hypothesis = ["The", "quick", "brown", "fox", "jumps", "over", "the", "dog"]

These PDFs contain the "Grille de prix" (price grid) for residential customers, outlining the cost per kWh and subscription fees. Categories: The documents typically break down costs into: Base: A flat rate regardless of the time of day.

Here is how you calculate the BLEU score using Python's nltk library:

Have you used BLEU to evaluate your PDF data pipeline? Share your scores and horror stories in the comments below