Bleu+pdf+work

smoothing = SmoothingFunction().method1 scores = [] for ref, cand in zip(ref_sents, cand_sents): score = sentence_bleu([ref.split()], cand.split(), smoothing_function=smoothing) scores.append(score)

def chunk_sentences(text): # Simple sentence splitter (improve with spaCy for production) return re.split(r'(?<=[.!?])\s+', text) bleu+pdf+work

This article explores why this combination matters, how to implement it, and best practices for making BLEU scores meaningful when working with PDF documents. What is BLEU Score? Developed by IBM in 2002, BLEU is an algorithm for evaluating the quality of machine-translated text against one or more human reference translations. It works by analyzing n-gram overlap (sequences of n words) between the candidate translation (machine output) and the reference (human gold standard). smoothing = SmoothingFunction()