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How to label DataFrame rows with closed-set Jev answers with Awesome Jev

Ask Jev a closed-set question for each row, cache the answers, and join them back to the table.

Who it is forA data analyst with a spreadsheet or DataFrame of tickets, records, or messages that need consistent labels.

The problemLabeling rows by hand takes time, and rule-based labels miss meaning in free text.

The outcomeA table with a new label column, plus cached answers that can be reused on later runs.

What you need first

  • Access to Jev through the TypeSafe early-access entry point at typesafe.ai
  • A small labeled sample of your own data to check Jev answers before relying on them

Steps

  1. Open the hunch repository at github.com/steven-shoemaker/hunch and follow its installation section.
  2. Define the closed set of labels your rows should receive.
  3. Run the classify or score verb over each row of your DataFrame.
  4. Join the cached answers back to the table and keep the thresholds and actions in your own code.
  5. Review a random sample of about twenty labeled rows by hand.

Prompt for your agent

Using the steven-shoemaker/hunch Python library, classify each row of <dataset path> into the closed-set labels <label list>. Return the labeled table and a count per label. Verify twenty random rows by hand before reporting results.

Caveat. Jev returns schema-valid answers, which are not guaranteed correct, so calibrate on a hand-labeled sample first.

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