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BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section

Text Classification
Transformers
PyTorch
ONNX
Safetensors
English
bert
document sections
sentence classification
document classification
medical
health
biomedical
Inference Endpoints
Model card Files Community
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BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext_pub_section / README.md
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metadata
language:
  - en
datasets:
  - pubmed
metrics:
  - f1
pipeline_tag: text-classification
widget:
  - text: >-
      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.
    example_title: background example
  - text: a total of 192 mi patients and 140 control persons were included.
    example_title: methods example
  - text: >-
      mi patients had 18 % higher plasma levels of map44 (iqr 11-25 %) as
      compared to the healthy control group (p < 0. 001.)
    example_title: results example
  - text: >-
      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.
    example_title: conclusions example
  - text: >-
      in order to understand and update the prevalence of myopia in taiwan, a
      nationwide survey was performed in 1995.
    example_title: objective example

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,

metadata

training_metrics

  • val_accuracy: 0.8724678754806519

  • val_matthewscorrcoef: 0.8290084004402161

  • val_f1score: 0.8707401752471924

  • val_cross_entropy: 0.3521413803100586

  • epoch: 4.0

  • train_accuracy_step: 0.6875

  • train_matthewscorrcoef_step: 0.5752688050270081

  • train_f1score_step: 0.6875

  • train_cross_entropy_step: 0.9718704223632812

  • train_accuracy_epoch: 0.8582640886306763

  • train_matthewscorrcoef_epoch: 0.8091862797737122

  • train_f1score_epoch: 0.8565496206283569

  • train_cross_entropy_epoch: 0.39623069763183594

  • test_accuracy: 0.863082766532898

  • test_matthewscorrcoef: 0.816676676273346

  • test_f1score: 0.8613550662994385

  • test_cross_entropy: 0.3769755959510803

  • date_run: Apr-21-2022_t-02

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