
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.
{"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"}]}
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 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.
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 
