Method generate_article

tests/performance_benchmark.py:389–411

Generate a realistic article of approximately target_length characters.

(self, target_length: int)

        return dict(test_texts)
    
    def generate_article(self, target_length: int) -> str:
        """Generate a realistic article of approximately target_length characters."""
        paragraphs = [
            "In the rapidly evolving landscape of artificial intelligence and machine learning, tokenization has emerged as a fundamental preprocessing step that significantly impacts model performance and efficiency.",
            
            "Tokenization, the process of converting raw text into discrete tokens that can be processed by neural networks, involves numerous design decisions that affect both accuracy and computational efficiency.",
            
            "Modern tokenization approaches, including Byte Pair Encoding (BPE), WordPiece, and SentencePiece, each offer distinct advantages and trade-offs in terms of vocabulary size, compression efficiency, and handling of out-of-vocabulary terms.",
            
            "The choice of tokenization strategy becomes particularly critical when dealing with multilingual models, code generation tasks, and domains with specialized vocabulary such as scientific literature or technical documentation.",
            
            "Performance optimization in tokenization systems requires careful consideration of algorithmic complexity, memory usage patterns, and the specific characteristics of the target corpus.",
            
            "Recent advances in tokenization include learned tokenizers that adapt to specific domains, sub-word regularization techniques that improve model robustness, and efficient implementations that leverage parallel processing capabilities.",
            
            "Evaluation of tokenization systems typically involves metrics such as compression ratio, vocabulary coverage, tokenization speed, and downstream task performance across diverse benchmarks and real-world applications.",
        ]
        
        article = ""
        while len(article) < target_length:
            article += random.choice(paragraphs) + "\n\n"
        
        return article[:target_length]
    
    def benchmark_single_text(self, test_name: str, text: str) -> BenchmarkResult:
        """Benchmark both tokenizers on a single text."""