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---------

Co-authored-by: Shan Jiang <[email protected]>

Author: JewelRoam

README.md

@@ -1,6 +1,6 @@
 # GraphNet  ![](https://img.shields.io/badge/version-v0.1-brightgreen) ![](https://img.shields.io/github/issues/PaddlePaddle/GraphNet?label=open%20issues)    [![](https://img.shields.io/badge/Contribute%20to%20GraphNet-blue)](https://github.com/PaddlePaddle/GraphNet/issues/98)
 
-**GraphNet** is a large-scale dataset of deep learning **computation graphs**, built as a standard benchmark for **tensor compiler** optimization. It provides 2.7K computation graphs extracted from state-of-the-art deep learning models spanning diverse tasks and ML frameworks. With standardized formats and rich metadata, GraphNet enables fair comparison and reproducible evaluation of the general optimization capabilities of tensor compilers, thereby supporting advanced research such as AI for System on compilers ([**ai4c**](https://github.com/PaddlePaddle/ai4c)).
+**GraphNet** is a large-scale dataset of deep learning **computation graphs**, built as a standard benchmark for **tensor compiler** optimization. It provides 2.7K computation graphs extracted from state-of-the-art deep learning models spanning diverse tasks and ML frameworks. With standardized formats and rich metadata, GraphNet enables fair comparison and reproducible evaluation of the general optimization capabilities of tensor compilers, thereby supporting advanced research such as AI for System on compilers (AI for Compiler).
 
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@@ -133,7 +133,7 @@ The script is designed to process a file structure as `/benchmark_path/compiler_
 3. Extract samples from multi-GPU scenarios to support benchmarking and optimization for large-scale, distributed computing.
 4. Enable splitting full graphs into independently optimized subgraphs and operator sequences.
 
-**Vision**: GraphNet aims to lay the foundation for [ai4c](https://github.com/PaddlePaddle/ai4c) by enabling **large-scale, systematic evaluation** of tensor compiler optimizations, and providing a **dataset for models to learn** and transfer optimization strategies.
+**Vision**: GraphNet aims to lay the foundation for AI for Compiler by enabling **large-scale, systematic evaluation** of tensor compiler optimizations, and providing a **dataset for models to learn** and transfer optimization strategies.
 
 ## 💬 GraphNet Community