---
library_name: transformers
tags: []
---

# Model Description

**Comp4Cls** is a retrieval-augmented classification framework that uses **entity-centric semantic compression** to turn long scientific/technical documents into short, task-focused representations for both retrieval and labeling. Documents (papers, patents, and R&D reports) are first compressed into structured summaries that preserve discriminative signals (e.g., core concepts, methods, problems, findings), embedded, and stored in a vector DB. At inference, a query is compressed the same way, nearest neighbors are retrieved, and a small LLM assigns the final class label using the compressed evidence.


The end-to-end workflow—**Phase 1: compression + indexing, Phase 2: retrieval + classification**—is illustrated in the framework diagram on *page 2*. Experiments on a large bilingual corpus with hierarchical, multi-label taxonomies show that a **4B-scale** Comp4Cls matches or outperforms **8B–14B** models, especially in fine-grained categories, while cutting token usage and compute. Moderate compression (often **~20% of entities**) preserves retrieval fidelity and boosts downstream F1, enabling lightweight, low-latency deployment in production pipelines. See *Table II on page 8* (compression vs. length), *Figure 6 on page 9* (retrieval quality under compression), and *Figure 7 on page 10* (accuracy vs. larger LLMs). 

## Framework Diagram

<h2>Framework Diagram</h2>

<p align="center">
  <img src="Comp4Cls.pdf" width="720" alt="Comp4Cls framework diagram">
  <br>
  <em>Figure 1. Two-phase pipeline: compression/indexing then retrieval/classification.</em>
</p>

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