Update README.md

README.md

@@ -3,19 +3,17 @@ library_name: transformers
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-# Model Card for Model ID
+# Model Description
 
-<!-- Provide a quick summary of what the model is/does. -->
+**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
 
+![Comp4Cls Framework](comp4cls.pdf)
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-**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). 
 
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.