NLP Named Entity Recognition — Custom NER Model for Your Domain

I will build a custom Named Entity Recognition model for your domain — training spaCy or a fine-tuned transformer to recognise the specific entity types relevant to your industry (product names, medical terms, legal entities, technical identifiers, or your custom categories) with higher precision than generic NER models trained on general text corpora. Generic NER models trained on news and Wikipedia corpora perform poorly on domain-specific text — a medical NER model misses drug names formatted differently from its training data, a legal NER model misses jurisdiction-specific entity patterns, and a financial NER model misses internal product naming conventions that your domain documents use throughout.

The NER build covers annotation schema design for your entity types, training data annotation guidance, spaCy or transformer fine-tuning, entity-level precision/recall evaluation, error analysis on false positives and negatives, and a deployment-ready NER pipeline.