readme: ---
  language: en
  tags:
  - scibert
  - token-classification
  - medical-domain
  metrics:
  - f1
  - precision
  - recall
  dataset:
  - Mathking/primary_outcomes
  widget:
  - text: "The FIRST primary outcome is pain at 12 months as measured by the VAS. The primary analysis is to assess whether surgical correction "
    example_title: "PubMed Article : Methods section"
  ---
  
  # Scibert Finetuned for Primary Outcomes extraction in Biomedical Text
  
  
  ## Model description
  SciBERT (Scivocab Uncased Version) finetuned on dataset from A. Koroleva ).
  This model is trained to detect primary outcomes (token classification) in biomedical articles.
  
  ## Intended uses & limitations
  This model was trained to detect primary outcomes in medical articles.
  
  ## Training data
  [Dataset from A. Koroleva](https://zenodo.org/record/3234811#.YnodlVzP2EI)
  
  ## Evaluation results
  Here are the results from the different models evaluated. Evaluation is done using 10-fold cross validation using the 10 splits originally defined in the dataset.
  
  |    | model                    |       f1 |   precision |   recall |
  |---:|:-------------------------|---------:|------------:|---------:|
  |  1 | biobert-v1.1             | 0.873347 |    0.870358 | 0.878657 |
  |  2 | scibert_scivocab_uncased | 0.870242 |    0.863077 | 0.879617 |
  |  3 | biobert_v1.0_pubmed_pmc  | 0.863499 |    0.858515 | 0.8713   |
  |  4 | scibert_scivocab_cased   | 0.85848  |    0.859122 | 0.862749 |
  