From b5b92c8c4bd8ad113fe34b58c4a6b41f6b6f519b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Quentin=20Gallou=C3=A9dec?=
 <45557362+qgallouedec@users.noreply.github.com>
Date: Mon, 22 Apr 2024 14:56:06 +0200
Subject: [PATCH] Update README.md (#161)

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 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 0e4ad28b..b901f2bb 100644
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+++ b/README.md
@@ -100,13 +100,13 @@ Here are some examples of how you might use JAT in both evaluation and fine-tuni
 For further details regarding usage, consult the documentation included with individual script files.
 
 ## Dataset
-You can find the training dataset used to train the JAT model at this [Hugging Face dataset repo](https://huggingface.co/datasets/jat-project/jat-dataset). Thhe dataset contains a large selection of Reinforcement Learning, textual and multimodal tasks:
+You can find the training dataset used to train the JAT model at this [Hugging Face dataset repo](https://huggingface.co/datasets/jat-project/jat-dataset). The dataset contains a large selection of Reinforcement Learning, textual and multimodal tasks:
 
 **Reinforment Learning tasks**
 - Atari 57
 - BabyAI
-- Metaworld
-- Mujoco
+- Meta-World
+- MuJoCo
 
 **Textual tasks**
 - Wikipedia