Refactor all scripts - simple and elegant code

PREPROCESS.PY (145→98 lines):
- Extracted helper functions for clarity
- Removed verbose output and debugging
- Cleaner data structure handling
- Focused, readable main() function

STATS.PY (227→81 lines):
- Condensed from detailed analysis to essential stats
- Single function for subset statistics
- Clean, emoji-enhanced output format
- Removed redundant statistical calculations

CLEANUP:
- Removed old statistics_report.md file
- Fixed all ruff linting issues
- Added shebangs for direct execution
- Consistent code style across all scripts

All scripts now follow the same simple, elegant pattern.

preprocess.py

@@ -1,145 +1,98 @@
+#!/usr/bin/env python3
+"""Process raw banking data into three task-specific subsets."""
+
 import json
 import re
 from pathlib import Path
 
 
-def process_banking_data(input_file, output_dir):
-    """
-    Process banking data into three subsets:
-    1. Classification: text -> label (main aspect only)
-    2. Sentiment: text -> sentiment (overall sentiment)
-    3. Aspect-Sentiment: text -> aspect, sentiment pairs
-    """
+def extract_labels_and_text(line):
+    """Extract labels, sentiments, and clean text from a labeled line."""
+    pattern = r"__label__([A-Z_]+)#(positive|negative|neutral)"
+    matches = re.findall(pattern, line)
+    text = re.sub(r"__label__[A-Z_]+#(positive|negative|neutral)\s*", "", line).strip()
 
-    classification_data = []
-    sentiment_data = []
-    aspect_sentiment_data = []
+    if not matches or not text:
+        return None
+
+    aspects = [m[0] for m in matches]
+    sentiments = [m[1] for m in matches]
+    return aspects, sentiments, text
+
+
+def get_overall_sentiment(sentiments):
+    """Get overall sentiment from multiple sentiments."""
+    if len(set(sentiments)) == 1:
+        return sentiments[0]
+
+    # Use most common sentiment
+    counts = {}
+    for s in sentiments:
+        counts[s] = counts.get(s, 0) + 1
+    return max(counts, key=counts.get)
 
-    with open(input_file, encoding="utf-8") as f:
-        for line_num, line in enumerate(f, 1):
-            line = line.strip()
-            if not line:
-                continue
 
-            # Extract labels and sentiments
-            label_pattern = r"__label__([A-Z_]+)#(positive|negative|neutral)"
-            matches = re.findall(label_pattern, line)
+def save_subset(data, output_path):
+    """Save data to JSONL file."""
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(output_path, "w", encoding="utf-8") as f:
+        for item in data:
+            f.write(json.dumps(item, ensure_ascii=False) + "\n")
+
 
-            # Remove labels from text
-            text = re.sub(
-                r"__label__[A-Z_]+#(positive|negative|neutral)\s*", "", line
-            ).strip()
+def process_file(input_file, output_dir):
+    """Process a single input file into three subsets."""
+    data = {"classification": [], "sentiment": [], "aspect_sentiment": []}
+    split_name = "train" if "train" in str(input_file) else "test"
 
-            if not text or not matches:
-                print(f"Skipping line {line_num}: No valid data found")
+    with open(input_file, encoding="utf-8") as f:
+        for line in f:
+            result = extract_labels_and_text(line.strip())
+            if not result:
                 continue
 
