From 4c2bbbafe0fc6078b9f9161c5336b89da3153f48 Mon Sep 17 00:00:00 2001
From: Tony-HYX <605698554@qq.com>
Date: Sat, 6 Jan 2024 21:41:12 +0800
Subject: [PATCH] Update installation instructions in README.md

---
 README.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 812e5b8..772d1de 100644
--- a/README.md
+++ b/README.md
@@ -24,8 +24,7 @@ To learn how to use it, please refer to - [document](https://www.lamda.nju.edu.c
 
 ## Installation
 
-ABL is distributed on [PyPI](https://pypi.org/) and can be installed with ``pip``:
-
+The easiest way to install ABL-Package is using ``pip``:
 ```bash
 # (TODO)
 $ pip install abl
@@ -37,7 +36,7 @@ For testing purposes, you can install it using:
 $ pip install -i https://test.pypi.org/simple/ --extra-index-url https://mirrors.nju.edu.cn/pypi/web/simple/ abl
 ```
 
-Alternatively, to install ABL by source code, sequentially run following commands in your terminal/command line.
+Alternatively, to install by source code, sequentially run following commands in your terminal/command line.
 
 ```bash
 $ git clone https://github.com/AbductiveLearning/ABL-Package.git
@@ -70,7 +69,6 @@ We use the MNIST Addition task as a quick start example. In this task, pairs of
 
 ### Working with Data
 
-
 ABL-Package requires data in the format of `(X, gt_pseudo_label, Y)` where `X` is a list of input examples containing instances, `gt_pseudo_label` is the ground-truth label of each example in `X` and `Y` is the ground-truth reasoning result of each example in `X`. Note that `gt_pseudo_label` is only used to evaluate the machine learning model's performance but not to train it. 
 
 In the MNIST Addition task, the data loading looks like: