Knowledgator Releases GLiFormer: A 575M-Parameter Encoder That Hits 91.10 F1 on Nested JSON Extraction Without Generating Tokens
MarkTechPost Asif Razzaq
Knowledgator Engineering released GLiFormer, a schema-conditioned encoder for multitask information extraction that produces nested JSON without generating output tokens token-by-token. GLiFormer Large v1 has 575.6M parameters and reports 91.10 F1 on a nested JSON benchmark (500 examples). Users can now run the Apache 2.0 checkpoints via pip on CPU or GPU while providing the extraction schema at inference time instead of relying on token-generation to format fields.
Why it matters
GLiFormer Large scores 91.10 F1 on nested JSON, near GPT-5.6-luna's 91.96, while grounding every value in source spans. The post Knowledgator Releases GLiFormer: A 575M-Parameter Encoder That Hits 91.10 F1 on Nested JSON Extraction Without Generating Tokens appeared first on MarkTechPost.
Related stories
Fastino Releases GLiNER2.5: A Boundary-Prediction Architecture That Removes Span Enumeration From Information Extraction
MarkTechPost · 3 weeks ago ·
34
Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM
Hugging Face · 1 year ago ·
29
How llm-d makes the most of the hardware you already have
IBM Research · 1 week ago ·
4