# Biomedical NLP

BioClinical ModernBERT is a biomedical and clinical natural language processing model built on ModernBERT, with the ability to process long contexts and performs excellently in biomedical and clinical NLP tasks.

This is an improved version based on the merging of multiple 70B parameter models, primarily adjusting the embedding layer dimensions to match standard L3 70B model specifications.

Med-CXRGen-F is a multimodal large language model fine-tuned based on LLaVA-v1.5-7B, specifically designed for radiology report generation tasks, particularly the automatic generation of chest X-ray examination results.

A cell type prediction model based on the Pythia-410m architecture, fine-tuned by converting scRNA-seq data into 'cell sentences' through the Cell2Sentence method, focusing on cell type prediction in single-cell RNA sequencing data.

A large language model for the medical domain developed by John Snow Labs, optimized based on Microsoft Phi-2 architecture

BioMistral-MedMNX is a specialized language model in the biomedical field created by fusing multiple pre-trained models. It uses the DARE and TIES fusion methods to optimize performance.

A multilingual medical text sentence embedding model based on sentence-transformers, supporting 8 languages

A general-purpose sentence embedding model based on the MPNet architecture, optimized for sentence similarity tasks, particularly suitable for clinical trial result similarity analysis.

Bio_ClinicalBERT is a clinical text processing model based on the BERT architecture, specifically optimized for biomedical and clinical domain texts.

A T5 model fine-tuned based on SciFive-base-Pubmed_PMC, specifically designed for radiology report summary generation tasks, trained on the MIMIC-III dataset

DrugGPT is a generative drug design model based on the GPT2 architecture, bringing innovation to drug design through natural language processing technology.

PMC_LLaMA_7B is a version fine-tuned on PMC papers from the S2ORC dataset based on LLaMA-7b, focusing on text generation tasks in the medical field.

Academic paper-specific language model based on DeBERTa v2 architecture, excelling in scientific literature processing tasks

A text summarization generation model fine-tuned on the PubMed abstract dataset based on the t5-small model

A PubMedBERT-based sentence transformer model for generating 768-dimensional vector representations of sentences and paragraphs, suitable for semantic search and clustering tasks.

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

Automated radiology report de-identification system based on transformer and rule-based methods, capable of detecting PHI entities and replacing them with realistic values

MolT5 is a large language model for translation between molecules and natural language, built on the T5 architecture.

This model aims to encode Gene Ontology definitions of proteins into vector representations for exploring gene-level similarities and comparisons between functional terms.

A biomedical language model pretrained on 164,179 COVID-19 abstracts, excelling in COVID-19 topic classification tasks

BioLinkBERT is an improved BERT model pre-trained on PubMed abstracts and literature citation links, excelling in biomedical NLP tasks

BiomedBERT is a biomedical domain-specific language model pretrained on PubMed abstracts and PubMedCentral full-text articles, achieving state-of-the-art performance in multiple biomedical NLP tasks.

A compact language modeling extension of SciBERT, specifically optimized for COVID-19 related research

Large-scale biomedical language model based on BERT, ALBERT, and ELECTRA, specialized for biomedical domain tasks

Bio+Clinical BERT is a clinical BERT model initialized from BioBERT, trained on all notes from MIMIC III, suitable for biomedical and clinical text processing.

This model is trained on Italian biomedical data, specializing in processing Italian-language biomedical content.

A biomedical domain-specific BERT model pretrained on PubMed article abstracts, achieving state-of-the-art performance in multiple biomedical NLP tasks

A BERT model initialized with BioBERT and further pretrained on health-related Reddit posts, specializing in processing medical texts from social media

An HIV-specific protein sequence prediction model optimized based on ProtBert-BFD, enhanced for related tasks through HIV genome fine-tuning

VetBERT is a pretrained language model specifically designed for processing veterinary clinical notes, further trained on veterinary medical domain data based on Bio_ClinicalBERT.

SciBERT-based scientific text sentence encoder, trained on 2 million scientific papers through self-supervised learning

A text generation model fine-tuned on medical literature abstract datasets based on BART-large-cnn, excelling in medical abstract generation tasks

CODER is a knowledge-infused cross-language medical terminology embedding model focused on medical terminology standardization tasks.

CODER is a knowledge-fused cross-language medical terminology embedding model for medical terminology standardization.

A language model further trained on English scientific texts in the life sciences domain, based on the RoBERTa base pre-trained model