Docs · Data & extraction
Cookbook: Knowledge Graph Entity Alignment
Decide which of 450 candidate product pairs from two beer catalogues describe the same entity: one three-level Score per pair splits 40 merges, 50…
Open docs.typesafe.ai ↗How builders describe it
Decide which of 450 candidate product pairs from two beer catalogues describe the same entity: one three-level Score per pair splits 40 merges, 50 curator reviews, and 360 non-matches.
The decision Jev makes
Keep or drop a row, assign a label, or select one field value at a time.
Where it fits
Putting Jev next to the data: filtering rows with plain-English conditions, classifying records, labelling datasets and pulling structure out of text one small decision at a time. All 67 data & extraction projects →
Related projects
Data & extractionjevfilterPython library/PyPI: filter or classify text with plain-English rules via TypeSafe Jev (choose/check/rate).GitHub · damiensmith1
Data & extractionread-with-jevA Demo of using JEV to classify various attributes of a book, and present that to the reader to augment the reading experience.GitHub · ★ 3 · carlaiau
Data & extraction12 million source updates every six hoursA research pipeline that used to cost over a million dollars.X post · leiroData & extractionQualifying candidates with JevAn internal tool, and the limits of a rubric.X post · Prasad Pilla
Data & extractionScoring educational video segmentsJev scores segments of educational video.X post · Ziggyzonty
Data & extractiontab-jevA Jev-like model paired with a tabular foundation model.X post · jerrycxu