# High-precision NER

This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the InLegalNER dataset, specifically designed for named entity recognition tasks in Indian legal texts.

A named entity recognition model for the Portuguese legal domain, compliant with Brazil's LGPD data protection law, specializing in identifying sensitive information in legal texts.

A named entity recognition model fine-tuned on ModernBERT-base, trained on the CoNLL2003 dataset, excelling at identifying person, organization, and location entities.

French Named Entity Recognition model fine-tuned based on CamemBERTa v2, supporting LOC/PER/ORG three-type entity recognition

A medical domain named entity recognition model based on the RoBERTa architecture, used to identify specific entities from medical texts

A biomedical named entity recognition model based on DeBERTaV3, specifically designed to extract structured information such as diseases and medications from clinical texts

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.

CyNER 2.0 is a named entity recognition model specifically designed for the cybersecurity domain, based on the DeBERTa architecture, capable of identifying various cybersecurity-related entities.

A named entity recognition model for identifying address components from Russian strings

A legal named entity recognition model fine-tuned on Legal-BERT for identifying specific entities in legal texts

A model fine-tuned from FewNERD-fine-supervised based on numind/NuNER-v1.0 for recognizing organizational entities (ORG) in text

BERT-based Portuguese named entity recognition model supporting multiple entity categories

A named entity recognition model fine-tuned based on electra-small, capable of identifying three types of entities: locations, person names, and organizations

A DeBERTa-based fine-tuned medical named entity recognition model capable of identifying 41 types of medical entities.

RoBERTaLexPT-base is a Portuguese masked language model specifically optimized for the legal domain, pretrained on LegalPT and CrawlPT corpora.

A cybersecurity entity recognition model fine-tuned on the CyNER dataset based on CYBERT, used to identify named entities related to cyber threats.

A PII (Personally Identifiable Information) recognition model fine-tuned based on distilbert-base-uncased, designed to detect and remove sensitive information from text

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

A cryptocurrency named entity recognition model fine-tuned based on xlm-roberta-base, specializing in identifying cryptocurrency token symbols, names, and blockchain addresses.

A named entity recognition model fine-tuned based on Roberta-large, specifically designed for historical newspaper texts that may contain OCR errors.

A named entity recognition model fine-tuned on the CoNLL2003 dataset based on BERT-base-cased

This model is a named entity recognition model fine-tuned on a financial domain dataset, capable of accurately identifying named entities in financial texts.

A named entity recognition model fine-tuned on BioBERT for detecting medical symptoms from clinical records.

DistilBERT-based LinkedIn skill recognition model for identifying professional skills from text

A Polish named entity recognition model fine-tuned based on HerBERT, capable of identifying three types of entities: person, location, and organization

Korean Named Entity Recognition model based on KoELECTRA-small-v3, supporting 15 entity types

This is a Named Entity Recognition (NER) model fine-tuned on the Russian Wikiann dataset based on the xlm-roberta-large model

A food entity recognition model specifically designed for informal text on social media, fine-tuned based on the RoBERTa architecture

Japanese named entity recognition model fine-tuned based on xlm-roberta-base

YODA is a named entity recognition model optimized for Google Feed products, focusing on feature extraction from e-commerce product metadata

This model is a fine-tuned version of ckiplab/bert-base-chinese-ws for Chinese Named Entity Recognition (NER) tasks, demonstrating outstanding performance on evaluation datasets.

A Korean personal name recognition model fine-tuned based on kor-bert, used to identify and anonymize Korean personal names in text.

This model is a fine-tuned version of Bio_ClinicalBERT on the bc4chemd_ner dataset for chemical drug named entity recognition tasks.

A named entity recognition model fine-tuned on a toy dataset based on the xlm-roberta-large-ner-hrl model

BERT-base-cased fine-tuned Named Entity Recognition (NER) model

A named entity recognition model fine-tuned based on the BERT-base-cased architecture, specifically designed to identify various named entities in text.

Named entity recognition model based on distilbert-base-uncased architecture, fine-tuned on the conll2012_ontonotesv5-english-v4 dataset

A named entity recognition model fine-tuned on the CoNLL2003 dataset based on the RoBERTa-large model, demonstrating excellent performance in NER tasks.

A fine-tuned movie release information recognition model based on distilbert-base-uncased, excelling in identifying named entities related to movie releases

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