README.md

metadata

license: mit
language:
  - en
metrics:
  - accuracy
  - mse
  - f1
base_model:
  - dmis-lab/biobert-base-cased-v1.2
  - google-bert/bert-base-cased
pipeline_tag: text-classification
model-index:
  - name: bert-causation-rating-pubmed
    results:
      - task:
          type: text-classification
        dataset:
          name: pubmed_textdata
          type: dataset
        metrics:
          - name: off by 1 accuracy
            type: accuracy
            value: 83.5621
          - name: mean squared error for ordinal data
            type: mse
            value: 0.8108
          - name: weighted F1 score
            type: f1
            value: 0.8208
          - name: Kendall's tau coefficient
            type: Kendall's tau
            value: 0.7929

Model

This is a BioBERT based model trained on a set of manually annotated texts with causation labels, tasked with classifying a sentence into different levels of strength of causation. This rating-pubmed version is tuned on the dataset provided in a published article Yu et al. (2019) Detecting Causal Language Use in Science Findings.