[DOC] modify details in intro

docs/start/intro.rst

@@ -3,29 +3,29 @@
 Introduction
 ================
 
-The learnware paradigm attempts to help the user reuse existed well-trained models to solve their problems instead of starting from scratch. The ``Learnware`` package offers a fundamental implementation of the central concepts and procedures for the learnware paradigm.
+The ``learnware`` package provides a fundamental implementation of the central concepts and procedures for the learnware paradigm, which is a new paradigm aimed at enabling users to reuse existed well-trained models to solve their AI tasks instead of starting from scratch.
 
-The ``Learnware`` packag effectively organizes related concepts and core functionalities of the learnware paradigm while maintaining high scalability. This design enables effortless integration of various new features and techniques in the future.
+Moreover, the package's well-structured design ensures high scalability and allows for the effortless integration of various new features and techniques in the future.
 
-The ``Learnware`` package serves as the engine for the `Beimingwu System <https://bmwu.cloud/#/>`_ and can also be utilized for experiments related to learnware.
+In addition, the ``learnware`` package serves as the engine for the `Beimingwu System <https://bmwu.cloud/#/>`_ and can be effectively employed for conducting experiments related to learnware.
 
-What is Learnware ?
-================
+What is Learnware?
+====================
 
 A learnware consists of high-performance machine learning models and specifications that characterize the models, i.e., "Learnware = Model + Specification."
 
 The learnware specification consists of "semantic specification" and "statistical specification":
 
-- semantic specification describes the type and functionality of the model through text.
-- statistical specification characterizes the statistical information contained in the model using various machine learning techniques.
+- ``Semantic Specification``: Describe the type and functionality of the model through text.
+- ``Statistical Specification``: Characterize the statistical information contained in the model using various machine learning techniques.
 
 Learnware specifications describe the model's capabilities, enabling the model to be identified and reused by future users who may know nothing about the learnware in advance.
 
-Why do we need Learnware ?
-================
+Why do we need Learnware?
+============================
 
 The Benefits of Learnware Paradigm
------------------
+-------------------------------------
 
 Machine learning has achieved great success in many fields but still faces various challenges, such as the need for extensive training data and advanced training techniques, the difficulty of continuous learning, the risk of catastrophic forgetting, and the leakage of data privacy.
 
@@ -56,23 +56,24 @@ Although there are many efforts focusing on one of these issues separately, they
 +-----------------------+-----------------------------------------------------------------------------------------------+
 
 How to Solve Future Tasks with Learnware Paradigm?
---------------
+----------------------------------------------------
 
 .. image:: ../_static/img/learnware_paradigm.jpg
    :align: center
 
-Instead of building a model from scratch, users can submit their requirements to the learnware market, which then identifies and deploys helpful learnware(s) based on the specifications. Users can apply the learnware directly, adapt it using their data, or exploit it in other ways to improve their model. This process is more efficient and less expensive than building a model from scratch.
+Instead of building a model from scratch, users can submit their requirements to the learnware market, which then identifies and deploys helpful learnware(s) based on the specifications. Users can apply the learnware directly, adapt it using their data, or exploit it in other ways to improve their models. This process is more efficient and less expensive than building a model from scratch.
+
 
 Procedure of Learnware Paradigm
-================
-- **Submitting Stage**: Developers voluntarily submit various learnwares to the learnware dock system, and the system conducts quality checks and further organization of these learnwares.
-- **Deploying Stage**: When users submit task requirements, the learnware dock system automatically selects whether to recommend a single learnware or a combination of multiple learnwares and provides efficient deployment methods. Whether it's a single learnware or a combination of multiple learnwares, the system offers convenient learnware reuse interfaces.
+==================================
+- ``Submitting Stage``: Developers voluntarily submit various learnwares to the learnware market, and the system conducts quality checks and further organization of these learnwares.
+- ``Deploying Stage``: When users submit task requirements, the learnware market automatically selects whether to recommend a single learnware or a combination of multiple learnwares and provides efficient deployment methods. Whether it's a single learnware or a combination of multiple learnwares, the system offers convenient learnware reuse interfaces.
 
 .. image:: ../_static/img/learnware_market.svg
    :align: center
 
 
 Learnware Package Design
-================
+==========================
 
 TBD by xiaodong.