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Why datasets?

Datasets are named, versioned collections of rows. Each row is just a Python dict. Use them to store test cases for your model and share them across experiments.

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Quick example

import zeroeval as ze

ze.init()  # 1️⃣ pick up API key from `zeroeval setup`

capitals = ze.Dataset(
    name="Capitals",
    description="Country → capital mapping",
    data=[
        {"input": "Colombia", "output": "Bogotá"},
        {"input": "Peru",     "output": "Lima"},
    ],
)

capitals.push()          # 🚀 creates version 1 in your workspace
capitals = ze.Dataset.pull("Capitals")  # later, fetch it back
print(capitals[0])       # {'input': 'Colombia', 'output': 'Bogotá'}

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Adding rows

capitals.add_rows([{"input": "USA", "output": "Washington, DC"}])
capitals.push()  # automatically becomes version 2

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Multimodal fields

Images, audio, video or plain URLs are all welcome: 
medical = ze.Dataset("Chest_X_Ray", [{"symptoms": "shortness of breath"}])

medical.add_image(0, "scan", "sample_images/patient01.jpg")
medical.add_audio(0, "dictation", "sample_audio/doctor_note.wav")
medical.add_media_url(0, "external_report",
                      "https://example.com/report.pdf", media_type="image")
medical.push()

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Inspecting & slicing

len(capitals)     # number of rows
capitals.columns  # ['input', 'output']
sample = capitals[:5]  # standard list slicing for quick smoke-tests