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LinkedIn Announcement: The Definitive Guide to NER in 2026

A 300M-parameter model now matches the zero-shot NER accuracy of a 13B model.

While running 100x faster.

Two years ago, picking an NER approach meant choosing between speed and accuracy. That trade-off is gone — and it didn't even put up a fight.

I spent weeks benchmarking every major approach and built a companion repo with runnable code. Here's what I found:

⚡ A fine-tuned GLiNER hits 93.4% F1 at $0.10/hour on CPU — matching its Llama-70b teacher at $8/hour. 80x cheaper, slightly better accuracy.

🔄 The LLM-as-teacher pipeline has become the standard: $70 of LLM annotations replaces thousands in human labeling. The student surpasses the teacher.

📐 The new bi-encoder architecture loses only 5.2% throughput at 1,024 entity types. The cross-encoder loses 98.7%. That's a 130x advantage at scale.

🏗️ The 3-tier production pattern: encoders for the fast 90%, GLiNER 2 for multi-task extraction, LLMs for the hard 10% that requires reasoning. The same LLMs that handle the hardest cases generate the training data for everything else.

The circle of life, but for tensors.

Full guide covers papers, benchmarks, deployment optimization (ONNX, Rust), and structured extraction with Instructor and Outlines.

🔗 Links in the comments

AIEngineering #NER #NLP

Version 2: Self-Contained

A 300M-parameter model now matches the zero-shot NER accuracy of a 13B model.

While running 100x faster.

Two years ago, picking an NER approach meant choosing between speed and accuracy. That trade-off is gone — and it didn't even put up a fight.

I spent weeks benchmarking every major approach and built a companion repo with runnable code. Here's what I found:

⚡ A fine-tuned GLiNER hits 93.4% F1 at $0.10/hour on CPU — matching its Llama-70b teacher at $8/hour. 80x cheaper, slightly better accuracy.

🔄 The LLM-as-teacher pipeline has become the standard: $70 of LLM annotations replaces thousands in human labeling. The student surpasses the teacher.

📐 The new bi-encoder architecture loses only 5.2% throughput at 1,024 entity types. The cross-encoder loses 98.7%. That's a 130x advantage at scale.

🏗️ The 3-tier production pattern: encoders for the fast 90%, GLiNER 2 for multi-task extraction, LLMs for the hard 10% that requires reasoning. The same LLMs that handle the hardest cases generate the training data for everything else.

The circle of life, but for tensors.

📖 Full guide: https://www.yoursite.com/blog/2026/04/02/definitive-guide-ner-2026/ 💻 Companion repo: https://github.com/slavadubrov/ner-field-guide

AIEngineering #NER #NLP

First Comment (for Version 1)

📖 Full guide: https://www.yoursite.com/blog/2026/04/02/definitive-guide-ner-2026/ 💻 Companion repo with runnable demos: https://github.com/slavadubrov/ner-field-guide