[MNT] modify details

docs/about/dev.rst

@@ -66,7 +66,7 @@ Continuous Integration (CI) tools help you stick to the quality standards by run
 ``pre-commit`` Config
 ========================
 
-The ``Learnware`` Package support config ``pre-commit``. Run the following command to install ``pre-commit``:
+The ``learnware`` package support config ``pre-commit``. Run the following command to install ``pre-commit``:
 
 .. code-block:: bash
 
@@ -82,7 +82,7 @@ Run the following command in the root directory of ``Learnware`` Project to enab
 ``isort`` Config
 ===================
 
-The codes in the ``Learnware`` Package will be processed by ``isort`` (``examples`` and ``tests`` are excluded). Run the following command to install ``isort``:
+The codes in the ``learnware`` package will be processed by ``isort`` (``examples`` and ``tests`` are excluded). Run the following command to install ``isort``:
 
 .. code-block:: bash
 

docs/components/learnware.rst

@@ -55,7 +55,7 @@ All Reuse Methods
 ===========================
 
 In addition to applying ``Learnware``, ``FeatureAlignLearnware`` or ``HeteroMapAlignLearnware`` objects directly by calling their ``predict`` interface, 
-the ``Learnware`` package also provides a set of ``Reuse Methods`` for users to further customize a single or multiple learnwares, with the hope of enabling learnwares to be 
+the ``learnware`` package also provides a set of ``Reuse Methods`` for users to further customize a single or multiple learnwares, with the hope of enabling learnwares to be 
 helpful beyond their original purposes, and eliminating the need for users to build models from scratch.
 
 There are two main categories of ``Reuse Methods``: (1) direct reuse and (2) reuse based on a small amount of labeled data.
@@ -107,7 +107,7 @@ specifies the ensemble method(default is set to ``mean``).
 Reuse Learnware with Labeled Data
 ----------------------------------
 
-When users have a small amount of labeled data available, ``Learnware`` package provides two methods: ``EnsemblePruningReuser`` and ``FeatureAugmentReuser`` to help reuse learnwares.
+When users have a small amount of labeled data available, the ``learnware`` package provides two methods: ``EnsemblePruningReuser`` and ``FeatureAugmentReuser`` to help reuse learnwares.
 They are both initialized with a list of ``Learnware`` objects ``learnware_list``, and have different implementations of ``fit`` and ``predict`` methods.
 
 EnsemblePruningReuser

docs/components/market.rst

@@ -48,7 +48,7 @@ This ``checker`` checks the statistical specification and functionality of a lea
 Current Markets
 ======================================
 
-The ``Learnware`` package provide two different implementation of ``market``, i.e. ``Easy Market`` and ``Hetero Market``. They have different implementation of ``organizer`` and ``searcher``.
+The ``learnware`` package provide two different implementation of ``market``, i.e. ``Easy Market`` and ``Hetero Market``. They have different implementation of ``organizer`` and ``searcher``.
 
 Easy Market
 -------------

docs/components/spec.rst

@@ -5,7 +5,7 @@ Specification
 
 Learnware specification is the core component of the learnware paradigm, linking all processes about learnwares, including uploading, organizing, searching, deploying and reusing. 
 
-In this section, we will introduce the concept and design of learnware specification in the ``Learnware`` package.
+In this section, we will introduce the concept and design of learnware specification in the ``learnware`` package.
 We will then explore ``regular specification``\ s tailored for different data types such as tables, images and texts.
 Lastly, we cover a ``system specification`` specifically assigned to table learnwares by the learnware market, aimed at accommodating all available table learnwares into a unified "specification world" despite their heterogeneity.
 
