# Multilingual NER

GLiNER is a general-purpose named entity recognition (NER) model capable of identifying any type of entity, providing a practical alternative to traditional NER models.

GLiNER is a general-purpose named entity recognition (NER) model capable of identifying any type of entity, providing a practical alternative to traditional NER models while addressing the high resource consumption issues of large language models.

GLiNER is a general-purpose Named Entity Recognition (NER) model capable of identifying any entity type through a bidirectional Transformer encoder.

GLiNER is a general-purpose named entity recognition (NER) model capable of identifying any type of entity, providing a practical alternative to traditional NER models and large language models.

GLiNER is a general-purpose named entity recognition (NER) model capable of identifying any entity type, providing a practical alternative to traditional NER models.

GLiNER is a multilingual Named Entity Recognition (NER) model capable of identifying any entity type through a bidirectional Transformer encoder, providing a flexible alternative to traditional NER models.

This is a multilingual named entity recognition (NER) model based on DistilBERT, supporting 10 languages, and quantized for compatibility with Transformers.js.

A named entity recognition model fine-tuned based on the XLM-RoBERTa base model, supporting multiple languages and capable of identifying three types of entities: locations, organizations, and persons.

This is a multilingual named entity recognition model trained on the MultiNERD dataset, supporting over 20 languages, using bert-base-multilingual-cased as the underlying encoder.

A named entity recognition model fine-tuned based on microsoft/mdeberta-v3-base, demonstrating outstanding performance on evaluation datasets

Named entity recognition model fine-tuned on the English CoNLL-2003 dataset based on the XLM-RoBERTa-large model

A multilingual named entity recognition model based on the RoBERTa architecture, supporting entity recognition tasks in 20 languages.

A cross-lingual model fine-tuned on German and French datasets based on XLM-RoBERTa-base, primarily used for named entity recognition tasks.

A fine-tuned version based on the XLM-RoBERTa-base model on a specific dataset, primarily used for sequence labeling tasks, with an evaluated F1 score of 0.8561.

A token classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base, used for named entity recognition tasks

A fine-tuned version of the XLM-RoBERTa-base model on German and French datasets, primarily used for sequence labeling tasks.

Named entity recognition model fine-tuned on multilingual datasets based on xlm-roberta-base

Cross-lingual model fine-tuned on German and French datasets based on XLM-RoBERTa-base

A token classification model fine-tuned on the PAN-X German dataset based on XLM-RoBERTa-base

Named entity recognition model fine-tuned based on Geotrend/distilbert-base-en-fr-de-no-da-cased, demonstrating outstanding performance on evaluation datasets

A German token classification model fine-tuned on the xtreme dataset based on xlm-roberta-base

This model is a named entity recognition (NER) model trained on the HiNER-original dataset based on the XLM-RoBERTa-large architecture, specifically designed for token classification tasks.

A German token classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base

A cross-lingual model fine-tuned on German and French datasets based on XLM-RoBERTa-base, primarily used for named entity recognition tasks.

A cross-lingual model fine-tuned on German and French datasets based on xlm-roberta-base, primarily used for named entity recognition tasks.

Named Entity Recognition (NER) model fine-tuned based on xlm-roberta-base, demonstrating excellent performance on the evaluation set (F1 score 0.9777)

A German token classification model fine-tuned on the xtreme dataset based on XLM-RoBERTa-base, designed for named entity recognition tasks.

A fine-tuned version of the XLM-RoBERTa-base model on German and French datasets, primarily used for named entity recognition tasks.

This model is a named entity recognition model fine-tuned on the conll2003 dataset based on XLMR-ENIS, supporting English and Icelandic.

This is an XLM-RoBERTa model fine-tuned for named entity recognition tasks, suitable for entity recognition in multilingual texts.

A multilingual named entity recognition model fine-tuned on the PAN-X dataset based on XLM-RoBERTa-base

Multilingual named entity recognition model based on XLM-Roberta-base, supporting entity recognition in 40 languages

This model is a fine-tuned version of xlm-roberta-base on the xtreme dataset for German token classification tasks.

A CPU-optimized multilingual named entity recognition model supporting location, organization, person, and other entity types

A named entity recognition model fine-tuned on XLM-RoBERTa, supporting token classification tasks in English text.

A named entity recognition model fine-tuned based on XLM-RoBERTa, supporting entity recognition tasks in English text.

A fast 4-class named entity recognition model supporting English, German, Dutch, and Spanish, based on the Flair framework and LSTM-CRF architecture.

Flair's standard 4-class NER model, suitable for named entity recognition tasks in English, German, Dutch, and Spanish

This is a multilingual model based on DistilBERT, specifically fine-tuned for named entity recognition tasks on the CoNLL 2003 dataset.

A named entity recognition model fine-tuned based on XLM-RoBERTa large, supporting 10 high-resource languages, capable of identifying three types of entities: location, organization, and person.