Merge pull request #50512 from JnHs/jh-fgai-3typo

fix typo

Author: prmerger-automator[bot]

learn-pr/wwl-data-ai/fundamentals-generative-ai/includes/3-language-models.md

@@ -33,7 +33,7 @@ Vectors represent lines in multidimensional space, describing direction and dist
 
 :::image type="content" source="../media/word-embeddings.png" alt-text="A screenshot showing a simple example of word embeddings.":::
 
-The elements of the tokens in the embeddings space each represent some semantic attribute of the token, so that semantically similar tokens should result in vectors that have a similar orientation – in other words they point in the same direction. a technique called cosine similarity is used to determine if two vectors have similar directions (regardless of distance), and therefore represent semantically linked words. For example, the embedding vectors for "dog" and "puppy" describe a path along an almost identical direction, which is also fairly similar to the direction for "cat". The embedding vector for "skateboard" however describes journey in a very different direction.
+The elements of the tokens in the embeddings space each represent some semantic attribute of the token, so that semantically similar tokens should result in vectors that have a similar orientation – in other words they point in the same direction. A technique called cosine similarity is used to determine if two vectors have similar directions (regardless of distance), and therefore represent semantically linked words. For example, the embedding vectors for "dog" and "puppy" describe a path along an almost identical direction, which is also fairly similar to the direction for "cat". The embedding vector for "skateboard" however describes journey in a very different direction.
 
 ## Architectural developments