This project is the culmination of the course ID2223 - Scalable Machine Learning and Deep Learning. Our team has developed a political fact-checking tool leveraging an LSTM model trained on dynamic data. The project comprises four key components: backfilling pipeline, EDA and feature engineering pipeline, transformation and training pipeline, and deployment into Hugging Face Spaces.
- Hopsworks: For the feature store.
- Modal: For function scheduling and deployment.
- Hugging Face Spaces and Gradio: For UI deployment.
- Data Source: Historical data was sourced from PolitiFact, scraping 600 data points.
- Process: The data was uploaded to the Hopsworks feature store.
- Scheduling: A scheduler was deployed on Modal to retrieve all data points from the page daily.
- Data Storage: The retrieved data was uploaded to the Hopsworks feature store.
- Data Transformation: This included label standardization and encoding, text tokenization and indexing, and converting the data into PyTorch tensors.
- Model Training: We used GloVe embeddings with 2 LSTM layers. Post-training, the model was uploaded to the Hopsworks model registry.
- UI Interface: A simple UI was created on Hugging Face Spaces.
- Implement logic to avoid duplicate data points in the feature store.
- Increase the historical data size to improve model training and performance.
- The model showed inconsistent performance, indicated by large fluctuations in F1 scores, likely due to overfitting.
- Future improvements could focus on expanding the data set and refining the model to enhance accuracy.