Merge branch 'Dev'

README.md

@@ -20,27 +20,27 @@
 
 Key Features of ABLkit:
 
-- **Great Flexibility**: Adaptable to various machine learning modules and logical reasoning components.
-- **User-Friendly**: Provide data, model, and KB, and get started with just a few lines of code.
-- **High-Performance**: Optimization for high accuracy and fast training speed.
+- **High Flexibility**: Compatible with various machine learning modules and logical reasoning components.
+- **User-Friendly Interface**: Provide data, model, and knowledge, and get started with just a few lines of code.
+- **Optimized Performance**: Optimization for high performance and accelerated training speed.
 
 ABLkit encapsulates advanced ABL techniques, providing users with an efficient and convenient toolkit to develop dual-driven ABL systems, which leverage the power of both data and knowledge.
 
 <p align="center">
 <img src="https://raw.githubusercontent.com/AbductiveLearning/ABLkit/main/docs/_static/img/ABLkit.png" alt="ABLkit" style="width: 80%;"/>
 </p>
 
-### Installation
+## Installation
 
-#### Install from PyPI
+### Install from PyPI
 
 The easiest way to install ABLkit is using ``pip``:
 
 ```bash
 pip install ablkit
 ```
 
-#### Install from Source
+### Install from Source
 
 Alternatively, to install from source code, sequentially run following commands in your terminal/command line.
 
@@ -50,7 +50,7 @@ cd ABLkit
 pip install -v -e .
 ```
 
-#### (Optional) Install SWI-Prolog
+### (Optional) Install SWI-Prolog
 
 If the use of a [Prolog-based knowledge base](https://ablkit.readthedocs.io/en/latest/Intro/Reasoning.html#prolog) is necessary, please also install [SWI-Prolog](https://www.swi-prolog.org/):
 
@@ -62,7 +62,7 @@ sudo apt-get install swi-prolog
 
 For Windows and Mac users, please refer to the [SWI-Prolog Install Guide](https://github.com/yuce/pyswip/blob/master/INSTALL.md).
 
-### Quick Start
+## Quick Start
 
 We use the MNIST Addition task as a quick start example. In this task, pairs of MNIST handwritten images and their sums are given, alongwith a domain knowledge base which contains information on how to perform addition operations. Our objective is to input a pair of handwritten images and accurately determine their sum.
 
@@ -186,7 +186,7 @@ bridge.test(test_data)
 
 To explore detailed tutorials and information, please refer to - [document](https://ablkit.readthedocs.io/en/latest/index.html).
 
-### Examples
+## Examples
 
 We provide several examples in `examples/`. Each example is stored in a separate folder containing a README file.
 
@@ -195,7 +195,7 @@ We provide several examples in `examples/`. Each example is stored in a separate
 + [Handwritten Equation Decipherment](https://github.com/AbductiveLearning/ABLkit/tree/main/examples/hed)
 + [Zoo](https://github.com/AbductiveLearning/ABLkit/tree/main/examples/zoo)
 
-### References
+## References
 
 For more information about ABL, please refer to: [Zhou, 2019](http://scis.scichina.com/en/2019/076101.pdf) and [Zhou and Huang, 2022](https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf).
 

docs/index.rst

@@ -16,9 +16,9 @@ where both data and (logical) domain knowledge are available.
 
 Key Features of ABLkit:
 
-- **Great Flexibility**: Adaptable to various machine learning modules and logical reasoning components.
-- **User-Friendly**: Provide **data**, :blue-bold:`model`, and :green-bold:`KB`, and get started with just a few lines of code.
-- **High-Performance**: Optimization for high accuracy and fast training speed.
+- **High Flexibility**: Compatible with various machine learning modules and logical reasoning components.
+- **User-Friendly Interface**: Provide **data**, :blue-bold:`model`, and :green-bold:`knowledge`, and get started with just a few lines of code.
+- **Optimized Performance**: Optimization for high performance and accelerated training speed.
 
 ABLkit encapsulates advanced ABL techniques, providing users with
 an efficient and convenient toolkit to develop dual-driven ABL systems,