
Binary causal sentence classification: 

* LABEL_0 = Non-causal
* LABEL_1 = Causal

Trained on multiple datasets.
{"language": "en", "license": "unknown", "widget": [{"text": "She fell because he pushed her.", "example_title": "Causal Example 1"}, {"text": "He pushed her, causing her to fall.", "example_title": "Causal Example 2"}, {"text": "She fell onto him.", "example_title": "Non-causal Example 1"}, {"text": "He is Billy and he pushed her.", "example_title": "Non-causal Example 2"}]}
transformers pytorch bert text-classification en license:unknown autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #en #license-unknown #autotrain_compatible #endpoints_compatible #region-us 


Binary causal sentence classification: 

* LABEL_0 = Non-causal
* LABEL_1 = Causal

Trained on multiple datasets.
TAGS
#transformers #pytorch #bert #text-classification #en #license-unknown #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #text-classification #en #license-unknown #autotrain_compatible #endpoints_compatible #region-us 
