# High-precision Entity Recognition

A named entity recognition model optimized for Serbian legal documents, fine-tuned based on BERT architecture, used for automatically extracting key entity information from legal texts.

This model is a fine-tuned version of obi/deid_roberta_i2b2 on an unknown dataset, primarily used for text processing tasks.

A specialized model fine-tuned based on the GLiNER architecture for efficient identification of Personally Identifiable Information (PII) and Protected Health Information (PHI)

A fine-tuned model optimized for PII/PHI detection, based on the GLiNER foundation model knowledgator/gliner-bi-small-v1.0

Named entity recognition model optimized with model fusion technology, F1 score improved by 3.25 points to 0.6601

This model is a fine-tuned version of BSC-LT/roberta-base-biomedical-clinical-es for Named Entity Recognition (NER) tasks on Spanish biomedical clinical texts.

This model is a privacy protection model fine-tuned based on the Microsoft DeBERTa-v3-base architecture, specifically designed to identify and mask Personally Identifiable Information (PII) in text.

A BERT-based fine-tuned named entity recognition model specifically designed to identify technology product names in sentences, such as laptops and smartphones.

A BPMN process information extraction model fine-tuned based on bert-base-cased, used to extract key elements such as executors and tasks from textual process descriptions

A named entity recognition model for Portuguese legal texts, fine-tuned based on BERT architecture, designed to identify specific entity categories in legal documents.

A multilingual RoBERTa-based fine-tuned NER model for medieval texts, supporting historical document analysis in Latin, French, and Spanish.

This model is a Named Entity Recognition (NER) model fine-tuned on the PLOD-unfiltered dataset based on ALBERT-large-v2, excelling in the field of scientific literature.

A model trained for named entity recognition tasks using the MIT movie dataset based on the Roberta Base model

A versatile language model based on Transformer architecture, supporting tasks such as text generation, Q&A, and code completion

This is a Named Entity Recognition (NER) model for the Portuguese legal domain, fine-tuned based on the BERT base architecture, specifically designed to identify named entities in legal texts.

BERT-based Portuguese named entity recognition model, specifically fine-tuned for archival documents

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

A German legal named entity recognition model included in the Flair framework, specifically designed for legal texts, supporting 19 types of legal entity recognition

Financial domain specialized pre-trained language model, optimized based on RoBERTa-wwm architecture to enhance performance in financial tasks

A built-in German 4-class large named entity recognition model in the Flair framework, based on XLM-R embeddings and FLERT technique, achieving an F1 score of 92.31 on the CoNLL-03 German dataset.

Transformer-based Persian language understanding model, optimized for Persian Named Entity Recognition (NER) tasks