Merge branch 'dev' into 'main'

Dev into main

See merge request learnware/learnware-market!9

docs/advanced/Heterogeneous Learnware.rst

@@ -2,12 +2,12 @@
 Heterogeneous learnware
 ==============================
 
-The learnware market naturally consits of models with different feature spaces, different label spaces or different objectives. It is beneficial for the market to accomendate these heterogeneous learnwares and  provide corresponding learnware recommendation and reuse service to the user, so as to expand the applicable scope of learnware paradigm.
+The learnware market naturally consists of models with different feature spaces, different label spaces, or different objectives. It is beneficial for the market to accommodate these heterogeneous learnwares and provide corresponding learnware recommendation and reuse services to the user so as to expand the applicable scope of learnware paradigm.
 
-Models are submitted to the market with their original specifications, however, these specifcations are hard to be used for responding to user requirement due to heterogenousity. Specifcations of heterogenenous models reside in different specification spaces, the market needs to merge these specification spaces to a unfied one. To achive this adjustment, you need to implement the class ``EvolvedMarket``, especially the the function ``EvolvedMarket.generate_new_stat_specification``, which generates new statistical specifcation in an identical space for each submitted model.
+Models are submitted to the market with their original specifications. However, these specifications are hard to be used for responding to user requirements due to heterogeneity. Specifications of heterogeneous models reside in different specification spaces. The market needs to merge these specification spaces into a unified one. To achieve this adjustment, you need to implement the class ``EvolvedMarket``, especially the function ``EvolvedMarket.generate_new_stat_specification``, which generates new statistical specifcation in an identical space for each submitted model.
 
-One important case is that models has different feature space. In order to enable learnware market to handle heterogeneous feature spaces, you need to implement the class ``HeterogeneousFeatureMarket`` in the following way:
+One important case is that models have different feature spaces. In order to enable the learnware market to handle heterogeneous feature spaces, you need to implement the class ``HeterogeneousFeatureMarket`` in the following way:
 
-- First, design a method for the market to connect different feature space to a common subspace and implement the fucntion ``HeterogeneousFeatureMarket.learn_mapping_functions``, this function use specifcations of all submitted models to learn mapping functions which can map the data in the original feature space to the common subspace and vice verse.
+- First, design a method for the market to connect different feature spaces to a common subspace and implement the function ``HeterogeneousFeatureMarket.learn_mapping_functions``. This function uses specifications of all submitted models to learn mapping functions that can map the data in the original feature space to the common subspace and vice verse.
 - Second, use learned mapping functions to implement the functions ``HeterogeneousFeatureMarket.transform_original_to_subspace`` and ``HeterogeneousFeatureMarket.transform_subspace_to_original``.
-- Third, use the functions ``HeterogeneousFeatureMarket.transform_original_to_subspace`` and ``HeterogeneousFeatureMarket.transform_subspace_to_original`` to overwrite the mehtod ``EvolvedMarket.generate_new_stat_specification`` of the base class.
+- Third, use the functions ``HeterogeneousFeatureMarket.transform_original_to_subspace`` and ``HeterogeneousFeatureMarket.transform_subspace_to_original`` to overwrite the mehtod ``EvolvedMarket.generate_new_stat_specification`` and ``EvolvedMarket.EvolvedMarket.evolve_learnware_list`` of the base class ``EvolvedMarket``.

docs/start/intro.rst

@@ -2,7 +2,7 @@
 Introduction
 ================
 
-Learnware is an AI-oriented paradigm aimed at empowering users to create versatile and efficient machine learning models. By reusing small models for tasks beyond their original purposes, Learnware enables users to experiment with and develop better strategies without building models from scratch efficient and safe.
+``Learnware`` is a model sharing platform, which give a basic implementation of the learnware paradigm. A learnware is a well-performed trained machine learning model with a specification that enables it to be adequately identified to reuse according to the requirement of future users who may know nothing about the learnware in advance. The learnware paradigm can solve entangled problems in the current machine learning paradigm, like continual learning and catastrophic forgetting. It also reduces resources for training a well-performed model.
 
 
 Motivation