@@ -13,7 +13,7 @@ Concepts & Types
 ==================
 
 The learnware specification describes the model's specialty and utility in a certain format, allowing the model to be identified and reused by future users who may have no prior knowledge of the learnware.
-The ``Learnware`` package employs a highly extensible specification design, which consists of two parts:
+The ``learnware`` package employs a highly extensible specification design, which consists of two parts:
 
 - **Semantic specification** describes the model's type and functionality through a set of descriptions and tags. Learnwares with similar semantic specifications reside in the same specification island
 - **Statistical specification** characterizes the statistical information contained in the model using various machine learning techniques. It plays a crucial role in locating the appropriate place for the model within the specification island.
@@ -28,7 +28,7 @@ We employ the ``Reduced Kernel Mean Embedding (RKME) Specification`` as the foun
 with adjustments made according to the characteristics of each data type. 
 The RKME specification is a recent development in learnware specification design, which represents the distribution of a model's training data in a privacy-preserving manner.
 
-Within the ``Learnware`` package, you'll find two types of statistical specifications: ``regular specification`` and ``system specification``. The former is generated locally
+Within the ``learnware`` package, you'll find two types of statistical specifications: ``regular specification`` and ``system specification``. The former is generated locally
 by users to express their model's statistical information, while the latter is assigned by the learnware market to accommodate and organize heterogeneous learnwares. 
 
 Semantic Specification
@@ -44,7 +44,7 @@ In the case of table learnwares, users should additionally provide descriptions
 Regular Specification
 ======================================
 
-The ``Learnware`` package provides a unified interface, ``generate_stat_spec``, for generating ``regular specification``\ s across different data types. 
+The ``learnware`` package provides a unified interface, ``generate_stat_spec``, for generating ``regular specification``\ s across different data types. 
 Users can use the training data ``train_x`` (supported types include numpy.ndarray, pandas.DataFrame, and torch.Tensor) as input to generate the ``regular specification`` of the model,
 as shown in the following code:
 
@@ -134,7 +134,7 @@ with particular learnware market implementations.
 - Learnware searchers perform helpful learnware recommendations among all table learnwares in the market, leveraging the ``system specification``\ s generated for users.
 
 
-``Learnware`` package now includes a type of ``system specification``, named ``HeteroMapTableSpecification``, made especially for the ``Hetero Market`` implementation.
+The ``learnware`` package now includes a type of ``system specification``, named ``HeteroMapTableSpecification``, made especially for the ``Hetero Market`` implementation.
 This specification is automatically given to all table learnwares when they are added to the ``Hetero Market``.
 It is also set up to be updated periodically, ensuring it remains accurate as the learnware market evolves and builds more precise specification worlds.
 Please refer to `COMPONENTS: Hetero Market  <../components/market.html#hetero-market>`_ for implementation details.

docs/start/install.rst

@@ -4,11 +4,11 @@ Installation Guide
 ========================
 
 
-``Learnware`` Package Installation
+``learnware`` Package Installation
 ===================================
 .. note::
 
-   ``Learnware`` package supports `Windows`, `Linux`. It's recommended to use ``Learnware`` in `Linux`. ``Learnware`` supports Python3, which is up to Python3.11.
+   The ``learnware`` package supports `Windows`, `Linux`. It's recommended to use ``Learnware`` in `Linux`. ``Learnware`` supports Python3, which is up to Python3.11.
 
 Users can easily install ``Learnware`` by pip according to the following command:
 
@@ -16,7 +16,7 @@ Users can easily install ``Learnware`` by pip according to the following command
 
     pip install learnware
 
-In the ``Learnware`` package, besides the base classes, many core functionalities such as "learnware specification generation" and "learnware deployment" rely on the ``torch`` library. Users have the option to manually install ``torch``, or they can directly use the following command to install the ``learnware`` package:
+In the ``learnware`` package, besides the base classes, many core functionalities such as "learnware specification generation" and "learnware deployment" rely on the ``torch`` library. Users have the option to manually install ``torch``, or they can directly use the following command to install the ``learnware`` package:
 
 .. code-block:: bash
 
@@ -26,7 +26,7 @@ In the ``Learnware`` package, besides the base classes, many core functionalitie
     However, it's crucial to note that due to the potential complexity of the user's local environment, installing ``learnware[full]`` does not guarantee that ``torch`` will successfully invoke ``CUDA`` in the user's local setting.
 
