Update understanding-creating-word-embeddings.md

Correct spelling of lens, lines 20 (abstract) and 29.

en/lessons/understanding-creating-word-embeddings.md

@@ -17,7 +17,7 @@ review-ticket: https://github.com/programminghistorian/ph-submissions/issues/555
 difficulty: 2
 activity: analyzing
 topics: [python, distant-reading, machine-learning]
-abstract: Word embeddings allow you to analyze the usage of different terms in a corpus of texts by capturing information about their contextual usage. Through a primarily theoretical lense, you will learn how to prepare a corpus and train a word embedding model. You will explore how word vectors work, how to interpret them, and how to answer humanities research questions using them.
+abstract: Word embeddings allow you to analyze the usage of different terms in a corpus of texts by capturing information about their contextual usage. Through a primarily theoretical lens, you will learn how to prepare a corpus and train a word embedding model. You will explore how word vectors work, how to interpret them, and how to answer humanities research questions using them.
 avatar_alt: Simple line drawing of a round goblin in a cloak and hat, tucked inside a chestnut.
 doi: 10.46430/phen0116
 ---
@@ -26,7 +26,7 @@ doi: 10.46430/phen0116
 
 ## Lesson Goals
 
-This lesson is designed to get you started with word embedding models. Through a primarily theoretical lense, you will learn how to prepare your corpus, read it into your Python session, and train a model. You will explore how word vectors work, how to interpret them, and how to perform some exploratory queries using them. We will provide some introductory code to get you started with word vectors, but the main focus will be on equipping you with fundamental knowledge and core concepts to use word embedding models for your own research. 
+This lesson is designed to get you started with word embedding models. Through a primarily theoretical lens, you will learn how to prepare your corpus, read it into your Python session, and train a model. You will explore how word vectors work, how to interpret them, and how to perform some exploratory queries using them. We will provide some introductory code to get you started with word vectors, but the main focus will be on equipping you with fundamental knowledge and core concepts to use word embedding models for your own research. 
 
 While word embeddings can be implemented in many different ways using varying algorithms, this lesson does not aim to provide an in-depth comparison of word embedding algorithms (though we may at times make reference to them). It will instead focus on the `word2vec` algorithm, which has been used in a range of digital humanities and computational social science projects.[^1]