✨GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface

📝 Summary:
GLiNER2 is an efficient, unified transformer framework supporting named entity recognition, text classification, and structured data extraction. It offers competitive performance and improved accessibility over LLMs, all in a CPU-efficient, compact model.

🔹 Publication Date: Published on Jul 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.18546
• PDF: https://arxiv.org/pdf/2507.18546
• Github: https://github.com/fastino-ai/GLiNER2

🔹 Models citing this paper:
• https://huggingface.co/fastino/gliner2-base-v1
• https://huggingface.co/fastino/gliner2-large-v1

✨ Spaces citing this paper:
• https://huggingface.co/spaces/fastino/gliner2-official-demo

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#InformationExtraction #NER #NLP #DeepLearning #AI

✨Structured Extraction from Business Process Diagrams Using Vision-Language Models

📝 Summary:
This paper presents a method using Vision-Language Models to extract structured JSON from BPMN diagram images. It incorporates OCR for text enrichment, demonstrating improved model performance and enabling extraction when source files are unavailable.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22448
• PDF: https://arxiv.org/pdf/2511.22448
• Github: https://github.com/pritamdeka/BPMN-VLM

✨ Datasets citing this paper:
• https://huggingface.co/datasets/pritamdeka/BPMN-VLM

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#VisionLanguageModels #BPMN #InformationExtraction #AI #ComputerVision

✨ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images

📝 Summary:
ExStrucTiny is a new benchmark dataset for structured information extraction from document images. It addresses limitations of existing datasets by covering diverse document types and flexible schemas. This aims to improve generalist models for structured information extraction.

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12203
• PDF: https://arxiv.org/pdf/2602.12203

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For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#InformationExtraction #DocumentAI #MachineLearning #Dataset #ComputerVision