Opper

Data Normalization

Data processing and normalization tasks evaluate structured output from messy prose and different structures. This capability is essential for catalogue and product pipelines where data needs to be extracted from unstructured text and formatted consistently. Models must understand the desired output format and extract relevant information accurately.

Data Normalization – cerebras/qwen-3-32b Task | Opper AI

Input Tokens

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Output Tokens

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Duration

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Cost

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Context

Input

Read the form carefully and fill it in. You can only use one of the following words (Write them exactly as they are):['Jon Doe', 'Spaceship', 'Pineapple', 'Fourteen', 'Yellow', '42nd dimension', 'Barking', '18 horsepower', 'Sushi', 'Unicorn', 'Chair', 'Home renovation', 'Wi-Fi', '$3.50', 'Yelling', 'Saturn', 'Time travel', 'Bookcase', 'SEK 42,500', 'Lava lamp', 'Teleportation', 'Infinite sadness', 'Crayon', '36 months', '3.14159', 'Marshmallow', 'Rainbows', 'Software engineer', 'Green noise', 'Clockwise']

Expected output

{
  "placeholders": [
    "Jon Doe",
    "Home renovation",
    "SEK 42,500",
    "36 months",
    "Software engineer"
  ]
}

Model output

{
  "placeholders": [
    "Jon Doe",
    "Home renovation",
    "SEK 42,500",
    "36 months",
    "Software engineer"
  ]
}