# Clinical Text Analysis

A medical domain named entity recognition model based on the RoBERTa architecture, used to identify specific entities from medical texts

A specialized named entity recognition model for Chinese medical texts, capable of identifying medical-related entities such as diseases, drugs, and treatment procedures.

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.

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

Biomedical sentence embedding model based on BioLinkBERT, specifically designed for biomedical text similarity calculation

medBERT.de is a German medical natural language processing model based on the BERT architecture, specifically fine-tuned for medical texts, clinical records, and research papers, suitable for various NLP tasks in the medical field.

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

A Portuguese clinical text named entity recognition model trained on BioBERTpt (Full Version), specifically designed for medical entity recognition in the TempClinBr corpus

This model is a fine-tuned version of roberta-base-biomedical-clinical-es on the CRAFT dataset, designed for named entity recognition in biomedical clinical texts.

This is a model based on sentence-transformers that can map sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as sentence similarity calculation and semantic search.