BiomedNLP-PubMedBERT Open-source Model - Free Deployment to Boost the PubMedQA Task of Biomedical Text

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Biomednlp PubMedBERT Base Uncased Abstract Fulltext Finetuned Pubmedqa 2

Developed by blizrys

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

Question Answering System

Transformers

Open Source License:MIT #Biomedical Text Classification #PubMedBERT Fine-tuning #Medical Q&A

Downloads 16

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of PubMedBERT, pre-trained on biomedical literature abstracts and full-text data, specifically optimized for the PubMedQA question-answering task. Primarily used for text classification tasks in the biomedical domain.

Model Features

Biomedical Domain Optimization

Specifically pre-trained and fine-tuned for biomedical literature, demonstrating superior performance in medical text processing

PubMedQA Task Adaptation

Optimized for the PubMedQA biomedical question-answering dataset

Model Capabilities

Biomedical Text Classification

Medical Literature Q&A

Use Cases

Medical Research

Medical Literature Classification

Automatic classification of PubMed literature

54% accuracy (based on evaluation set)

Medical Q&A System

Answering questions based on medical literature

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	57	1.3510	0.54
No log	2.0	114	0.9606	0.54
No log	3.0	171	0.9693	0.54
No log	4.0	228	1.0445	0.54
No log	5.0	285	1.0005	0.54

Property	Details
Model Type	Fine - tuned version of microsoft/BiomedNLP - PubMedBERT - base - uncased - abstract - fulltext
Training Data	None
Metrics	Accuracy
Model Index Name	BiomedNLP - PubMedBERT - base - uncased - abstract - fulltext - finetuned - pubmedqa - 2
Task	Text Classification
Accuracy on Evaluation Set	0.54

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Biomednlp PubMedBERT Base Uncased Abstract Fulltext Finetuned Pubmedqa 2

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

🔧 Technical Details

Training procedure

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Training results

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