Update README.md

README.md

@@ -23,7 +23,7 @@ The training data is sourced from two types of datasets:
 - **Coarse semantic labels**: 8.5 million images paired with captions of varying quality, ranging from well-defined descriptions to noisy and less relevant text.
 
 ### 2. Data Filtering
-To refine the coarse dataset, we propose a data filtering strategy using the CLIP-based model, $\text{CLIP}_{\text{Sem}}$. This model is pre-trained on high-quality captions to ensure that only semantically accurate image-text pairs are retained. The similarity scores (SS) between each image-text pair are calculated, and captions with low similarity are discarded.
+To refine the coarse dataset, we propose a data filtering strategy using the CLIP-based model, CLIP_Sem. This model is pre-trained on high-quality captions to ensure that only semantically accurate image-text pairs are retained. The similarity scores (SS) between each image-text pair are calculated, and captions with low similarity are discarded.
 
 ![Data Purification Process](figure/newversion.png)
 *Figure 1: Data Refinement Process of the CLIP-RS Dataset. Left: Workflow for filtering and refining low-quality captions. Right: Examples of low-quality captions and their refined versions.*