# Multilingual Entity Recognition

A multilingual Holocaust-related named entity recognition model fine-tuned based on XLM-RoBERTa, supporting 9 languages with an F1 score of 81.5%.

This model is a fine-tuned version based on xlm-roberta-base for specific datasets, primarily used for named entity recognition tasks.

This is a SpanMarker model trained on the FewNERD dataset for multilingual named entity recognition tasks, based on the xlm-roberta-base encoder.

A named entity recognition model for 21 African languages, fine-tuned based on the Davlan/afro-xlmr-large model, supporting the recognition of four entity types: dates, locations, organizations, and person names.

This model is designed for the Bengali track of the SemEval Multiconer task, focusing on natural language processing tasks such as Named Entity Recognition (NER).

This is a model for the SemEval Multiconer task in the Bengali track, focusing on named entity recognition.

Arabic and English named entity recognition model based on GigaBERTv4, supporting multiple entity type recognition

IndicNER is a model specifically trained for recognizing named entities in sentences of 11 Indian languages, fine-tuned based on the bert-base-multilingual-uncased model.

This is a model for Arabic named entity recognition, capable of identifying entities such as person names, place names, and organization names in text.

Named entity recognition model fine-tuned on the CoNLL2003 dataset based on distilroberta-base

A named entity recognition model for 9 African languages, based on fine-tuned BERT base model

The first named entity recognition model for 10 African languages, fine-tuned on XLM-RoBERTa large, supporting the recognition of four types of entities: dates, locations, organizations, and persons.