readme: ---
  
  language: en
  license: unknown
  widget:
  - text: "<ARG1>She fell</ARG1> because <ARG0>he pushed her</ARG0> ."
    example_title: "Causal Example 1"
  - text: "<ARG0>He pushed her</ARG0> , <ARG1>causing her to fall</ARG1>."
    example_title: "Causal Example 2"
  - text: "<ARG0>She fell</ARG0> because <ARG1>he pushed her</ARG1> ."
    example_title: "Non-causal Example 1"
  - text: "<ARG1>He is Billy</ARG1> and <ARG0>he pushed her</ARG0>."
    example_title: "Non-causal Example 2"
  ---
  
  Binary causal sentence classification with argument prompts: 
  
  * LABEL_0 = Non-causal
  * LABEL_1 = Causal (ARG0 causes ARG1)
  
  Trained on multiple datasets.
  For Causal sequences, try swapping the arguments to observe the prediction results.