From 518c0b8737a1f57a53f0b9cba13d106e769a9f46 Mon Sep 17 00:00:00 2001
From: Jael Gu <mengjia.gu@zilliz.com>
Date: Thu, 7 Apr 2022 17:27:07 +0800
Subject: [PATCH] Update README

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
---
 README.md | 22 +++++++++++++---------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index d4dfc04..85afa8d 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 *author: Jael Gu*
 
-
+<br />
 
 ## Desription
 
@@ -10,12 +10,14 @@ A text embedding operator takes a sentence, paragraph, or document in string as
 and output an embedding vector in ndarray which captures the input's core semantic elements.
 This operator is implemented with pretrained models from [Huggingface Transformers](https://huggingface.co/docs/transformers).
 
+<br />
+
 ## Code Example
 
 Use the pretrained model 'distilbert-base-cased'
 to generate a text embedding for the sentence "Hello, world.". 
 
- *Write the pipeline*:
+*Write the pipeline*:
 
 ```python
 from towhee import dc
@@ -26,6 +28,8 @@ dc.stream(["Hello, world."]) \
   .to_list()
 ```
 
+<br />
+
 ## Factory Constructor
 
 Create the operator via the following factory method
@@ -34,11 +38,12 @@ Create the operator via the following factory method
 
 **Parameters:**
 
-​	***model_name***: *str*
+***model_name***: *str*
 
-​	The model name in string.
+The model name in string.
 You can get the list of supported model names by calling `get_model_list` from [auto_transformers.py](https://towhee.io/text-embedding/transformers/src/branch/main/auto_transformers.py).
 
+<br />
 
 ## Interface
 
@@ -49,15 +54,14 @@ and then return text embedding in ndarray.
 
 **Parameters:**
 
-​	***text***: *str*
-
-​	The text in string.
+***text***: *str*
 
+The text in string.
 
 
 **Returns**:
 
-​    *numpy.ndarray*
+*numpy.ndarray*
 
-​	The text embedding extracted by model.
+The text embedding extracted by model.