Merge pull request #123 from Yulv-git/main

Update some links in README_CN.md and fix some typos.

docs/NAS-Bench-201-PURE.md

@@ -29,7 +29,7 @@ You can simply type `pip install nas-bench-201` to install our api. Please see s
 You can move it to anywhere you want and send its path to our API for initialization.
 - [2020.02.25] APIv1.0/FILEv1.0: [`NAS-Bench-201-v1_0-e61699.pth`](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) (2.2G), where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial.
 - [2020.02.25] APIv1.0/FILEv1.0: The full data of each architecture can be download from [
-NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
+NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the trained weights.
 - [2020.02.25] APIv1.0/FILEv1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
 - [2020.03.09] APIv1.2/FILEv1.0: More robust API with more functions and descriptions
 - [2020.03.16] APIv1.3/FILEv1.1: [`NAS-Bench-201-v1_1-096897.pth`](https://drive.google.com/open?id=16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_) (4.7G), where `096897` is the last six digits for this file. It contains information of more trials compared to `NAS-Bench-201-v1_0-e61699.pth`, especially all models trained by 12 epochs on all datasets are avaliable.

docs/NAS-Bench-201.md

@@ -27,7 +27,7 @@ You can simply type `pip install nas-bench-201` to install our api. Please see s
 You can move it to anywhere you want and send its path to our API for initialization.
 - [2020.02.25] APIv1.0/FILEv1.0: [`NAS-Bench-201-v1_0-e61699.pth`](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) (2.2G), where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial.
 - [2020.02.25] APIv1.0/FILEv1.0: The full data of each architecture can be download from [
-NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
+NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the trained weights.
 - [2020.02.25] APIv1.0/FILEv1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
 - [2020.03.09] APIv1.2/FILEv1.0: More robust API with more functions and descriptions
 - [2020.03.16] APIv1.3/FILEv1.1: [`NAS-Bench-201-v1_1-096897.pth`](https://drive.google.com/open?id=16Y0UwGisiouVRxW-W5hEtbxmcHw_0hF_) (4.7G), where `096897` is the last six digits for this file. It contains information of more trials compared to `NAS-Bench-201-v1_0-e61699.pth`, especially all models trained by 12 epochs on all datasets are avaliable.

docs/README_CN.md

@@ -3,7 +3,7 @@
 </p>
 
 ---------
-[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md)
+[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](../LICENSE.md)
 
 自动深度学习库 (AutoDL-Projects) 是一个开源的，轻量级的，功能强大的项目。
 该项目实现了多种网络结构搜索(NAS)和超参数优化(HPO)算法。
@@ -142,8 +142,8 @@
 
 # 其他
 
-如果你想要给这份代码库做贡献，请看[CONTRIBUTING.md](.github/CONTRIBUTING.md)。
-此外，使用规范请参考[CODE-OF-CONDUCT.md](.github/CODE-OF-CONDUCT.md)。
+如果你想要给这份代码库做贡献，请看[CONTRIBUTING.md](../.github/CONTRIBUTING.md)。
+此外，使用规范请参考[CODE-OF-CONDUCT.md](../.github/CODE-OF-CONDUCT.md)。
 
 # 许可证
-The entire codebase is under [MIT license](LICENSE.md)
+The entire codebase is under [MIT license](../LICENSE.md)