From 03130e6b40f7f81cd8c3804cab623ccdb05a8752 Mon Sep 17 00:00:00 2001
From: Jannik Wagner <jannik.wagner@web-computing.de>
Date: Wed, 8 Jan 2025 15:20:03 +0100
Subject: [PATCH] Update README.rst

- fix typo
- remove obsolete Table of Contents entries
---
 README.rst | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/README.rst b/README.rst
index 3cea35bb..1f9f9ed4 100644
--- a/README.rst
+++ b/README.rst
@@ -67,7 +67,7 @@ Welcome to PyOD, a well-developed and easy-to-use Python library for detecting a
 
 **Additional Resources**:
 
-* **NLP Anomaly Detection**: `NLP-ADBench <https://github.com/USC-FORTIS/NLP-ADBench>`_ provides both NLP anonaly detection datasets and algorithms
+* **NLP Anomaly Detection**: `NLP-ADBench <https://github.com/USC-FORTIS/NLP-ADBench>`_ provides both NLP anomaly detection datasets and algorithms
 * **Time-series Outlier Detection**: `TODS <https://github.com/datamllab/tods>`_
 * **Graph Outlier Detection**: `PyGOD <https://pygod.org/>`_
 * **Performance Comparison & Datasets**: Our 45-page `anomaly detection benchmark paper <https://openreview.net/forum?id=foA_SFQ9zo0>`_ and `ADBench <https://github.com/Minqi824/ADBench>`_, comparing 30 algorithms on 57 datasets
@@ -173,8 +173,6 @@ For a broader perspective on anomaly detection, see our NeurIPS papers `ADBench:
 * `Thresholding Outlier Scores <#thresholding-outlier-scores>`_
 * `Implemented Algorithms <#implemented-algorithms>`_
 * `Quick Start for Outlier Detection <#quick-start-for-outlier-detection>`_
-* `How to Contribute <#how-to-contribute>`_
-* `Inclusion Criteria <#inclusion-criteria>`_
 
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