# PubMed Optimization

A sentence transformer model trained on the PubMed dataset, supporting multiple embedding dimensions, suitable for biomedical text processing.

This is a Llama3 model fine-tuned on the PubMedQA dataset, specializing in medical Q&A tasks.

MedCPT-Cross-Encoder is a sequence classification model for biomedical information retrieval, particularly suitable for relevance ranking of medical-related articles.

A sentence embedding model based on sentence-transformers, specifically optimized for biomedical literature, capable of mapping text to a 384-dimensional vector space

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

Named entity recognition model fine-tuned on the NCBI disease dataset based on BioBERT

A sentence transformer model optimized based on PubMedBert, specifically designed for medical literature and scientific fact-checking tasks