# Biomedical Entity Recognition

OpenBioNER is a lightweight BERT model specifically designed for open-domain biomedical named entity recognition (NER). It can identify unseen entity types using only natural language descriptions of target entity types, without requiring retraining.

GENIE is an end-to-end model specifically designed for structuring free text from Electronic Health Records (EHR) to extract biomedical named entities and their related attributes.

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

A clinical text processing model fine-tuned based on Bio_ClinicalBERT, performing well in F1 score

An English named entity recognition model trained on distilbert-base-uncased, specifically designed for identifying biomedical entities (107 entity categories), suitable for text corpora such as case reports.

A model fine-tuned based on microsoft/deberta-large for specific task processing

A model fine-tuned based on roberta-large for specific task processing, with an evaluation loss value of 0.7825

A named entity recognition model fine-tuned on BioBERT, specializing in identifying epidemiological information in rare disease abstracts.

A biomedical named entity recognition model fine-tuned on BC5CDR chemical substances and BC4CHEMD datasets based on the BioBERT architecture

A biomedical entity recognition model based on BioBERT developed by Dmis-lab at Korea University, specializing in feature extraction tasks for the NCBI disease dataset