From 5a307ece824300a79aeade2cbe39459aa98a7890 Mon Sep 17 00:00:00 2001
From: Darigov Research <30328618+darigovresearch@users.noreply.github.com>
Date: Wed, 20 Jan 2021 18:28:40 +0000
Subject: [PATCH] Adds flashcards to Glossary & makes small corrections (#8949)

* fix: Makes small typo corrections & standardises glossary

* feat: Adds introduction & links to transformer flashcards

* feat: Adds attribution & adjustments requested in #8949

* feat: Adds flashcards to community.md

* refactor: Removes flashcards from glossary
---
 docs/source/community.md | 5 +++++
 docs/source/glossary.rst | 6 +++---
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/docs/source/community.md b/docs/source/community.md
index d67c20a18a..2372743a21 100644
--- a/docs/source/community.md
+++ b/docs/source/community.md
@@ -2,6 +2,11 @@
 
 This page regroups resources around 🤗 Transformers developed by the community.
 
+## Community resources:
+| Resource     |      Description      |      Author      |
+|:----------|:-------------|------:|
+| [Hugging Face Transformers Glossary Flashcards](https://www.darigovresearch.com/huggingface-transformers-glossary-flashcards) | A set of flashcards based on the [Transformers Docs Glossary](https://huggingface.co/transformers/master/glossary.html) that has been put into a form which can be easily learnt/revised using [Anki ](https://apps.ankiweb.net/) an open source, cross platform app specifically designed for long term knowledge retention. See this [Introductory video on how to use the flashcards](https://www.youtube.com/watch?v=Dji_h7PILrw). | [Darigov Research](https://www.darigovresearch.com/) |
+
 ## Community notebooks:
 
 | Notebook     |      Description      |      Author      |      |
diff --git a/docs/source/glossary.rst b/docs/source/glossary.rst
index 2e8e43e563..8c52a67d5c 100644
--- a/docs/source/glossary.rst
+++ b/docs/source/glossary.rst
@@ -24,11 +24,11 @@ General terms
 - MLM: masked language modeling, a pretraining task where the model sees a corrupted version of the texts, usually done
   by masking some tokens randomly, and has to predict the original text.
 - multimodal: a task that combines texts with another kind of inputs (for instance images).
-- NLG: natural language generation, all tasks related to generating text ( for instance talk with transformers,
-  translation)
+- NLG: natural language generation, all tasks related to generating text (for instance talk with transformers,
+  translation).
 - NLP: natural language processing, a generic way to say "deal with texts".
 - NLU: natural language understanding, all tasks related to understanding what is in a text (for instance classifying
-  the whole text, individual words)
+  the whole text, individual words).
 - pretrained model: a model that has been pretrained on some data (for instance all of Wikipedia). Pretraining methods
   involve a self-supervised objective, which can be reading the text and trying to predict the next word (see CLM) or
   masking some words and trying to predict them (see MLM).