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Model evaluation workbench
Compare selected model outputs on your own approved examples before choosing a workflow.
AI & Tools
Build more with Hugging Face.
Access thousands of ML models for NLP, vision, audio, and more via Inference API. Shape those capabilities into a system that fits your team.
Build concepts, not preinstalled apps. Provider plans, permissions, and API availability apply.
The possibilities
What could you build?
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Content classification queue
Use a suitable model to propose labels, then let reviewers accept or correct them.
Start with the model evaluation workbench.
Keep the first version small enough to test with the people who will use it.
- 01Hugging Face
- 02Model evaluation workbench
- 03Reviewed next action
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Choose the first job
Use the first idea as your brief: Compare selected model outputs on your own approved examples before choosing a workflow. Name the person responsible and the information they need to act.
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Confirm access before connecting
Verify each model's task support, license, and hosting requirements. Evaluate with representative data before rollout.
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Shape the app with Scout
Use the starter prompt below. Ask Scout to propose the records, source field mapping, views, and access rules. Preview sample data before connecting Hugging Face; keep source IDs so records stay traceable.
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Pilot, then expand
Test with a small approved dataset and a least-privileged user. Check missing records, revoked access, repeated events, and failed requests. Enable live writes or messages only after the responsible owner approves the workflow.
Start with this prompt
Paste into Scout, then adapt it to your team. Do not paste credentials or private customer data.
Create this app with Hugging Face: Model evaluation workbench. Compare selected model outputs on your own approved examples before choosing a workflow. Start with sample data and a small, approved set of records. Before connecting live data: Verify each model's task support, license, and hosting requirements. Evaluate with representative data before rollout. Propose the data model, access rules, and source-to-app field mapping before implementation. Add source links, a last-updated time, and clear empty and connection-error states. Keep credentials out of client code. Require human approval for external messages, source changes, or money movement. Test duplicate events, revoked access, and recovery before publishing.
Make it work your way.
Start with Scout, or map the first build with our team.