diff --git a/docs/NAS-Bench-201.md b/docs/NAS-Bench-201.md
index 3a2a810..e01191c 100644
--- a/docs/NAS-Bench-201.md
+++ b/docs/NAS-Bench-201.md
@@ -20,9 +20,10 @@ You can simply type `pip install nas-bench-201` to install our api. Please see s
 
 The benchmark file of NAS-Bench-201 can be downloaded from [Google Drive](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) or [Baidu-Wangpan (code:6u5d)](https://pan.baidu.com/s/1CiaNH6C12zuZf7q-Ilm09w).
 You can move it to anywhere you want and send its path to our API for initialization.
-- v1.0: `NAS-Bench-201-v1_0-e61699.pth`, where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial.
-- v1.0: The full data of each architecture can be download from [Google Drive](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
-- v1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
+- [2020.02.25] v1.0: `NAS-Bench-201-v1_0-e61699.pth`, 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] v1.0: The full data of each architecture can be download from [Google Drive](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
+- [2020.02.25] v1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
+- [2020.03.08] v2.0: coming soon (results of two set of hyper-parameters avaliable on all three datasets)
 
 The training and evaluation data used in NAS-Bench-201 can be downloaded from [Google Drive](https://drive.google.com/open?id=1L0Lzq8rWpZLPfiQGd6QR8q5xLV88emU7) or [Baidu-Wangpan (code:4fg7)](https://pan.baidu.com/s/1XAzavPKq3zcat1yBA1L2tQ).
 It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default). If you want to generate NAS-Bench-201 or similar NAS datasets or training models by yourself, you need these data.
diff --git a/lib/nas_201_api/__init__.py b/lib/nas_201_api/__init__.py
index 3eb8b38..f43cec9 100644
--- a/lib/nas_201_api/__init__.py
+++ b/lib/nas_201_api/__init__.py
@@ -4,4 +4,4 @@
 from .api import NASBench201API
 from .api import ArchResults, ResultsCount
 
-NAS_BENCH_201_API_VERSION="v1.1"
+NAS_BENCH_201_API_VERSION="v1.1"  # [2020.02.25]
diff --git a/lib/nas_201_api/api.py b/lib/nas_201_api/api.py
index e172386..fcb4bc8 100644
--- a/lib/nas_201_api/api.py
+++ b/lib/nas_201_api/api.py
@@ -3,8 +3,8 @@
 ############################################################################################
 # NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020 #
 ############################################################################################
-# NAS-Bench-201-v1_0-e61699.pth : 6219 architectures are trained once, 1621 architectures are trained twice, 7785 architectures are trained three times. `LESS` only supports CIFAR10-VALID.
-#
+# [2020.02.25] NAS-Bench-201-v1_0-e61699.pth : 6219 architectures are trained once, 1621 architectures are trained twice, 7785 architectures are trained three times. `LESS` only supports CIFAR10-VALID.
+# [2020.03.08] Next version (coming soon)
 #
 #
 import os, sys, copy, random, torch, numpy as np