Unimodal Training for Multimodal Meme Sentiment Classification—Metric: Weighted F1-Score

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Unimodal Training for Multimodal Meme Sentiment Classification—Metric: Weighted F1-Score
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Explore the impact of unimodal training on enhancing meme sentiment analysis and strategies in multimodal meme classifiers for improved performance.

Authors: Muzhaffar Hazman, University of Galway, Ireland; Susan McKeever, Technological University Dublin, Ireland; Josephine Griffith, University of Galway, Ireland. Table of Links Abstract and Introduction Related Works Methodology Results Limitations and Future Works Conclusion, Acknowledgments, and References A Hyperparameters and Settings B Metric: Weighted F1-Score C Architectural Details D Performance Benchmarking E Contingency Table: Baseline vs.

“Weighted” here denotes that the F1-score is first computed per-class and then averaged while weighted by the proportion of occurrences of each class in the ground truth labels. We compute this using PyTorch’s implementation multiclass f1 score. Class-wise F1-scores, F1c where c ∈ , are computed as: Where Nc is the number of samples with the ground truth label c in the testing set.

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