distilbert base fallacy classification This model is a fine tuned version of distilbert base uncased on the Logical Fallacy Dataset. Model description The model is fine tuned for text classification of logical fallacies. There are a total of 14 classes: ad hominem, ad populum, appeal to emotion, circular reasoning, equivocation, fallacy of credibility, fallacy of extension, fallacy of logic, fallacy of relevance, false causality, false dilemma, faulty generalization, intentional, and miscellaneous. Example Pipeline Full Classification Example Training and evaluation data The Logical Fallacy Dataset is used for training and evaluation. Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., ... Schölkopf, B. (2022). Logical Fallacy Detection. arXiv. https://doi.org/10.48550/arxiv.2202.13758 Training procedure The following hyperparameters were used during fine tuning: learning rate : 2e 5 warmup steps : 0 batch size: 16 num epochs: 8 batches per epoch: 122 total train steps: 976
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