-            # Extract aspects and sentiments
-            aspects = [m[0] for m in matches]
-            sentiments = [m[1] for m in matches]
-
-            # 1. Classification subset (first aspect as main label)
-            classification_data.append({"text": text, "label": aspects[0]})
-
-            # 2. Sentiment-only subset (overall sentiment)
-            # If multiple sentiments, use the first one or most frequent
-            overall_sentiment = sentiments[0]
-            if len(set(sentiments)) == 1:
-                overall_sentiment = sentiments[0]
-            else:
-                # Use most common sentiment
-                sentiment_counts = {}
-                for s in sentiments:
-                    sentiment_counts[s] = sentiment_counts.get(s, 0) + 1
-                overall_sentiment = max(sentiment_counts, key=sentiment_counts.get)
-
-            sentiment_data.append({"text": text, "sentiment": overall_sentiment})
-
-            # 3. Aspect-Sentiment subset
-            aspect_sentiment_pairs = []
-            for aspect, sentiment in zip(aspects, sentiments, strict=False):
-                aspect_sentiment_pairs.append(
-                    {"aspect": aspect, "sentiment": sentiment}
-                )
-
-            aspect_sentiment_data.append(
-                {"text": text, "aspects": aspect_sentiment_pairs}
-            )
-
-    # Save the three subsets
+            aspects, sentiments, text = result
+
+            # Classification subset
+            data["classification"].append({
+                "text": text,
+                "label": aspects[0]
+            })
+
+            # Sentiment subset
+            data["sentiment"].append({
+                "text": text,
+                "sentiment": get_overall_sentiment(sentiments)
+            })
+
+            # Aspect-sentiment subset
+            aspect_pairs = [
+                {"aspect": aspect, "sentiment": sentiment}
+                for aspect, sentiment in zip(aspects, sentiments, strict=False)
+            ]
+            data["aspect_sentiment"].append({
+                "text": text,
+                "aspects": aspect_pairs
+            })
+
+    # Save all subsets
     output_dir = Path(output_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
+    for subset_name, subset_data in data.items():
+        output_path = output_dir / subset_name / f"{split_name}.jsonl"
+        save_subset(subset_data, output_path)
+        print(f"✅ {subset_name}/{split_name}.jsonl: {len(subset_data)} examples")
 
-    # Determine if this is train or test based on input filename
-    split = "train" if "train" in str(input_file) else "test"
 
-    # Save classification subset
-    classification_file = output_dir / "classification" / f"{split}.jsonl"
-    classification_file.parent.mkdir(parents=True, exist_ok=True)
-    with open(classification_file, "w", encoding="utf-8") as f:
-        for item in classification_data:
-            f.write(json.dumps(item, ensure_ascii=False) + "\n")
-    print(
-        f"Saved {len(classification_data)} classification examples to {classification_file}"
-    )
-
-    # Save sentiment subset
-    sentiment_file = output_dir / "sentiment" / f"{split}.jsonl"
-    sentiment_file.parent.mkdir(parents=True, exist_ok=True)
-    with open(sentiment_file, "w", encoding="utf-8") as f:
-        for item in sentiment_data:
-            f.write(json.dumps(item, ensure_ascii=False) + "\n")
-    print(f"Saved {len(sentiment_data)} sentiment examples to {sentiment_file}")
+def main():
+    """Process raw data into task-specific subsets."""
+    print("🔄 Processing banking data...")
 
-    # Save aspect-sentiment subset
-    aspect_sentiment_file = output_dir / "aspect_sentiment" / f"{split}.jsonl"
-    aspect_sentiment_file.parent.mkdir(parents=True, exist_ok=True)
-    with open(aspect_sentiment_file, "w", encoding="utf-8") as f:
-        for item in aspect_sentiment_data:
-            f.write(json.dumps(item, ensure_ascii=False) + "\n")
-    print(
-        f"Saved {len(aspect_sentiment_data)} aspect-sentiment examples to {aspect_sentiment_file}"
-    )
-
-    # Print statistics
-    print("\n=== Statistics ===")
-    print(f"Total examples processed: {len(classification_data)}")
-
-    # Label distribution
-    label_counts = {}
-    for item in classification_data:
-        label = item["label"]
-        label_counts[label] = label_counts.get(label, 0) + 1
-
-    print("\nLabel distribution:")
-    for label, count in sorted(label_counts.items(), key=lambda x: x[1], reverse=True):
-        print(f"  {label}: {count}")
-
-    # Sentiment distribution
-    sentiment_counts = {}
-    for item in sentiment_data:
-        sentiment = item["sentiment"]
-        sentiment_counts[sentiment] = sentiment_counts.get(sentiment, 0) + 1
-
-    print("\nSentiment distribution:")
-    for sentiment, count in sorted(
-        sentiment_counts.items(), key=lambda x: x[1], reverse=True
-    ):
-        print(f"  {sentiment}: {count}")
-
-    # Multi-aspect examples
-    multi_aspect_count = sum(
-        1 for item in aspect_sentiment_data if len(item["aspects"]) > 1
-    )
-    print(f"\nExamples with multiple aspects: {multi_aspect_count}")
+    process_file("raw_data/train.txt", "data")
+    process_file("raw_data/test.txt", "data")
+
+    print("\n🎉 Processing complete!")
+    print("💡 Run 'python validate.py' to test the dataset")
 
