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"""
Statistics for UD Vietnamese Dataset (UDD-v0.1)
"""
from collections import Counter
from os.path import dirname, join
def parse_conllu(filepath):
"""Parse CoNLL-U file and return sentences."""
sentences = []
current_sentence = {
'tokens': [],
'upos': [],
'deprel': [],
'head': [],
'metadata': {}
}
with open(filepath, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if not line:
if current_sentence['tokens']:
sentences.append(current_sentence)
current_sentence = {
'tokens': [],
'upos': [],
'deprel': [],
'head': [],
'metadata': {}
}
elif line.startswith('#'):
# Metadata
if '=' in line:
key, value = line[2:].split('=', 1)
current_sentence['metadata'][key.strip()] = value.strip()
else:
parts = line.split('\t')
if len(parts) >= 10:
# Skip multi-word tokens (e.g., 1-2)
if '-' in parts[0] or '.' in parts[0]:
continue
current_sentence['tokens'].append(parts[1])
current_sentence['upos'].append(parts[3])
current_sentence['head'].append(parts[6])
current_sentence['deprel'].append(parts[7])
# Add last sentence if exists
if current_sentence['tokens']:
sentences.append(current_sentence)
return sentences
def compute_statistics(sentences):
"""Compute statistics from parsed sentences."""
stats = {}
# Basic counts
stats['num_sentences'] = len(sentences)
stats['num_tokens'] = sum(len(s['tokens']) for s in sentences)
# Sentence length statistics
sent_lengths = [len(s['tokens']) for s in sentences]
stats['avg_sent_length'] = sum(sent_lengths) / len(sent_lengths) if sent_lengths else 0
stats['min_sent_length'] = min(sent_lengths) if sent_lengths else 0
stats['max_sent_length'] = max(sent_lengths) if sent_lengths else 0
# UPOS distribution
all_upos = []
for s in sentences:
all_upos.extend(s['upos'])
stats['upos_counts'] = Counter(all_upos)
# DEPREL distribution
all_deprel = []
for s in sentences:
all_deprel.extend(s['deprel'])
stats['deprel_counts'] = Counter(all_deprel)
# Tree depth statistics
depths = []
for s in sentences:
max_depth = compute_tree_depth(s['head'])
depths.append(max_depth)
stats['avg_tree_depth'] = sum(depths) / len(depths) if depths else 0
stats['max_tree_depth'] = max(depths) if depths else 0
# Root relation counts
root_upos = []
for s in sentences:
for i, (upos, deprel) in enumerate(zip(s['upos'], s['deprel'])):
if deprel == 'root':
root_upos.append(upos)
stats['root_upos_counts'] = Counter(root_upos)
return stats
def compute_tree_depth(heads):
"""Compute maximum depth of dependency tree."""
n = len(heads)
if n == 0:
return 0
depths = [0] * n
def get_depth(idx):
if depths[idx] > 0:
return depths[idx]
head = int(heads[idx])
if head == 0:
depths[idx] = 1
else:
depths[idx] = get_depth(head - 1) + 1
return depths[idx]
for i in range(n):
try:
get_depth(i)
except (RecursionError, IndexError):
depths[i] = 1
return max(depths) if depths else 0
def print_statistics(stats):
"""Print statistics in a nice format."""
print("=" * 60)
print("UD Vietnamese Dataset (UDD-v0.1) Statistics")
print("=" * 60)
print("\n## Basic Statistics")
print(f" Sentences: {stats['num_sentences']:,}")
print(f" Tokens: {stats['num_tokens']:,}")
print(f" Avg sent length: {stats['avg_sent_length']:.2f}")
print(f" Min sent length: {stats['min_sent_length']}")
print(f" Max sent length: {stats['max_sent_length']}")
print(f" Avg tree depth: {stats['avg_tree_depth']:.2f}")
print(f" Max tree depth: {stats['max_tree_depth']}")
print("\n## UPOS Distribution")
print(f" {'Tag':<10} {'Count':>8} {'Percent':>8}")
print(" " + "-" * 28)
total_tokens = stats['num_tokens']
for tag, count in stats['upos_counts'].most_common():
pct = count / total_tokens * 100
print(f" {tag:<10} {count:>8,} {pct:>7.2f}%")
print("\n## DEPREL Distribution")
print(f" {'Relation':<20} {'Count':>8} {'Percent':>8}")
print(" " + "-" * 38)
for rel, count in stats['deprel_counts'].most_common():
pct = count / total_tokens * 100
print(f" {rel:<20} {count:>8,} {pct:>7.2f}%")
print("\n## Root UPOS Distribution")
print(f" {'UPOS':<10} {'Count':>8} {'Percent':>8}")
print(" " + "-" * 28)
total_roots = sum(stats['root_upos_counts'].values())
for tag, count in stats['root_upos_counts'].most_common():
pct = count / total_roots * 100
print(f" {tag:<10} {count:>8,} {pct:>7.2f}%")
print("\n" + "=" * 60)
def main():
# Find train.conllu file
base_dir = dirname(dirname(__file__))
conllu_file = join(base_dir, 'train.conllu')
print(f"Reading: {conllu_file}")
sentences = parse_conllu(conllu_file)
stats = compute_statistics(sentences)
print_statistics(stats)
if __name__ == "__main__":
main()
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