# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("noahjadallah/cause-effect-detection")
model = AutoModelForTokenClassification.from_pretrained("noahjadallah/cause-effect-detection")Quick Links
Cause-Effect Detection for Software Requirements Based on Token Classification with BERT
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.
The model outputs one of the following 5 labels for each token:
Other
B-Cause
I-Cause
B-Effect
I-Effect
The source code can be found here: https://colab.research.google.com/drive/14V9Ooy3aNPsRfTK88krwsereia8cfSPc?usp=sharing
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="noahjadallah/cause-effect-detection")