# Medical Entity Recognition

EriBERTa is a bilingual domain-specific language model pre-trained on a massive corpus of clinical medical texts. It surpasses all previous Spanish-language models in the clinical domain, demonstrating exceptional medical text comprehension and information extraction capabilities.

The first publicly available medical language model trained on real European Portuguese data

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

A BERT-based named entity recognition model for the Chinese medical domain, capable of identifying professional entities in medical texts.

DrBERT is a French RoBERTa model trained on the open-source French medical text corpus NACHOS, specializing in the biomedical and clinical fields

A named entity recognition model fine-tuned on the BERT-base-cased for the medical insurance domain

RuBioRoBERTa is a pre-trained biomedical language model designed for Russian biomedical text mining, specifically tailored to handle text data in the Russian biomedical domain.

ChineseBLUE is a benchmark dataset for Chinese medical natural language understanding, designed to evaluate model performance on Chinese medical texts.

en_core_med7_trf is a clinical natural language processing model based on spaCy, specifically designed to identify and classify medication-related named entities from electronic health records.