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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

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Dataset used to train ml4pubmed/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section