 
 if __name__ == "__main__":
-    # Process train data
-    print("Processing train data...")
-    process_banking_data("raw_data/train.txt", "data")
-
-    # Process test data
-    print("\n" + "=" * 50)
-    print("Processing test data...")
-    process_banking_data("raw_data/test.txt", "data")
+    main()

statistics_report.md

@@ -1,262 +0,0 @@
-# UTS2017_Bank Dataset Statistics Report
-
-```
-
-============================================================
-DATASET SUMMARY
-============================================================
-
-Total Dataset Size:
-  Train: 1977 examples
-  Test: 494 examples
-  Total: 2471 examples
-  Train/Test Ratio: 4.00:1
-
-Available Subsets:
-  1. Classification: Text → Label (14 banking aspect categories)
-  2. Sentiment: Text → Sentiment (positive/negative/neutral)
-  3. Aspect-Sentiment: Text → Multiple (Aspect, Sentiment) pairs
-
-Data Format:
-  - All subsets use JSONL format
-  - UTF-8 encoding
-  - Vietnamese language text
-
-Recommended Use Cases:
-  - Classification: Banking domain text classification
-  - Sentiment: Customer feedback sentiment analysis
-  - Aspect-Sentiment: Fine-grained aspect-based sentiment analysis
-
-============================================================
-CLASSIFICATION SUBSET ANALYSIS
-============================================================
-
-TRAIN Split:
-  Total examples: 1977
-
-  Label Distribution:
-    CUSTOMER_SUPPORT    :  774 ( 39.2%)
-    TRADEMARK           :  699 ( 35.4%)
-    LOAN                :   74 (  3.7%)
-    INTERNET_BANKING    :   70 (  3.5%)
-    OTHER               :   69 (  3.5%)
-    CARD                :   66 (  3.3%)
-    INTEREST_RATE       :   60 (  3.0%)
-    PROMOTION           :   53 (  2.7%)
-    DISCOUNT            :   41 (  2.1%)
-    MONEY_TRANSFER      :   34 (  1.7%)
-    PAYMENT             :   15 (  0.8%)
-    SAVING              :   13 (  0.7%)
-    ACCOUNT             :    5 (  0.3%)
-    SECURITY            :    4 (  0.2%)
-
-  Text Statistics:
-    Words per text - Avg: 23.8, Min: 1, Max: 816, Median: 14.0
-    Chars per text - Avg: 106.3, Min: 3, Max: 3787, Median: 62.0
-
-TEST Split:
-  Total examples: 494
-
-  Label Distribution:
-    CUSTOMER_SUPPORT    :  338 ( 68.4%)
-    CARD                :   44 (  8.9%)
-    TRADEMARK           :   41 (  8.3%)
-    INTERNET_BANKING    :   32 (  6.5%)
-    OTHER               :   12 (  2.4%)
-    PROMOTION           :    9 (  1.8%)
-    INTEREST_RATE       :    6 (  1.2%)
-    LOAN                :    3 (  0.6%)
-    SAVING              :    3 (  0.6%)
-    MONEY_TRANSFER      :    2 (  0.4%)
-    DISCOUNT            :    2 (  0.4%)
-    PAYMENT             :    2 (  0.4%)
-
-  Text Statistics:
