LabelSets vs Cleanlab

Cleanlab finds errors. LQS signs the proof.

Cleanlab is the gold-standard OSS library for finding label errors. We use it. So should you. But it's a developer tool — not a procurement artifact. LabelSets LQS produces a cryptographically-signed, 19-dimension quality cert your auditors and risk team can cite directly. They solve different problems at different stages of the pipeline.

What each is for

Two stages. Two different artifacts.

Honest framing: if you're an ML engineer fixing your own dataset, Cleanlab. If you're a risk team filing model evidence, LQS. Most production teams need both.

Cleanlab

Find and fix label errors in your existing data

Confident learning identifies likely-mislabeled examples. Rerun annotation, re-train, repeat. The output is cleaner data.

Open-source library — pip install, MIT license
Targets ML engineers + data scientists
Excellent for label-error detection during dev
Per-record confidence scores
Active community + research lineage

LabelSets LQS

Rate the dataset and produce a signed audit-grade cert

19-dimension quality rating, oracle agreement, contamination check, signed with Ed25519. The output is a procurement-grade artifact.

Cryptographically-signed cert per dataset
Targets model-risk + compliance + procurement
SR 11-7, EU AI Act Art. 10, §1557, 21 CFR 11 framing
Composite score + 95% CI per dimension
Public revocation registry, offline verification

Use both. They're complementary, not competitive.

Run Cleanlab during development to clean your data. Run LQS at the end to prove it was checked, scored, and audit-grade. Cite both: "Cleaned with Cleanlab v2.7 · Rated by LabelSets LQS v3.1 (cert_hash: 3f1a…)". Your model package now has both the dev-tool and the audit-tool covered.

Capability comparison

What each does, honestly.

Capability

Cleanlab

LabelSets LQS

Find mislabeled examples in YOUR dataset

Best-in-class

Not the goal — use Cleanlab

Composite quality score per dataset

Per-record only

19-dim composite + per-dim CIs

Cryptographically signed output

Ed25519, public verifier

Oracle agreement (κ across model families)

7 oracles, 5 algorithm families

Benchmark contamination check (40+ public evals)

Procurement-grade artifact (file with payload + signature)

Maps to SR 11-7 / EU AI Act / §1557 paperwork

Public revocation registry

Open-source library

Today

Q4 2026 (in roadmap)

Marketplace of pre-rated datasets

147 published, growing

Score datasets you don't own (HF / Zenodo)

Live test drive on /