ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section

BiomedNLP-PubMedBERT-base-uncased-abstract-fulltextpubsection

• —original model file name: textclassiferBiomedNLP-PubMedBERT-base-uncased-abstract-fulltextpubmed_20k
• —This is a fine-tuned checkpoint of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext for document section text classification
• —possible document section classes are:BACKGROUND, CONCLUSIONS, METHODS, OBJECTIVE, RESULTS,

usage in python

install transformers as needed:

pip install -U transformers

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

metadata

training_metrics

• —val_accuracy: 0.8678670525550842

• —val_matthewscorrcoef: 0.8222037553787231

• —val_f1score: 0.866841197013855

• —valcrossentropy: 0.3674609065055847

• —epoch: 8.0

• —trainaccuracystep: 0.83984375

• —trainmatthewscorrcoefstep: 0.7790813446044922

• —trainf1scorestep: 0.837363600730896

• —traincrossentropy_step: 0.39843088388442993

• —trainaccuracyepoch: 0.8538406491279602

• —trainmatthewscorrcoefepoch: 0.8031334280967712

• —trainf1scoreepoch: 0.8521654605865479

• —traincrossentropy_epoch: 0.4116102457046509

• —test_accuracy: 0.8578397035598755

• —test_matthewscorrcoef: 0.8091378808021545

• —test_f1score: 0.8566917181015015

• —testcrossentropy: 0.3963385224342346

• —daterun: Apr-22-2022t-19

• —huggingface_tag: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext