diff --git a/README.md b/README.md
index 8b3ff5f..1bce52e 100644
--- a/README.md
+++ b/README.md
@@ -170,11 +170,6 @@ Users could view the information all built-in predictors by `list_latency_predic
 
 Users could get a nn-Meter IR graph by applying `model_file_to_graph` and `model_to_graph` by calling the model name or model object and specify the model type. The supporting model types of `model_file_to_graph` include "onnx", "pb", "torch", "nnmeter-ir" and "nni-ir", while the supporting model types of `model_to_graph` include "onnx", "torch" and "nni-ir".
 
-## Benchmark Dataset
-
-To evaluate the effectiveness of a prediction model on an arbitrary DNN model, we need a representative dataset that covers a large prediction scope. As there is no such available latency dataset, nn-Meter collects and generates 26k CNN models. It contains various operators, configurations, and edge connections, with covering different levels of FLOPs and latency. (Please refer the paper for the dataset generation method and dataset numbers.)
-
-We release the dataset, and provide an interface of `nn_meter.dataset` for users to get access to the dataset. Users can also download the data from the [Download Link](https://github.com/microsoft/nn-Meter/releases/download/v1.0-data/datasets.zip) for testing nn-Meter or their own prediction models. 
 
 ## Hardware-aware NAS by nn-Meter and NNI
 
@@ -236,7 +231,7 @@ Refer to [NNI Doc](https://nni.readthedocs.io/en/stable/nas.html) for how to per
 
 ProxylessNAS currently builds a lookup table, that stores the measured latency of each candidate building block in the search space. The latency sum of all building blocks in a candidate model will be treated as the model inference latency. With leveraging nn-Meter in NNI, users can apply ProxylessNAS to search efficient DNN models on more types of edge devices. In NNI implementation, a `HardwareLatencyEstimator` predicts expected latency for the mixed operation based on the path weight of `ProxylessLayerChoice`. To call nn-Meter in NNI ProxylessNAS, users can add the arguments of "`--applied_hardware <hardware> --reference_latency <reference latency (ms)>`" in the [example](https://github.com/microsoft/nni/blob/master/examples/nas/oneshot/proxylessnas/main.py).
 
-## Bench Dataset
+## Benchmark Dataset
 
 To evaluate the effectiveness of a prediction model on an arbitrary DNN model, we need a representative dataset that covers a large prediction scope. nn-Meter collects and generates 26k CNN models. (Please refer the paper for the dataset generation method.)
 
diff --git a/examples/nn-meter presentation/1.quick_start.ipynb b/examples/nn-meter presentation/1.quick_start.ipynb
index 0af3d04..fddce6b 100644
--- a/examples/nn-meter presentation/1.quick_start.ipynb	
+++ b/examples/nn-meter presentation/1.quick_start.ipynb	
@@ -8,6 +8,7 @@
     "nn-Meter is a novel and efficient toolkit to accurately predict the inference latency of DNN models on diverse edge devices. nn-Meter has achieved the **Mobisys 21 Best Paper Award**, here is the paper link: [nn-Meter: towards accurate latency prediction of deep-learning model inference on diverse edge devices](https://dl.acm.org/doi/10.1145/3458864.3467882). nn-Meter has been released as open source in [GitHub](https://github.com/microsoft/nn-Meter) and released as python package. In this notebook, we will try to leap the first step to use nn-Meter predictor, benchmark dataset and nn-Meter building tools. Let's start our journey!\n",
     "\n",
     "nn-Meter supports and is tested on Ubuntu >= 16.04, macOS >= 10.14.1, and Windows 10/11. Simply run the following `pip install` in an environment that has `python 64-bit >= 3.6`.\n",
+    "\n",
     "```bash\n",
     "pip install nn-meter\n",
     "```\n",
@@ -30,23 +31,34 @@
     "  --list-predictors     list all supported predictors\n",
     "```\n",
     "\n",
-    "We provide three main usages for nn-Meter here.\n",
+    "We provide two main usages for nn-Meter here.\n",
     "\n",
     "- Use nn-Meter for latency prediction\n",
     "\n",
     "- Use nn-Meter benchmark dataset\n",
     "\n",
-    "- Use nn-Meter building tools\n",
-    "\n",
-    "To run the jupyter notebook in this folder, users should download and unzip the test model data from [this link](https://github.com/microsoft/nn-Meter/blob/nn-Meter-pre/examples/nn-meter%20presentation/testmodel.zip)."
+    "To run the jupyter notebook in this folder, users should download and unzip the test model data from [this link](https://github.com/microsoft/nn-Meter/blob/nn-Meter-pre/examples/nn-meter%20presentation/testmodel.zip), and copy them to the user's project folder."
    ]
   }
  ],
  "metadata": {
-  "language_info": {
-   "name": "python"
+  "kernelspec": {
+   "display_name": "nn-meter1.1-test",
+   "language": "python",
+   "name": "nn-meter1.1-test"
   },
-  "orig_nbformat": 4
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
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  "nbformat": 4,
  "nbformat_minor": 2