I'm with Daron on this one. Option D sounds like the kind of advice you'd get from someone who's never actually used an ML Extractor before. Definitely not the way to go.
Wow, Option D really takes the cake! Bigger models performing better? That's like saying the more ingredients you throw in a cake, the tastier it'll be. Nonsense!
I'm going to have to go with Option B as well. Bigger isn't always better when it comes to ML models. It's about finding the right tool for the job, not the flashiest one.
I agree with Francine. The quality and diversity of the training data used to develop the ML Extractor is a key factor in determining its performance. Popularity and cost shouldn't be the primary drivers here.
Option B seems the most logical choice. Considering the document types, language, and data quality is crucial for ensuring accurate and reliable extraction results. The ML Extractor needs to be tailored to the specific use case, not just the most popular or cheapest one.
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