Update README.md

README.md

@@ -15,7 +15,9 @@ tags:
 The model categorizes images into two classes:  
 - **Class 0:** "Issue in Deepfake" – indicating that the deepfake image has noticeable flaws or inconsistencies.  
 - **Class 1:** "High-Quality Deepfake" – indicating that the deepfake image is of high quality and appears more realistic.
-  
+- 
+# **Run with Transformers**
+
 ```python
 !pip install -q transformers torch pillow gradio
 ```
@@ -68,6 +70,4 @@ The **Deepfake-Quality-Assess-Siglip2** model is designed to evaluate the qualit
 - **Deepfake Quality Assessment:** Identifying whether a generated deepfake meets high-quality standards or contains artifacts and inconsistencies.  
 - **Content Moderation:** Assisting in filtering low-quality deepfake images in digital media platforms.  
 - **Forensic Analysis:** Supporting researchers and analysts in assessing the credibility of synthetic images.  
-- **Deepfake Model Benchmarking:** Helping developers compare and improve deepfake generation models.  
-
-This model is intended for research, forensic analysis, and quality control applications rather than real-time detection of deepfake authenticity.
+- **Deepfake Model Benchmarking:** Helping developers compare and improve deepfake generation models.