Part 6/13:
Generating Embeddings: Using TensorFlow Hub and the Universal Sentence Encoder to transform text (titles + abstracts) into vectors.
Batch Processing: Implementing efficient batch processing for scalability.
Error Handling: Incorporating error catchers to ensure robustness during large-scale processing.
Storage & Download: Zipping and uploading the resulting 29GB embedding dataset to Kaggle for easy access.
This pipeline allowed embedding millions of papers efficiently, making the later search and retrieval processes feasible.