🤯 Did You Know (click to read)
BERT can classify documents spanning thousands of words into multiple categories using aggregated token embeddings.
BERT generates contextual embeddings for each token and aggregates them to classify full documents. Fine-tuning enables accurate prediction across multiple topics, genres, or intents. This improves applications like spam detection, news categorization, and sentiment analysis at the document level.
💥 Impact (click to read)
Document classification enhances content management, search organization, and automated tagging. It enables faster insights and reduces manual categorization efforts.
For users, BERT’s classifications appear coherent and relevant. The irony is that predictions are based purely on statistical embedding patterns rather than comprehension.
Source
Devlin et al., 2018, BERT: Pre-training of Deep Bidirectional Transformers
💬 Comments