Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for teams building invoice, form, and document-intelligence pipelines, but the code license and the model and commercial terms are not the same story and need to be read carefully before rollout.
Who should use it
Who should skip it
Hold off on datalab-to/lift for mission-critical workflows without a containment strategy, explicit approvals, and a hands-on security review.
About this signal
datalab-to/lift is tracked by RepoRadar as a tool in the Document Intelligence section. It was first seen on 2026-06-26 and last updated on 2026-06-26. The current verdict is 'worth watch' with a Gold tier and hard setup difficulty. The standout signals for datalab-to/lift are maturity (8.5) and workflow potential (8.5), while setup ease (4.2) trails — that balance shapes where it fits best. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned datalab-to/lift a composite score of 8.1 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 540.0 and never affects the composite score or tier. The risk label of 'medium' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
The repository separates permissive Apache-2.0 code from a modified OpenRAIL-M model license, and the README explicitly says commercial self-hosting requires a separate license; The strongest benchmark numbers in the README come from Datalab's managed platform rather than the open local weights, so teams should validate the local path on their own documents before assuming parity; First-run local inference expects either a vLLM server or a Hugging Face stack with the right GPU and runtime setup, which makes deployment heavier than the short CLI examples imply.