# Biomedical Text Processing

Turkish pre-trained model based on ModernBERT architecture, supporting 8192 context length with excellent performance across multiple domains

This model is a fine-tuned version of t5-small, optimized for summarizing scientific and medical texts using the PubMed dataset.

BiomedBERT is a large-scale language model pretrained from scratch on PubMed abstracts, specifically designed to enhance performance in biomedical natural language processing tasks.

This is a BERT-based pre-trained language model for the biomedical domain, suitable for processing biomedical text data.

RoBERTa-based Spanish clinical trial text named entity recognition model capable of detecting 4 medical semantic groups

A fine-tuned model based on BioBERT for the biomedical domain, achieving an F1 score of 0.8621 on specific tasks

Transformer-based automated de-identification system for radiology reports, achieving privacy protection by detecting Protected Health Information (PHI) and replacing it with realistic surrogate values

Transformer-based automated de-identification system for radiology reports, combining rule-based methods for high-precision PHI recognition and replacement

An abstract generation model fine-tuned from mT5-small, supporting Chinese text summarization tasks

BioLinkBERT is a biomedical language model pre-trained on PubMed abstracts and citation links, enhancing performance through cross-document knowledge integration.

A BERT model trained on the CORD19 coronavirus research paper dataset, fine-tuned for natural language inference tasks to generate universal sentence embeddings

BioBERTpt is a Portuguese clinical and biomedical BERT model based on the BERT architecture, specifically optimized for clinical named entity recognition tasks.

BERT-based Portuguese model trained on clinical records and biomedical literature

Biomedical text processing model fine-tuned on the CORD-19 abstract dataset based on PubMedBERT

RuBioRoBERTa is a RoBERTa model pretrained for Russian biomedical text, specifically designed for natural language processing tasks in the biomedical domain.