BiomedNLP-PubMedBERT Open-Source Model - Free for Identifying Biomedical Literature Section Types

Biomednlp PubMedBERT Base Uncased Abstract Fulltext Pub Section

Developed by ml4pubmed

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

EnglishOpen Source License:Apache-2.0 #Biomedical Text Classification #PubMedBERT Fine-tuning #Literature Section Identification

Downloads 748

Release Time : 5/4/2022

Model Overview

This model is a fine-tuned checkpoint of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext, specifically designed for document section text classification tasks in biomedical literature.

Model Features

Biomedical Domain Optimization

Fine-tuned on PubMedBERT, specifically optimized for biomedical literature content

Multi-Section Classification

Capable of identifying text sections such as Background, Conclusion, Methods, Objectives, or Results

High Performance

Achieves 0.857 accuracy and 0.856 F1 score on test datasets

Model Capabilities

Biomedical Text Classification

Literature Section Identification

Research Paper Analysis

Use Cases

Research Literature Processing

Automatic Literature Classification

Automatically classify different parts of research papers into corresponding sections

Improves literature processing efficiency with 85.7% accuracy

Knowledge Extraction Assistance

Assists in extracting specific section content (e.g., Methods or Results) from large volumes of literature

Medical Information Processing

Clinical Trial Report Analysis

Automatically identify different section contents in clinical trial reports

🚀 BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section

This is a fine - tuned model for document section text classification in the biomedical field, which can classify text into different document sections.

🚀 Quick Start

📦 Installation

Install transformers as needed:

pip install -U transformers

💻 Usage Examples

Basic Usage

Run the following, changing the example text to your use case:

from transformers import pipeline

model_tag = "ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section"
classifier = pipeline(
              'text-classification', 
              model=model_tag, 
            )
            
prompt = """
Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.
"""

classifier(
    prompt,
) # classify the sentence

📚 Documentation

Metadata

Training Metrics

Property	Details
val_accuracy	0.8678670525550842
val_matthewscorrcoef	0.8222037553787231
val_f1score	0.866841197013855
val_cross_entropy	0.3674609065055847
epoch	8.0
train_accuracy_step	0.83984375
train_matthewscorrcoef_step	0.7790813446044922
train_f1score_step	0.837363600730896
train_cross_entropy_step	0.39843088388442993
train_accuracy_epoch	0.8538406491279602
train_matthewscorrcoef_epoch	0.8031334280967712
train_f1score_epoch	0.8521654605865479
train_cross_entropy_epoch	0.4116102457046509
test_accuracy	0.8578397035598755
test_matthewscorrcoef	0.8091378808021545
test_f1score	0.8566917181015015
test_cross_entropy	0.3963385224342346
date_run	Apr-22-2022_t-19
huggingface_tag	microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext

Additional Information

Language: English
Datasets: pubmed, ml4pubmed/pubmed-classification-20k
Metrics: f1
Tags: text-classification, document sections, sentence classification, document classification, medical, health, biomedical
Pipeline Tag: text-classification
License: apache-2.0

Widget Examples

Background Example: many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti - inflammatory actions.
Methods Example: a total of 192 mi patients and 140 control persons were included.
Results Example: mi patients had 18 % higher plasma levels of map44 (iqr 11 - 25 %) as compared to the healthy control group (p < 0. 001.)
Conclusions Example: the finding that a brief cb group intervention delivered by real - world providers significantly reduced mdd onset relative to both brochure control and bibliotherapy is very encouraging, although effects on continuous outcome measures were small or nonsignificant and approximately half the magnitude of those found in efficacy research, potentially because the present sample reported lower initial depression.
Objective Example: in order to understand and update the prevalence of myopia in taiwan, a nationwide survey was performed in 1995.

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