# Multi-entity recognition

A high-precision named entity recognition model specifically designed for Arabic text processing, supporting detection of multiple entity types.

The ONNX version of NuNerZero, a zero-shot named entity recognition model optimized for fast inference using ONNX Runtime.

XLM-RoBERTa-large fine-tuned Uzbek named entity recognition model supporting 21 entity types

A Russian text named entity recognition (NER) model fine-tuned based on bert-base-multilingual-cased, using BIOLU annotation format, capable of recognizing various entity types such as person names, locations, and organizations.

A named entity recognition model specifically designed for Uzbek text, built on the XLM-RoBERTa large architecture, supporting multiple entity category recognition.

A keyword extraction model fine-tuned based on BART-large, excelling at extracting technical terms, tool names, and platform-related vocabulary from text

A Kazakh named entity recognition model based on XLM-RoBERTa-large architecture, trained on the KazNERD dataset, supporting multiple entity type recognition

A model based on the ELECTRA architecture, specifically designed to extract legally relevant entities from civil judgments issued by the Italian Supreme Court.

Named Entity Recognition model for Catalan, fine-tuned based on BERTa