Add scripts/fetch_data.py

scripts/fetch_data.py

@@ -0,0 +1,115 @@
+"""
+Fetch data from HuggingFace dataset undertheseanlp/UTS_VLC
+- Get documents from law dataset
+- Segment sentences using underthesea
+- Get first 3000 sentences
+"""
+
+import re
+from os.path import dirname, join
+
+from datasets import load_dataset
+
+from underthesea import sent_tokenize, text_normalize
+
+
+def clean_text(text):
+    """Remove markdown formatting and clean text."""
+    # Normalize Unicode using underthesea
+    text = text_normalize(text)
+    # Remove markdown headers
+    text = re.sub(r'^#+\s+', '', text, flags=re.MULTILINE)
+    # Remove bold/italic markers
+    text = re.sub(r'\*+', '', text)
+    # Remove horizontal rules
+    text = re.sub(r'^-+$', '', text, flags=re.MULTILINE)
+    # Remove links
+    text = re.sub(r'\[([^\]]+)\]\([^)]+\)', r'\1', text)
+    # Remove multiple newlines
+    text = re.sub(r'\n{2,}', '\n', text)
+    # Remove leading/trailing whitespace per line
+    lines = [line.strip() for line in text.split('\n')]
+    text = '\n'.join(lines)
+    return text
+
+
+def is_valid_sentence(sent):
+    """Check if sentence is valid for UD annotation."""
+    sent = sent.strip()
+    # Remove trailing list markers like "1." or "a)"
+    sent = re.sub(r'\n\d+\.$', '', sent)
+    sent = re.sub(r'\n[a-z]\)$', '', sent)
+    sent = sent.strip()
+
+    if not sent:
+        return False, sent
+    # Too short
+    if len(sent) < 20:
+        return False, sent
+    # Too long
+    if len(sent) > 300:
+        return False, sent
+    # Skip headers (all caps, or starts with "Điều", "Chương", etc.)
+    if re.match(r'^(QUỐC HỘI|CỘNG HÒA|Độc lập|Phần thứ|Chương [IVX]+|MỤC \d+)', sent):
+        return False, sent
+    # Skip article titles
+    if re.match(r'^(Điều \d+|Khoản \d+|Mục \d+)', sent):
+        return False, sent
+    # Skip if mostly uppercase
+    if sum(1 for c in sent if c.isupper()) > len(sent) * 0.5:
+        return False, sent
+    # Skip if starts with special markers
+    if sent.startswith(('English:', 'Số hiệu:', 'Ngày hiệu lực:', '---', '|')):
+        return False, sent
+    # Must contain Vietnamese characters
+    if not re.search(r'[àáảãạăắằẳẵặâấầẩẫậèéẻẽẹêếềểễệìíỉĩịòóỏõọôốồổỗộơớờởỡợùúủũụưứừửữựỳýỷỹỵđ]', sent, re.IGNORECASE):
+        return False, sent
+    # Skip if ends with just a number (incomplete sentence)
+    if re.search(r'\n\d+$', sent):
+        return False, sent
+    return True, sent
+
+
+def fetch_and_process():
+    # Load dataset from HuggingFace
+    print("Loading dataset from HuggingFace...")
+    ds = load_dataset("undertheseanlp/UTS_VLC", split="2026")
+
+    # Segment sentences from all documents until we have 3000
+    print("Segmenting sentences...")
+    all_sentences = []
+    for idx, doc in enumerate(ds):
+        content = doc["content"]
+        content = clean_text(content)
+        sentences = sent_tokenize(content)
+        for sent in sentences:
+            sent = sent.strip()
+            is_valid, cleaned_sent = is_valid_sentence(sent)
+            if is_valid:
+                all_sentences.append(cleaned_sent)
+        if len(all_sentences) >= 3000:
+            print(f"Processed {idx + 1} documents")
+            break
+
+    # Get first 3000 sentences
+    sentences_3000 = all_sentences[:3000]
+    print(f"Total sentences collected: {len(sentences_3000)}")
+
+    # Save to output file
+    output_dir = dirname(dirname(__file__))
+    output_file = join(output_dir, "sentences.txt")
+
+    with open(output_file, "w", encoding="utf-8") as f:
+        for i, sent in enumerate(sentences_3000, 1):
+            f.write(f"{i}\t{sent}\n")
+
+    print(f"Saved to: {output_file}")
+
+    # Print sample
+    print("\nSample sentences:")
+    for i, sent in enumerate(sentences_3000[:5], 1):
+        print(f"  {i}. {sent[:80]}...")
+
+
+if __name__ == "__main__":
+    fetch_and_process()