Language Models for Text Classification: From Bag-of-Words to Jev
11 hours ago
- Jev is a fast and cheap classifier that performs well on diverse tasks out of the box, without needing fine-tuning.
- It offers a user-friendly API with Choice, Noul, and Score endpoints for various classification needs.
- The article traces the history of text classification from bag-of-words and RNNs to CNNs and transformer models like BERT and GPT.
- Jev's likely architecture is a small transformer (similar to ModernBERT) trained on synthetic data using Reinforcement Learning for Calibrated Decisions (RLCD).
- Calibration of probabilities is discussed, with RLCR and Brier loss as methods to improve confidence accuracy.
- Jev excels as a plug-and-play classifier for one-off or low-volume tasks, reducing the need for custom fine-tuning or expensive LLMs.
- While many quick Jev clones exist, none match its performance breadth; a strong open-weight alternative is still desired for privacy and speed.