx (Numerical Computing)
 
 ```elixir
-# Load numerical dataset
 {:ok, dataset} = ElixirDatasets.load_dataset({:hf, "california_housing"})
 
-# Convert to Nx tensors
 features = dataset
 |> Explorer.DataFrame.select(["feature1", "feature2", "feature3"])
 |> Explorer.DataFrame.to_columns()
@@ -300,13 +271,10 @@ Full documentation is available at [HexDocs](https://hexdocs.pm/elixir_datasets)
 ## 🧪 Testing
 
 ```bash
-# Run all tests
 mix test
 
-# Run with coverage
 mix coveralls
 
-# Run specific test file
 mix test test/elixir_datasets_test.exs
 ```