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 – mistral/mistral-small-eu 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

Reformat the following context text by adding a hyphen in between every word or simbol. Example: 'This is the text.' -> '-This-is-the-text-.-'

Expected output

{
  "instructions": "-Plug-in-the-vacuum-cleaner-and-switch-it-on-.-Adjust-the-height-setting-for-your-floor-type-.-Move-the-cleaner-slowly-back-and-forth-across-the-surface-.-Empty-the-dust-container-when-full-.-After-use-,-switch-off-,-unplug-,-and-store-in-a-dry-place-.-"
}

Model output

{
  "instructions": "-Plug-in-the-vacuum-cleaner-and-switch-it-on.-Adjust-the-height-setting-for-your-floor-type.-Move-the-cleaner-slowly-back-and-forth-across-the-surface.-Empty-the-dust-container-when-full.-After-use,-switch-off,-unplug,-and-store-in-a-dry-place.-"
}