-    Words per text - Avg: 30.6, Min: 1, Max: 411, Median: 18.0
-    Chars per text - Avg: 134.8, Min: 4, Max: 1800, Median: 80.0
-
-============================================================
-SENTIMENT SUBSET ANALYSIS
-============================================================
-
-TRAIN Split:
-  Total examples: 1977
-
-  Sentiment Distribution:
-    positive  : 1211 ( 61.3%)
-    negative  :  743 ( 37.6%)
-    neutral   :   23 (  1.2%)
-
-  Text Statistics:
-    Words per text - Avg: 23.8, Min: 1, Max: 816, Median: 14.0
-
-TEST Split:
-  Total examples: 494
-
-  Sentiment Distribution:
-    negative  :  301 ( 60.9%)
-    positive  :  185 ( 37.4%)
-    neutral   :    8 (  1.6%)
-
-  Text Statistics:
-    Words per text - Avg: 30.6, Min: 1, Max: 411, Median: 18.0
-
-============================================================
-ASPECT-SENTIMENT SUBSET ANALYSIS
-============================================================
-
-TRAIN Split:
-  Total examples: 1977
-
-  Aspect Coverage:
-    Single aspect: 1948 (98.5%)
-    Multi-aspect: 29 (1.5%)
-    Max aspects per example: 4
-
-  Top 10 Aspect-Sentiment Pairs:
-    TRADEMARK            + positive:  652 ( 32.5%)
-    CUSTOMER_SUPPORT     + positive:  409 ( 20.4%)
-    CUSTOMER_SUPPORT     + negative:  370 ( 18.4%)
-    INTEREST_RATE        + negative:   63 (  3.1%)
-    LOAN                 + negative:   61 (  3.0%)
-    INTERNET_BANKING     + negative:   57 (  2.8%)
-    CARD                 + negative:   54 (  2.7%)
-    TRADEMARK            + negative:   47 (  2.3%)
-    OTHER                + negative:   35 (  1.7%)
-    PROMOTION            + positive:   33 (  1.6%)
-
-  Aspect Distribution with Sentiment Breakdown:
-
-    CUSTOMER_SUPPORT    :  784 ( 39.0%)
-      - positive: 409 ( 52.2%)
-      - negative: 370 ( 47.2%)
-      - neutral :   5 (  0.6%)
-
-    TRADEMARK           :  699 ( 34.8%)
-      - positive: 652 ( 93.3%)
-      - negative:  47 (  6.7%)
-
-    INTERNET_BANKING    :   79 (  3.9%)
-      - positive:  20 ( 25.3%)
-      - negative:  57 ( 72.2%)
-      - neutral :   2 (  2.5%)
-
-    LOAN                :   74 (  3.7%)
-      - positive:  13 ( 17.6%)
-      - negative:  61 ( 82.4%)
-
-    OTHER               :   69 (  3.4%)
-      - positive:  30 ( 43.5%)
-      - negative:  35 ( 50.7%)
-      - neutral :   4 (  5.8%)
-
-    INTEREST_RATE       :   68 (  3.4%)
-      - positive:   4 (  5.9%)
-      - negative:  63 ( 92.6%)
-      - neutral :   1 (  1.5%)
-
-    CARD                :   67 (  3.3%)
-      - positive:  12 ( 17.9%)
-      - negative:  54 ( 80.6%)
-      - neutral :   1 (  1.5%)
-
-    PROMOTION           :   53 (  2.6%)
-      - positive:  33 ( 62.3%)
-      - negative:  17 ( 32.1%)
-      - neutral :   3 (  5.7%)
