From 5116c89c2d6d132c774200d0ec6f0c3da2cbf82b Mon Sep 17 00:00:00 2001
From: Tony-HYX <605698554@qq.com>
Date: Sat, 23 Dec 2023 22:20:20 +0800
Subject: [PATCH] [DOC] minor changes

---
 docs/Intro/Basics.rst   | 2 +-
 docs/Intro/Learning.rst | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/Intro/Basics.rst b/docs/Intro/Basics.rst
index 492e854..a9d3076 100644
--- a/docs/Intro/Basics.rst
+++ b/docs/Intro/Basics.rst
@@ -22,7 +22,7 @@ AI: data, models, and knowledge. Below is an overview of the ABL-Package.
 
 **Data** part manages the storage, operation, and evaluation of data efficiently.
 It includes the ``ListData`` class, which defines the data structures used in
-Abductive Learning, and comprises common data operations like insertion, deletion, 
+ABL, and comprises common data operations like insertion, deletion, 
 retrieval, slicing, etc. Additionally, it contains a series of evaluation metrics 
 such as ``SymbolAccuracy`` and ``ReasoningMetric`` (both specialized metrics 
 inherited from the ``BaseMetric`` class), for evaluating model quality from a 
diff --git a/docs/Intro/Learning.rst b/docs/Intro/Learning.rst
index 6e7e001..5dae929 100644
--- a/docs/Intro/Learning.rst
+++ b/docs/Intro/Learning.rst
@@ -15,7 +15,7 @@ In this section, we will look at how to build the learning part.
 In ABL-Package, building the learning part involves two steps:
 
 1. Build a machine learning base model used to make predictions on instance-level data.
-2. Instantiate an ``ABLModel`` with the base model, which enables the learning part to train, test, and predict on example-level data.
+2. Instantiate an ``ABLModel`` with the base model, which enables the learning part to process example-level data.
 
 .. code:: python