Bleu Pdf (2026)

from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction reference = [["The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"]] The "Hypothesis" (What your OCR/LLM extracted from the PDF) hypothesis = ["The", "quick", "brown", "fox", "jumps", "over", "the", "dog"] Apply smoothing to handle missing n-grams smoother = SmoothingFunction().method1 Calculate BLEU (using 1-gram to 4-grams) score = sentence_bleu(reference, hypothesis, smoothing_function=smoother) print(f"BLEU Score: {score:.2f}") # Output: ~0.82

"The closer a machine's generated text is to a professional human's text, the better it is." bleu pdf

Your OCR software extracted: "The quick brown fox jumps over the dog." from nltk

Have you used BLEU to evaluate your PDF data pipeline? Share your scores and horror stories in the comments below Need to calculate BLEU for your PDFs? Check out nltk for Python or evaluate by Hugging Face. While BLEU was originally designed for machine translation,

While BLEU was originally designed for machine translation, it has become the de facto standard for evaluating any text generated from PDFs against a "ground truth" (perfect human-generated text).