AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

Claire Radar

ExtractBench

ExtractBench evaluates schema-guided extraction from enterprise documents. Given a document and a user-defined JSON schema, systems must return schema-valid JSON with correct values, include every record of repeated structures, mark missing fields as null, and provide source evidence. The benchmark includes 4,869 pages across 370 documents, 8 business domains, and 67 document types, with tags for challenge, perception, table structure, length, and domain. Scoring uses unified value F1 for value accuracy and word- and page-level F1 for grounding.

Evidence available for this benchmark

Numeric results have not been collected for this source record. Use the paper, code or dataset below to check the task and experiments; this does not imply that the original work contains no results.

Start with these resources

Cited documents and dates (1)

Catalog collected on 2026-10-14; resources and fields reflect that saved source.

Recorded attention

Observed on: 2026-10-11

GitHub stars
107
Hugging Face paper upvotes
25
Hugging Face downloads · 30 days before observation
15,709
Star change · 30 days before observation
+21

run-llama/extractbench · View source snapshot

Dated attention observations, not live counts or benchmark scores.

Evaluation context and evidence

Source
Claire Radar
Publisher
Not recorded
Benchmark release
2026-07-31
Collected
2026-10-14
Modality
Not recorded

These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.

Reported results 0

This source record has no numeric results in our collection. That does not mean a score of zero or that no public results exist. Consult the original resources above.