# PubMedBERT Fine-tuning

An extractive summarization model fine-tuned on the MS^2 dataset based on the PubMedBERT pre-trained model, specifically designed for biomedical text summarization tasks.

PubMedNCL is a pre-trained biomedical document representation language model based on PubMedBERT, fine-tuned through citation neighborhood contrastive learning.

This is a named entity recognition model fine-tuned on PubMedBERT, specifically designed to identify gene and protein entities in biomedical texts.

A biomedical literature section classification model fine-tuned on PubMedBERT for identifying text section types

This model is a textual entailment model fine-tuned on the MNLI dataset based on PubMedBERT, specifically designed for natural language inference tasks in the biomedical domain.

A binary classification model fine-tuned on biomedical text using PubMedBERT, designed for marker association classification tasks

A model fine-tuned on biomedical domain text based on PubMedBERT, designed for PubMedQA tasks

This model is a question-answering system fine-tuned on the squad_v2 dataset based on PubMedBERT, suitable for medical domain Q&A tasks.

Biomedical text processing model fine-tuned on the CORD-19 abstract dataset based on PubMedBERT

A sentence-transformers model fine-tuned on the MS-MARCO dataset based on PubMedBERT, suitable for semantic similarity calculation and information retrieval tasks in the medical/health text domain

This model is a text classification model fine-tuned on PubMedBERT for the marker-association-snp-binary-baseline dataset, primarily used for binary classification tasks of SNP markers.

A multi-label text classification model fine-tuned on PubMedBERT for identifying challenges and research directions in scientific literature