Understanding GLiFormer’s Benchmarks and Schema-Driven Extraction
Overview
gliformer-large-v1 is a 575.6-million-parameter, Apache-2.0 model from knowledgatorfor schema-driven information extraction and text representation. It uses a shared DeBERTa encoder with task heads for named entity recognition, classification, joint relation extraction, structured records, and text embeddings. Its layout-aware architecture supports text and document-layout inputs, including PDF-oriented processing, but the published examples and evaluation results focus on English text rather than visual document understanding. You run it through the gliformer Python library or the GLiFormer source framework in Python 3.10 or newer. The most important consideration is its breadth: one checkpoint accepts labels and schemas at inference time, but its quality varies by task and dataset. Reported mean strict entity F1 is 50.91 across 26 NER datasets, mean macro-F1 is 75.03 across 13 classification datasets, joint relation extraction is weak on several benchmarks, and the strong 91.10 structuring score uses order-free, boundary-tolerant JSON evaluation rather than exact JSON matching....
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