Investigating the efficacy of Semi-Supervisedlearning to generate pseudo labels using US VoterFraud data
In this project, I classify youtube videos into voter fraud and videos that contain information about the US elections/voterfraud using a novel technique similar to pseudo-labeling. Pseudo labelling uses semi-supervised learning such as k-means clustering. First, on the pre-preprocessed voter fraud dataset, I generated word embeddings using different sentence transformer models. Then, I applied k-means clustering using word-embeddings to generate 200 clusters. To automatically label the clusters, I used pseudo labeling. To test how effectively our clusters were labeled, I trained our data on neural-based state-of-the-art transformer models such as BERT and RoBERTa. The best model (Roberta) achieved up to 83% Accuracy-Precision Area (AUPRC).
Run in code folder:
- Clean dataset:
python video_cleaner.py - Sentence embeddings:
python video_vectorizer.py - Cluster embeddings:
python video_cluster.py - Evaluate embeddings:
python cluster_evaluator.py. - Generate Pseudo Labels:
pseduo_labels.pyandremove_cluster_from_labels.py - Generate Graphs:
plot_graphs.py