-
-    DISCOUNT            :   42 (  2.1%)
-      - positive:  20 ( 47.6%)
-      - negative:  18 ( 42.9%)
-      - neutral :   4 (  9.5%)
-
-    MONEY_TRANSFER      :   36 (  1.8%)
-      - positive:   5 ( 13.9%)
-      - negative:  31 ( 86.1%)
-
-    PAYMENT             :   15 (  0.7%)
-      - positive:  11 ( 73.3%)
-      - negative:   4 ( 26.7%)
-
-    SAVING              :   13 (  0.6%)
-      - positive:   6 ( 46.2%)
-      - negative:   6 ( 46.2%)
-      - neutral :   1 (  7.7%)
-
-    ACCOUNT             :    5 (  0.2%)
-      - negative:   5 (100.0%)
-
-    SECURITY            :    5 (  0.2%)
-      - positive:   1 ( 20.0%)
-      - negative:   2 ( 40.0%)
-      - neutral :   2 ( 40.0%)
-
-TEST Split:
-  Total examples: 494
-
-  Aspect Coverage:
-    Single aspect: 492 (99.6%)
-    Multi-aspect: 2 (0.4%)
-    Max aspects per example: 3
-
-  Top 10 Aspect-Sentiment Pairs:
-    CUSTOMER_SUPPORT     + negative:  215 ( 43.3%)
-    CUSTOMER_SUPPORT     + positive:  122 ( 24.5%)
-    CARD                 + negative:   37 (  7.4%)
-    TRADEMARK            + positive:   35 (  7.0%)
-    INTERNET_BANKING     + negative:   27 (  5.4%)
-    PROMOTION            + positive:    7 (  1.4%)
-    TRADEMARK            + negative:    6 (  1.2%)
-    OTHER                + positive:    6 (  1.2%)
-    INTERNET_BANKING     + positive:    6 (  1.2%)
-    INTEREST_RATE        + negative:    6 (  1.2%)
-
-  Aspect Distribution with Sentiment Breakdown:
-
-    CUSTOMER_SUPPORT    :  339 ( 68.2%)
-      - positive: 122 ( 36.0%)
-      - negative: 215 ( 63.4%)
-      - neutral :   2 (  0.6%)
-
-    CARD                :   44 (  8.9%)
-      - positive:   5 ( 11.4%)
-      - negative:  37 ( 84.1%)
-      - neutral :   2 (  4.5%)
-
-    TRADEMARK           :   41 (  8.2%)
-      - positive:  35 ( 85.4%)
-      - negative:   6 ( 14.6%)
-
-    INTERNET_BANKING    :   33 (  6.6%)
-      - positive:   6 ( 18.2%)
-      - negative:  27 ( 81.8%)
-
-    OTHER               :   12 (  2.4%)
-      - positive:   6 ( 50.0%)
-      - negative:   3 ( 25.0%)
-      - neutral :   3 ( 25.0%)
-
-    PROMOTION           :    9 (  1.8%)
-      - positive:   7 ( 77.8%)
-      - negative:   1 ( 11.1%)
-      - neutral :   1 ( 11.1%)
-
-    INTEREST_RATE       :    6 (  1.2%)
-      - negative:   6 (100.0%)
-
-    LOAN                :    3 (  0.6%)
-      - negative:   3 (100.0%)
-
-    MONEY_TRANSFER      :    3 (  0.6%)
-      - negative:   3 (100.0%)
-
-    SAVING              :    3 (  0.6%)
-      - positive:   3 (100.0%)
-
-    DISCOUNT            :    2 (  0.4%)
-      - negative:   2 (100.0%)
-
-    PAYMENT             :    2 (  0.4%)
-      - positive:   1 ( 50.0%)
-      - negative:   1 ( 50.0%)
-```