 
-Install ``Learnware`` Package From Source
+Install ``learnware`` Package From Source
 ==========================================
 
 Also, Users can install ``Learnware`` by the source code according to the following steps:

docs/start/quick.rst

@@ -20,7 +20,7 @@ Learnware is currently hosted on `PyPI <https://pypi.org/>`_. You can easily int
 
     pip install learnware
 
-In the ``Learnware`` package, besides the base classes, many core functionalities such as "learnware specification generation" and "learnware deployment" rely on the ``torch`` library. Users have the option to manually install ``torch``, or they can directly use the following command to install the ``learnware`` package:
+In the ``learnware`` package, besides the base classes, many core functionalities such as "learnware specification generation" and "learnware deployment" rely on the ``torch`` library. Users have the option to manually install ``torch``, or they can directly use the following command to install the ``learnware`` package:
 
 .. code-block:: bash
 
@@ -32,7 +32,7 @@ In the ``Learnware`` package, besides the base classes, many core functionalitie
 Prepare Learnware
 ====================
 
-In learnware ``Learnware`` package, each learnware is encapsulated in a ``zip`` package, which should contain at least the following four files:
+In learnware ``learnware`` package, each learnware is encapsulated in a ``zip`` package, which should contain at least the following four files:
 
 - ``learnware.yaml``: learnware configuration file.
 - ``__init__.py``: methods for using the model.

docs/workflows/reuse.rst

@@ -135,7 +135,7 @@ combine ``HeteroMapAlignLearnware`` with the homogeneous reuse methods ``Averagi
 Reuse with ``Model Container``
 ================================
 
-``Learnware`` package provides ``Model Container`` to build executive environment for learnwares according to their runtime dependent files. The learnware's model will be executed in the containers and its env will be installed and uninstalled automatically.
+The ``learnware`` package provides ``Model Container`` to build executive environment for learnwares according to their runtime dependent files. The learnware's model will be executed in the containers and its env will be installed and uninstalled automatically.
 
 Run the following codes to try run a learnware with ``Model Container``:
 

docs/workflows/search.rst

@@ -51,7 +51,7 @@ Hetero Search
 
 For table-based user tasks, 
 homogeneous searchers like ``EasySearcher`` fail to recommend learnwares when no table learnware matches the user task's feature dimension, returning empty results.
-To enhance functionality, ``Learnware`` package includes the heterogeneous learnware search feature, whose processions is as follows: 
+To enhance functionality, the ``learnware`` package includes the heterogeneous learnware search feature, whose processions is as follows: 
 
 - Learnware markets such as ``Hetero Market`` integrate different specification islands into a unified "specification world" by assigning system-level specifications to all learnwares. This allows heterogeneous searchers like ``HeteroSearcher`` to find helpful learnwares from all available table learnwares.
 - Searchers assign system-level specifications to users based on ``UserInfo``'s statistical specification, using methods provided by corresponding organizers. In ``Hetero Market``, for example, ``HeteroOrganizer.generate_hetero_map_spec`` generates system-level specifications for users.

docs/workflows/upload.rst

@@ -83,7 +83,7 @@ Please note that module imports between Python files within the zip package shou
 Learnware Statistical Specification ``stat.json``
 ---------------------------------------------------
 
-A learnware consists of a model and a specification. Therefore, after preparing the model, you need to generate a statistical specification for it. Specifically, using the previously installed ``Learnware`` package, you can use the training data ``train_x`` (supported types include numpy.ndarray, pandas.DataFrame, and torch.Tensor) as input to generate the statistical specification of the model.
+A learnware consists of a model and a specification. Therefore, after preparing the model, you need to generate a statistical specification for it. Specifically, using the previously installed ``learnware`` package, you can use the training data ``train_x`` (supported types include numpy.ndarray, pandas.DataFrame, and torch.Tensor) as input to generate the statistical specification of the model.
 
 Here is an example of the code: