chore: fix release script

hanxiao · hanxiao · commit 5d78e7bfed63 · 2022-01-09T15:23:13.000+01:00
diff --git a/README.md b/README.md
@@ -18,7 +18,7 @@ DocArray is a library for nested, unstructured data such as text, image, audio,
 
 🐍 **Pythonic experience**: Designed to be as easy as Python list. If you know how to Python, you know how to DocArray. Intuitive idioms and type annotation simplify the code you write.
 
-🧑‍🔬 **Data science powerhouse**: greatly facilitate data scientists work on embedding, matching, visualizing, evaluating via Torch/Tensorflow/ONNX/PaddlePaddle on CPU/GPU.
+🧑‍🔬 **Data science powerhouse**: greatly accelerate data scientists work on embedding, matching, visualizing, evaluating via Torch/Tensorflow/ONNX/PaddlePaddle on CPU/GPU.
 
 🚡 **Portable**: ready-to-wire at anytime with efficient and compact serialization from/to Protobuf, bytes, JSON, CSV, dataframe.
 
diff --git a/docs/fundamentals/documentarray/index.md b/docs/fundamentals/documentarray/index.md
@@ -20,7 +20,7 @@ In a nutshell, you can simply consider it as a Python `list`, as it implements *
 
 It is also powerful as Numpy `ndarray`, allowing you to [access elements](access-elements.md) and [attributes](access-attributes.md).
 
-What makes it more exciting is those advanced features of DocumentArray. These features greatly facilitate data scientists work on accessing nested elements, evaluating, visualizing, parallel computing, serializing, matching etc. 
+What makes it more exciting is those advanced features of DocumentArray. These features greatly accelerate data scientists work on accessing nested elements, evaluating, visualizing, parallel computing, serializing, matching etc. 
 
 ## What's next?
 
diff --git a/docs/get-started/what-is.md b/docs/get-started/what-is.md
@@ -5,7 +5,7 @@
 - It is like `pandas.DataFrame`, but for nested and mixed media data.
 - It is like Protobuf, but for data scientists and deep learning engineers. 
 
-If you are a **data scientist** who works with image, text, video, audio data in Python all day, you should use DocArray: it can greatly facilitate the work on representing, embedding, matching, visualizing, evaluating, sharing data; while stay close with your favorite toolkits, e.g. Torch, Tensorflow, ONNX, PaddlePaddle, JupyterLab, Google Colab.
+If you are a **data scientist** who works with image, text, video, audio data in Python all day, you should use DocArray: it can greatly accelerate the work on representing, embedding, matching, visualizing, evaluating, sharing data; while stay close with your favorite toolkits, e.g. Torch, Tensorflow, ONNX, PaddlePaddle, JupyterLab, Google Colab.
 
 If you are a **deep learning engineer** who works on scalable deep learning service, you should use DocArray: it can be the basic building block of your system. Its portable data structure can be wired in Protobuf, compressed bytes, JSON; allowing your engineer friends to happily integrate it into the production system.
 
