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  This model uses BERT to detect cause and effect from a single sentence. The focus of this model is the domain of software requirements engineering, however, it can also be used for other domains.
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  The model outputs one of the following 5 labels for each token:
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  Other
 
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  B-Cause
 
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  I-Cause
 
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  B-Effect
 
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  I-Effect
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- ---
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- widget:
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- - text: "If a user signs up, he will receive a confirmation email."
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+ widget:
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+ - text: "If a user signs up, he will receive a confirmation email."
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+ # Cause-Effect Detection for Software Requirements Based on Token Classification with BERT
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  This model uses BERT to detect cause and effect from a single sentence. The focus of this model is the domain of software requirements engineering, however, it can also be used for other domains.
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  The model outputs one of the following 5 labels for each token:
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  Other
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  B-Cause
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  I-Cause
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  B-Effect
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  I-Effect
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+ The source code can be found here: https://colab.research.google.com/drive/14V9Ooy3aNPsRfTK88krwsereia8cfSPc?usp=sharing