stats.py

@@ -1,3 +1,6 @@
+#!/usr/bin/env python3
+"""Generate statistics for the UTS2017_Bank dataset."""
+
 import json
 import statistics as stats
 from collections import Counter
@@ -6,232 +9,73 @@ from pathlib import Path
 
 def load_jsonl(file_path):
     """Load JSONL file and return list of items."""
-    items = []
     with open(file_path, encoding="utf-8") as f:
-        for line in f:
-            items.append(json.loads(line.strip()))
-    return items
-
+        return [json.loads(line.strip()) for line in f]
 
-def calculate_text_statistics(items):
-    """Calculate statistics for text fields."""
-    text_lengths = [len(item["text"].split()) for item in items]
-    char_lengths = [len(item["text"]) for item in items]
 
+def text_stats(items):
+    """Calculate text length statistics."""
+    word_counts = [len(item["text"].split()) for item in items]
     return {
-        "avg_words": stats.mean(text_lengths),
-        "min_words": min(text_lengths),
-        "max_words": max(text_lengths),
-        "median_words": stats.median(text_lengths),
-        "avg_chars": stats.mean(char_lengths),
-        "min_chars": min(char_lengths),
-        "max_chars": max(char_lengths),
-        "median_chars": stats.median(char_lengths),
+        "avg": stats.mean(word_counts),
+        "min": min(word_counts),
+        "max": max(word_counts),
+        "median": stats.median(word_counts),
     }
 
 
-def analyze_classification_subset():
-    """Analyze classification subset statistics."""
-    print("\n" + "=" * 60)
-    print("CLASSIFICATION SUBSET ANALYSIS")
-    print("=" * 60)
+def print_subset_stats(subset_name, emoji):
+    """Print statistics for a dataset subset."""
+    print(f"\n{emoji} {subset_name.upper()} SUBSET")
+    print("-" * 40)
 
     for split in ["train", "test"]:
-        file_path = Path(f"data/classification/{split}.jsonl")
+        file_path = Path(f"data/{subset_name}/{split}.jsonl")
         items = load_jsonl(file_path)
 
-        print(f"\n{split.upper()} Split:")
-        print(f"  Total examples: {len(items)}")
-
-        # Label distribution
-        label_counter = Counter(item["label"] for item in items)
-        print("\n  Label Distribution:")
-        for label, count in label_counter.most_common():
-            percentage = (count / len(items)) * 100
-            print(f"    {label:20s}: {count:4d} ({percentage:5.1f}%)")
+        print(f"\n{split.capitalize()}: {len(items)} examples")
 
         # Text statistics
-        text_stats = calculate_text_statistics(items)
-        print("\n  Text Statistics:")
-        print(
-            f"    Words per text - Avg: {text_stats['avg_words']:.1f}, "
-            f"Min: {text_stats['min_words']}, Max: {text_stats['max_words']}, "
-            f"Median: {text_stats['median_words']:.1f}"
-        )
-        print(
-            f"    Chars per text - Avg: {text_stats['avg_chars']:.1f}, "
-            f"Min: {text_stats['min_chars']}, Max: {text_stats['max_chars']}, "
-            f"Median: {text_stats['median_chars']:.1f}"
-        )
-
-
-def analyze_sentiment_subset():
-    """Analyze sentiment subset statistics."""
-    print("\n" + "=" * 60)
-    print("SENTIMENT SUBSET ANALYSIS")
-    print("=" * 60)
+        text_data = text_stats(items)
+        print(f"  Words: avg={text_data['avg']:.1f}, range={text_data['min']}-{text_data['max']}")
 
-    for split in ["train", "test"]:
-        file_path = Path(f"data/sentiment/{split}.jsonl")
-        items = load_jsonl(file_path)
+        # Subset-specific stats
+        if subset_name == "classification":
+            labels = Counter(item["label"] for item in items)
+            print(f"  Top labels: {', '.join(f'{k}({v})' for k, v in labels.most_common(3))}")
 
-        print(f"\n{split.upper()} Split:")
-        print(f"  Total examples: {len(items)}")
+        elif subset_name == "sentiment":
+            sentiments = Counter(item["sentiment"] for item in items)
+            print(f"  Sentiments: {', '.join(f'{k}({v})' for k, v in sentiments.most_common())}")
 
