# Multi-label NER

This is a Russian patent named entity recognition model based on the spaCy framework, focusing on identifying specific entity types in patent texts.

The CNER model is a named entity recognition model based on the DeBERTa-v3-base architecture, capable of jointly identifying and classifying concepts and named entities with fine-grained labels.

An English named entity recognition model trained on distilbert-base-uncased, specifically designed for identifying biomedical entities (107 entity categories), suitable for text corpora such as case reports.

A model fine-tuned on the recipe ingredient NER dataset based on xlm-roberta-base, used to identify various labels in recipe ingredients.

This model is used to identify drug-related entities in medical texts, including drug names, dosage, duration, frequency, and reason for medication.