# Multi-category NER

Named entity recognition model fine-tuned on Indonesian NER tasks based on NusaBert-v1.3

A scientific literature entity recognition model based on SciBERT, supporting 6 predefined scientific entity types

GLiNER is a bidirectional Transformer-based named entity recognition model capable of identifying any entity type, offering greater flexibility than traditional NER models and higher efficiency than large language models.

A named entity recognition model fine-tuned based on the ConfliBERT model, excelling at identifying entities such as people, organizations, locations, time expressions, and monetary values in text.

English NER model fine-tuned based on RoBERTa-base architecture, supporting 18 entity types

An Arabic named entity recognition model based on the pre-trained BERT model, capable of identifying 8 entity types.

A named entity recognition model fine-tuned on English scientific texts in the life sciences domain, based on the RoBERTa base model