Update README.md

README.md

@@ -24,7 +24,7 @@ Torch-Pruning (TP) is a framework for structural pruning with the following feat
 * **Examples**: Pruning off-the-shelf models from Huggingface, Timm, Torchvision, including [Large Language Models (LLMs)](https://github.com/VainF/Torch-Pruning/tree/master/examples/LLMs), [Segment Anything Model (SAM)](https://github.com/czg1225/SlimSAM), [Diffusion Models](https://github.com/VainF/Diff-Pruning), [Vision Transformers](https://github.com/VainF/Isomorphic-Pruning), [ConvNext](https://github.com/VainF/Isomorphic-Pruning), [Yolov7](examples/yolov7/), [yolov8](examples/yolov8/),  [Swin Transformers](examples/transformers#swin-transformers-from-hf-transformers), [BERT](examples/transformers#bert-from-hf-transformers), FasterRCNN, SSD, ResNe(X)t, DenseNet, RegNet, DeepLab, etc. A detailed list can be found in 🎨 [Examples](examples).
 
 
-For more technical details, please refer to our CVPR'23 paper. You can also find the [code](reproduce) to reproduce paper results.
+For more technical details, please refer to our CVPR'23 paper.
 > [**DepGraph: Towards Any Structural Pruning**](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html)   
 > *[Gongfan Fang](https://fangggf.github.io/), [Xinyin Ma](https://horseee.github.io/), [Mingli Song](https://person.zju.edu.cn/en/msong), [Michael Bi Mi](https://dblp.org/pid/317/0937.html), [Xinchao Wang](https://sites.google.com/site/sitexinchaowang/)*    
 > *[Learning and Vision Lab](http://lv-nus.org/), National University of Singapore*