
Cause-effect span detection for causal sequences: 

```label_to_id = {'B-C': 0, 'B-E': 1, 'I-C': 2, 'I-E': 3, 'O': 4}```

* LABEL_0 = B-C
* LABEL_1 = B-E
* LABEL_2 = I-C
* LABEL_3 = I-E
* LABEL_4 = O

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"}]}
transformers pytorch bert token-classification en license:unknown autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #en #license-unknown #autotrain_compatible #endpoints_compatible #region-us 


Cause-effect span detection for causal sequences: 



* LABEL_0 = B-C
* LABEL_1 = B-E
* LABEL_2 = I-C
* LABEL_3 = I-E
* LABEL_4 = O

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

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