# schema-driven-extraction

Published articles for schema-driven-extraction.

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## Understanding GLiFormer's Benchmarks and Schema-Driven Extraction

DevFeed: [Understanding GLiFormer's Benchmarks and Schema-Driven Extraction](<https://devfeed.tech/articles/understanding-gliformer-s-benchmarks-and-schema-driven-extraction-58370.md>)

Original publisher: [Read original article](<https://hackernoon.com/understanding-gliformers-benchmarks-and-schema-driven-extraction?source=rss>)

Author: aimodels44

Published: 2026-09-22T20:50:51Z

Content type: article

Language: en

Sources: [HackerNoon](<https://devfeed.tech/sources/hackernoon.md>)

Topics: [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [ner](<https://devfeed.tech/topics/ner.md>), [text-classification](<https://devfeed.tech/topics/text-classification.md>), [Structured-data](<https://devfeed.tech/topics/structured-data.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>)

Tags: [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [classification](<https://devfeed.tech/tags/classification.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [gliformer](<https://devfeed.tech/tags/gliformer.md>), [knowledgator](<https://devfeed.tech/tags/knowledgator.md>), [model](<https://devfeed.tech/tags/model.md>), [named-entity-recognition](<https://devfeed.tech/tags/named-entity-recognition.md>), [pydantic-validation](<https://devfeed.tech/tags/pydantic-validation.md>), [python](<https://devfeed.tech/tags/python.md>), [schema-driven-extraction](<https://devfeed.tech/tags/schema-driven-extraction.md>), [structured](<https://devfeed.tech/tags/structured.md>), [structured-record-extraction](<https://devfeed.tech/tags/structured-record-extraction.md>)

### AI overview

An overview of GLiFormer Large v1, a 575.6-million-parameter Apache-2.0 model for schema-driven information extraction and text representation. The article examines its entity extraction, classification, relation extraction, structured-record, and embedding capabilities, with Python examples and benchmark results showing substantial variation by task and dataset.

### Source excerpt

Explore GLiFormer Large v1's extraction tasks, reported benchmarks, Python examples, and limitations across entities, relations, and structured records.