# High-precision entity recognition

An Italian named entity recognition model fine-tuned based on Italian-ModernBERT-base

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.

An Italian named entity recognition model fine-tuned based on dbmdz/bert-base-italian-cased, which performs excellently on the wiki_neural dataset.

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

Turkish transformer pipeline for TrSpaCy, including transformer, tokenizer, morphological analyzer, lemmatizer, parser, and named entity recognizer components.

Vietnamese named entity recognition model based on ELECTRA architecture, fine-tuned on VLSP 2018 dataset

Biomedical named entity recognition model fine-tuned on the bc2gm_corpus dataset based on BioBERT

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

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

Catalan named entity recognition model based on RoBERTa architecture, fine-tuned on the AnCora-Ca-NER dataset

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

This is an entity recognition model trained using AutoTrain, featuring high accuracy and F1 score.

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

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.

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

Named Entity Recognition model based on BioBERT, fine-tuned on the CRAFT English dataset

A named entity recognition model fine-tuned on the CRAFT corpus based on BioBERT, used to identify 6 types of entities in biomedical texts

Flair framework's built-in large English NER model for 4 entity types, utilizing document-level XLM-R embeddings and FLERT technique, achieving an F1 score of 94.36 on the CoNLL-03 dataset.

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 Turkish named entity recognition model fine-tuned based on xlm-roberta-base, used to identify person names, organization names, and location names in Turkish texts.

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

Named entity recognition model for Danish, Norwegian, Swedish, Icelandic, and Faroese, fine-tuned based on NbAiLab/nb-bert-base

A fine-tuned version based on the Spanish BERT cased model (BETO) on the NER-C dataset, specifically designed for Named Entity Recognition (NER) tasks.

A Hungarian named entity recognition model fine-tuned based on SZTAKI-HLT/hubert-base-cc, trained using the WikiANN dataset.

Flair's standard 4-class NER model for Dutch, used to identify person names, location names, organization names, and other names in Dutch text.

Slovak named entity recognition model fine-tuned on FERNET-CC_sk, supporting recognition of three entity types: locations, persons, and organizations

A Turkish named entity recognition model fine-tuned based on the dbmdz/bert-base-turkish-cased model, capable of recognizing entities such as person names, organization names, and location names.

This model is a fine-tuned named entity recognition model based on SlovakBERT on the Slovak wikiann dataset, supporting the recognition of three types of entities: geographical locations, persons, and organizations.

A named entity recognition model for the Portuguese legal domain, fine-tuned based on the BERT large architecture

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

A Spanish named entity recognition (NER) model based on the RoBERTa architecture, pre-trained on the BNE corpus and fine-tuned on the CAPITEL dataset, with optimized performance for lowercase named entity recognition.

A multilingual named entity recognition model based on XLM-RoBERTa, supporting 20 languages, capable of identifying three types of entities: locations, organizations, and person names.

This model is a Spanish named entity recognition model based on the RoBERTa architecture, pre-trained on the large-scale BNE corpus and fine-tuned using the CAPITEL-NERC dataset

Spanish named entity recognition model based on RoBERTa architecture, pre-trained on BNE corpus and fine-tuned on CAPITEL-NERC dataset

Named entity recognition model fine-tuned on the English CoNLL2003 dataset based on XLM-RoBERTa-large