AI EVALUATION, IN CONTEXT.BY EQUALITY MACHINE

Claire Radar

TRL-Bench

TRL-Bench evaluates tabular encoders at the representation level by exporting row-, column-, or table-level embeddings through each encoder's supported wrapper and probing them with shared lightweight heads across three suites: TRL-CTbench (column/table), TRL-Rbench (row), and TRL-DLTE (compositional Data-Lake Table Enrichment) covering 16 tasks and 20 models.

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: 2026-10-13

GitHub stars
10
Hugging Face paper upvotes
54
Star change · 30 days before observation
0

logo-cuhksz/trl-bench · View source snapshot

Dated attention observations, not live counts or benchmark scores.

Evaluation context and evidence

Source
Claire Radar
Publisher
Not recorded
Benchmark release
2026-06-08
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.