BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section

• original model file name: textclassifer_BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pubmed_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

• 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