-        # Sentiment distribution
-        sentiment_counter = Counter(item["sentiment"] for item in items)
-        print("\n  Sentiment Distribution:")
-        for sentiment, count in sentiment_counter.most_common():
-            percentage = (count / len(items)) * 100
-            print(f"    {sentiment:10s}: {count:4d} ({percentage:5.1f}%)")
+        elif subset_name == "aspect_sentiment":
+            multi_aspect = sum(1 for item in items if len(item["aspects"]) > 1)
+            print(f"  Multi-aspect: {multi_aspect}/{len(items)} examples")
 
-        # Text statistics
-        text_stats = calculate_text_statistics(items)
-        print("\n  Text Statistics:")
-        print(
-            f"    Words per text - Avg: {text_stats['avg_words']:.1f}, "
-            f"Min: {text_stats['min_words']}, Max: {text_stats['max_words']}, "
-            f"Median: {text_stats['median_words']:.1f}"
-        )
 
+def main():
+    """Generate and display dataset statistics."""
+    print("📊 UTS2017_Bank Dataset Statistics")
+    print("=" * 50)
 
-def analyze_aspect_sentiment_subset():
-    """Analyze aspect-sentiment subset statistics."""
-    print("\n" + "=" * 60)
-    print("ASPECT-SENTIMENT SUBSET ANALYSIS")
-    print("=" * 60)
+    # Overall stats
+    train_items = load_jsonl("data/classification/train.jsonl")
+    test_items = load_jsonl("data/classification/test.jsonl")
+    total = len(train_items) + len(test_items)
 
-    for split in ["train", "test"]:
-        file_path = Path(f"data/aspect_sentiment/{split}.jsonl")
-        items = load_jsonl(file_path)
+    print(f"\n📈 OVERALL: {total} examples ({len(train_items)} train, {len(test_items)} test)")
 
