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---
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)](https://aclanthology.org/D19-1473/) *Detecting Causal Language Use in Science Findings*. |