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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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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
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