Merge pull request #51137 from sherzyang/main

Fix typo.

Author: prmerger-automator[bot]

learn-pr/wwl-data-ai/introduction-language/includes/4-semantic-models.md

@@ -3,7 +3,7 @@ As the state of the art for NLP has advanced, the ability to train models that e
 Vectors represent lines in multidimensional space, describing direction and distance along multiple axes. Overall, the vector describes the direction and distance of the path from origin to end. Semantically similar tokens should result in vectors that have a similar orientation – in other words they point in the same direction. As a simple example, suppose the embeddings for our tokens consist of vectors with three elements, for example:
 
 ```
-- 4 ("dog"): [10.3.2]
+- 4 ("dog"): [10,3,2]
 - 5 ("bark"): [10,2,2]
 - 8 ("cat"): [10,3,1]
 - 9 ("meow"): [10,2,1]
@@ -36,4 +36,4 @@ For example, consider the following restaurant reviews, which are already labele
 - *Slow service and substandard food*: 0
 ```
 
-With enough labeled reviews, you can train a classification model using the tokenized text as *features* and the sentiment (0 or 1) a *label*. The model will encapsulate a relationship between tokens and sentiment - for example, reviews with tokens for words like `"great"`, `"tasty"`, or `"fun"` are more likely to return a sentiment of **1** (*positive*), while reviews with words like `"terrible"`, `"slow"`, and `"substandard"` are more likely to return **0** (*negative*).
+With enough labeled reviews, you can train a classification model using the tokenized text as *features* and the sentiment (0 or 1) a *label*. The model will encapsulate a relationship between tokens and sentiment - for example, reviews with tokens for words like `"great"`, `"tasty"`, or `"fun"` are more likely to return a sentiment of **1** (*positive*), while reviews with words like `"terrible"`, `"slow"`, and `"substandard"` are more likely to return **0** (*negative*).