Update README.md

README.md

@@ -61,25 +61,9 @@ Evaluated using the `model_200h_auto.pt` checkpoint on different video degradati
 ### 4. Stability and Variance Analysis
 Due to high computational resource requirements, comprehensive multiple-run variance testing was isolated to the 100-hour models. The models were trained across 3 different random data shuffles to observe stability and the true impact of human annotations versus auto-generated labels.
 
-| Data Type (100h) | Mean WER (%) | Standard Deviation ($\sigma$) (%) |
+| Data Type (100h) | Mean WER (%) | Standard Deviation (σ) (%) |
 |:---|:---:|:---:|
 | Human Annotated | 53.21 | ± 0.37 |
 | Auto Generated | 53.82 | ± 0.17 |
 
 ---
-
-## 💻 Usage
-
-To use these models, you can download them directly using the `huggingface_hub` library in Python:
-```python
-from huggingface_hub import hf_hub_download
-
-# Download the 200h auto model
-model_path = hf_hub_download(
-    repo_id="vsro200/VSR-Models",
-    filename="checkpoints/model_200h_auto.pt",
-    repo_type="model"
-)
-
-print(f"Model downloaded to: {model_path}")
-```