-        print(f"\n{split.upper()} Split:")
-        print(f"  Total examples: {len(items)}")
-
-        # Multi-aspect analysis
-        single_aspect = sum(1 for item in items if len(item["aspects"]) == 1)
-        multi_aspect = sum(1 for item in items if len(item["aspects"]) > 1)
-        max_aspects = max(len(item["aspects"]) for item in items)
-
-        print("\n  Aspect Coverage:")
-        print(
-            f"    Single aspect: {single_aspect} ({(single_aspect / len(items)) * 100:.1f}%)"
-        )
-        print(
-            f"    Multi-aspect: {multi_aspect} ({(multi_aspect / len(items)) * 100:.1f}%)"
-        )
-        print(f"    Max aspects per example: {max_aspects}")
-
-        # Aspect-sentiment pair distribution
-        aspect_sentiment_pairs = []
-        for item in items:
-            for asp in item["aspects"]:
-                aspect_sentiment_pairs.append(f"{asp['aspect']}#{asp['sentiment']}")
-
-        pair_counter = Counter(aspect_sentiment_pairs)
-        print("\n  Top 10 Aspect-Sentiment Pairs:")
-        for pair, count in pair_counter.most_common(10):
-            aspect, sentiment = pair.split("#")
-            percentage = (count / len(aspect_sentiment_pairs)) * 100
-            print(f"    {aspect:20s} + {sentiment:8s}: {count:4d} ({percentage:5.1f}%)")
-
-        # Overall aspect distribution
-        aspect_counter = Counter()
-        sentiment_by_aspect = {}
-
-        for item in items:
-            for asp in item["aspects"]:
-                aspect = asp["aspect"]
-                sentiment = asp["sentiment"]
-                aspect_counter[aspect] += 1
-
-                if aspect not in sentiment_by_aspect:
-                    sentiment_by_aspect[aspect] = Counter()
-                sentiment_by_aspect[aspect][sentiment] += 1
-
-        print("\n  Aspect Distribution with Sentiment Breakdown:")
-        for aspect, count in aspect_counter.most_common():
-            percentage = (count / sum(aspect_counter.values())) * 100
-            print(f"\n    {aspect:20s}: {count:4d} ({percentage:5.1f}%)")
-
-            # Sentiment breakdown for this aspect
-            sentiments = sentiment_by_aspect[aspect]
-            total_aspect = sum(sentiments.values())
-            for sentiment in ["positive", "negative", "neutral"]:
-                if sentiment in sentiments:
-                    sent_count = sentiments[sentiment]
-                    sent_pct = (sent_count / total_aspect) * 100
-                    print(f"      - {sentiment:8s}: {sent_count:3d} ({sent_pct:5.1f}%)")
-
-
-def generate_summary_statistics():
-    """Generate overall summary statistics."""
-    print("\n" + "=" * 60)
-    print("DATASET SUMMARY")
-    print("=" * 60)
-
-    total_train = len(load_jsonl("data/classification/train.jsonl"))
-    total_test = len(load_jsonl("data/classification/test.jsonl"))
-
-    print("\nTotal Dataset Size:")
-    print(f"  Train: {total_train} examples")
-    print(f"  Test: {total_test} examples")
-    print(f"  Total: {total_train + total_test} examples")
-    print(f"  Train/Test Ratio: {total_train / total_test:.2f}:1")
-
-    # Available subsets
-    print("\nAvailable Subsets:")
-    print("  1. Classification: Text → Label (14 banking aspect categories)")
-    print("  2. Sentiment: Text → Sentiment (positive/negative/neutral)")
-    print("  3. Aspect-Sentiment: Text → Multiple (Aspect, Sentiment) pairs")
-
-    # Data format
-    print("\nData Format:")
-    print("  - All subsets use JSONL format")
-    print("  - UTF-8 encoding")
-    print("  - Vietnamese language text")
-
-    # Use cases
-    print("\nRecommended Use Cases:")
-    print("  - Classification: Banking domain text classification")
-    print("  - Sentiment: Customer feedback sentiment analysis")
-    print("  - Aspect-Sentiment: Fine-grained aspect-based sentiment analysis")
-
-
-def save_statistics_report():
-    """Save statistics to a markdown file."""
-    import sys
-    from io import StringIO
-
-    # Capture output
-    old_stdout = sys.stdout
-    sys.stdout = buffer = StringIO()
-
-    # Run all analyses
-    generate_summary_statistics()
-    analyze_classification_subset()
-    analyze_sentiment_subset()
-    analyze_aspect_sentiment_subset()
-
-    # Get output
-    output = buffer.getvalue()
-    sys.stdout = old_stdout
-
-    # Save to file
-    with open("statistics_report.md", "w", encoding="utf-8") as f:
-        f.write("# UTS2017_Bank Dataset Statistics Report\n\n")
-        f.write("```\n")
-        f.write(output)
-        f.write("```\n")
-
-    print("Statistics report saved to statistics_report.md")
+    # Subset statistics
+    print_subset_stats("classification", "🏷️")
+    print_subset_stats("sentiment", "😊")
+    print_subset_stats("aspect_sentiment", "🎯")
 
+    # Available configurations
+    print("\n💡 USAGE:")
+    print("  load_dataset('undertheseanlp/UTS2017_Bank', 'classification')")
+    print("  load_dataset('undertheseanlp/UTS2017_Bank', 'sentiment')")
+    print("  load_dataset('undertheseanlp/UTS2017_Bank', 'aspect_sentiment')")
 
-if __name__ == "__main__":
-    generate_summary_statistics()
-    analyze_classification_subset()
-    analyze_sentiment_subset()
-    analyze_aspect_sentiment_subset()
 
-    print("\n" + "=" * 60)
-    save_statistics_report()
+if __name__ == "__main__":
+    main()