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40c7f57 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | #!/usr/bin/env python3
"""
Test script for paper type classifier
"""
import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from autoreview.paper_type_classifier import PaperTypeClassifier
os.environ['OPENAI_API_KEY'] = 'sk-Aors1iVXAbgd7sGwC9Ff781c75D14b74A71d4e63F1E46b68'
os.environ['OPENAI_BASEURL'] = 'https://api2.aigcbest.top/v1'
def test_paper_classifier():
"""Test the paper type classifier with sample content."""
# Sample paper contents for testing
test_cases = [
{
"content": "We propose a novel deep learning framework for natural language processing. Our approach introduces a new attention mechanism that significantly improves performance on benchmark datasets.",
"expected": "Technical Paper"
},
{
"content": "This survey provides a comprehensive overview of recent advances in computer vision. We review over 100 papers published in the last five years and present a taxonomy of current approaches.",
"expected": "Survey Paper"
},
{
"content": "We present a case study of deploying machine learning models in a real-world healthcare setting. Our application demonstrates the practical challenges and solutions for clinical decision support systems.",
"expected": "Application Paper"
},
{
"content": "We introduce a new dataset of 10,000 annotated medical images for disease classification. The dataset includes detailed annotations and is publicly available for research purposes.",
"expected": "Dataset Paper"
},
{
"content": "We release an open-source software tool for bioinformatics analysis. The tool provides a user-friendly interface and comprehensive documentation for researchers.",
"expected": "Tool Paper"
}
]
classifier = PaperTypeClassifier()
print("Testing Paper Type Classifier...")
print("=" * 50)
for i, test_case in enumerate(test_cases, 1):
print(f"\nTest Case {i}:")
print(f"Expected: {test_case['expected']}")
# Test keyword-based classification
keyword_result = classifier.classify_with_keywords(test_case['content'])
print(f"Keyword-based: {keyword_result}")
# Test LLM-based classification (if available)
try:
llm_result = classifier.classify_with_llm(test_case['content'])
print(f"LLM-based: {llm_result}")
except Exception as e:
print(f"LLM-based: Error - {e}")
print("-" * 30)
if __name__ == "__main__":
test_paper_classifier()
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