Add quality scoring script

scripts/add_quality_score.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python3
+"""
+Add quality score (1-10) to UVW 2026 dataset.
+
+Quality scoring based on Wikipedia article quality research:
+- Article length (comprehensiveness)
+- Number of sentences (content depth)
+- Sentence density (readability)
+
+References:
+- https://meta.wikimedia.org/wiki/Research:Prioritization_of_Wikipedia_Articles/Language-Agnostic_Quality
+- https://dl.acm.org/doi/10.1145/3625286
+"""
+
+import json
+import math
+from pathlib import Path
+from tqdm import tqdm
+
+
+INPUT_PATH = Path(__file__).parent.parent / "data" / "processed" / "uvw_2026.jsonl"
+OUTPUT_PATH = Path(__file__).parent.parent / "data" / "processed" / "uvw_2026_quality.jsonl"
+
+
+def calculate_quality_score(num_chars: int, num_sentences: int) -> int:
+    """
+    Calculate quality score from 1-10 based on article metrics.
+
+    Scoring criteria:
+    - Length score (40%): Based on character count thresholds
+    - Sentence score (30%): Based on number of sentences
+    - Density score (30%): Based on average sentence length (optimal ~80-150 chars)
+    """
+    # 1. Length score (1-10) - based on Wikipedia quality research
+    # Longer articles tend to be more comprehensive
+    if num_chars < 200:
+        length_score = 1
+    elif num_chars < 500:
+        length_score = 2
+    elif num_chars < 1000:
+        length_score = 3
+    elif num_chars < 2000:
+        length_score = 4
+    elif num_chars < 5000:
+        length_score = 5
+    elif num_chars < 10000:
+        length_score = 6
+    elif num_chars < 20000:
+        length_score = 7
+    elif num_chars < 50000:
+        length_score = 8
+    elif num_chars < 100000:
+        length_score = 9
+    else:
+        length_score = 10
+
+    # 2. Sentence score (1-10) - content depth
+    if num_sentences < 3:
+        sentence_score = 1
+    elif num_sentences < 5:
+        sentence_score = 2
+    elif num_sentences < 10:
+        sentence_score = 3
+    elif num_sentences < 20:
+        sentence_score = 4
+    elif num_sentences < 50:
+        sentence_score = 5
+    elif num_sentences < 100:
+        sentence_score = 6
+    elif num_sentences < 200:
+        sentence_score = 7
+    elif num_sentences < 500:
+        sentence_score = 8
+    elif num_sentences < 1000:
+        sentence_score = 9
+    else:
+        sentence_score = 10
+
+    # 3. Density score (1-10) - readability
+    # Optimal Vietnamese sentence length: ~80-150 chars
+    if num_sentences > 0:
+        avg_sentence_len = num_chars / num_sentences
+        if avg_sentence_len < 20:  # Too short - likely fragments
+            density_score = 3
+        elif avg_sentence_len < 40:
+            density_score = 5
+        elif avg_sentence_len < 80:
+            density_score = 8
+        elif avg_sentence_len < 150:  # Optimal range
+            density_score = 10
+        elif avg_sentence_len < 250:
+            density_score = 7
+        elif avg_sentence_len < 400:
+            density_score = 5
+        else:  # Too long - hard to read
+            density_score = 3
+    else:
+        density_score = 1
+
+    # Weighted average: length (40%), sentences (30%), density (30%)
+    final_score = (length_score * 0.4) + (sentence_score * 0.3) + (density_score * 0.3)
+
+    # Round to nearest integer, ensure 1-10 range
+    return max(1, min(10, round(final_score)))
+
+
+def main():
+    """Add quality scores to dataset."""
+    print("Adding quality scores to UVW 2026 dataset...")
+    print(f"Input: {INPUT_PATH}")
+    print(f"Output: {OUTPUT_PATH}")
+
+    # Count lines first
+    with open(INPUT_PATH, "r", encoding="utf-8") as f:
+        total = sum(1 for _ in f)
+
+    # Process and add quality scores
+    quality_distribution = {i: 0 for i in range(1, 11)}
+
+    with open(INPUT_PATH, "r", encoding="utf-8") as fin, \
+         open(OUTPUT_PATH, "w", encoding="utf-8") as fout:
+
+        for line in tqdm(fin, total=total, desc="Processing"):
+            article = json.loads(line)
+
+            # Calculate quality score
+            quality = calculate_quality_score(
+                article["num_chars"],
+                article["num_sentences"]
+            )
+            article["quality"] = quality
+            quality_distribution[quality] += 1
+
+            fout.write(json.dumps(article, ensure_ascii=False) + "\n")
+
+    # Print distribution
+    print("\nQuality score distribution:")
+    print("-" * 40)
+    for score in range(1, 11):
+        count = quality_distribution[score]
+        pct = count / total * 100
+        bar = "█" * int(pct / 2)
+        print(f"  {score:2d}: {count:8,} ({pct:5.1f}%) {bar}")
+
+    print(f"\nTotal articles: {total:,}")
+    print(f"Output saved to: {OUTPUT_PATH}")
+
+
+if __name__ == "__main__":
+    main()