prrao87/tweet-stance-prediction
Applying NLP transfer learning techniques to predict Tweet stance toward a topic
Implements dual transfer learning architectures—ULMFiT (fastai/LSTM-based) and OpenAI Transformer—for three-class stance classification (Favor/Against/None) on the SemEval 2016 benchmark. Built on PyTorch with spaCy tokenization, it evaluates predictions using official SemEval perl scripts and includes detailed Jupyter notebooks comparing both approaches' effectiveness on unseen stance detection tasks.
107 stars. No commits in the last 6 months.
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107
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Language
Jupyter Notebook
License
MIT
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Last pushed
Feb 10, 2019
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