From e346d5d1c31e4c50bf402e115348ec1d287e6cf9 Mon Sep 17 00:00:00 2001
From: rstojnic <rstojnic@users.noreply.github.com>
Date: Fri, 3 Jul 2020 10:52:27 +0100
Subject: [PATCH] Update README.md

Fix spacing issues
---
 templates/README.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/templates/README.md b/templates/README.md
index a926732..6695541 100644
--- a/templates/README.md
+++ b/templates/README.md
@@ -1,10 +1,10 @@
-> 📋A template README.md for code accompanying a Machine Learning paper
+> 📋 A template README.md for code accompanying a Machine Learning paper
 
 # My Paper Title
 
 This repository is the official implementation of [My Paper Title](https://arxiv.org/abs/2030.12345). 
 
-> 📋Optional: include a graphic explaining your approach/main result, bibtex entry, link to demos, blog posts and tutorials
+> 📋 Optional: include a graphic explaining your approach/main result, bibtex entry, link to demos, blog posts and tutorials
 
 ## Requirements
 
@@ -14,7 +14,7 @@ To install requirements:
 pip install -r requirements.txt
 ```
 
-> 📋Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc...
+> 📋 Describe how to set up the environment, e.g. pip/conda/docker commands, download datasets, etc...
 
 ## Training
 
@@ -24,7 +24,7 @@ To train the model(s) in the paper, run this command:
 python train.py --input-data <path_to_data> --alpha 10 --beta 20
 ```
 
-> 📋Describe how to train the models, with example commands on how to train the models in your paper, including the full training procedure and appropriate hyperparameters.
+> 📋 Describe how to train the models, with example commands on how to train the models in your paper, including the full training procedure and appropriate hyperparameters.
 
 ## Evaluation
 
@@ -34,7 +34,7 @@ To evaluate my model on ImageNet, run:
 python eval.py --model-file mymodel.pth --benchmark imagenet
 ```
 
-> 📋Describe how to evaluate the trained models on benchmarks reported in the paper, give commands that produce the results (section below).
+> 📋 Describe how to evaluate the trained models on benchmarks reported in the paper, give commands that produce the results (section below).
 
 ## Pre-trained Models
 
@@ -42,7 +42,7 @@ You can download pretrained models here:
 
 - [My awesome model](https://drive.google.com/mymodel.pth) trained on ImageNet using parameters x,y,z. 
 
-> 📋Give a link to where/how the pretrained models can be downloaded and how they were trained (if applicable).  Alternatively you can have an additional column in your results table with a link to the models.
+> 📋 Give a link to where/how the pretrained models can be downloaded and how they were trained (if applicable).  Alternatively you can have an additional column in your results table with a link to the models.
 
 ## Results
 
@@ -54,9 +54,9 @@ Our model achieves the following performance on :
 | ------------------ |---------------- | -------------- |
 | My awesome model   |     85%         |      95%       |
 
-> 📋Include a table of results from your paper, and link back to the leaderboard for clarity and context. If your main result is a figure, include that figure and link to the command or notebook to reproduce it. 
+> 📋 Include a table of results from your paper, and link back to the leaderboard for clarity and context. If your main result is a figure, include that figure and link to the command or notebook to reproduce it. 
 
 
 ## Contributing
 
-> 📋Pick a licence and describe how to contribute to your code repository. 
\ No newline at end of file
+> 📋 Pick a licence and describe how to contribute to your code repository.