✨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://t.me/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://t.me/DataScienceT

#VisionLanguageModels #BPMN #InformationExtraction #AI #ComputerVision