Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
Vietnamese
Size:
1K - 10K
DOI:
License:
File size: 3,035 Bytes
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#!/usr/bin/env python3
"""Process raw banking data into three task-specific subsets."""
import json
import re
from pathlib import Path
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()
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)
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")
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"
with open(input_file, encoding="utf-8") as f:
for line in f:
result = extract_labels_and_text(line.strip())
if not result:
continue
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)
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")
def main():
"""Process raw data into task-specific subsets."""
print("🔄 Processing banking data...")
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__":
main()
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