Datasets:
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"""
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()
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