Vector embeddings: Difference between revisions

Vector embeddings (view source)

82 bytes added , 8 April 2023

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 A basic set of vector embeddings (limited to 5 dimensions) for the objects and the search term might appear as follows:
-Word Vector embedding
+{| class="wikitable"
-kitty [1.5, -0.4, 7.2, 19.6, 20.2]
+! Word !! Vector Embedding
-puppy [1.7, -0.3, 6.9, 19.1, 21.1]
+|-
-orange [-5.2, 3.1, 0.2, 8.1, 3.5]
+| kitty || [1.5, -0.4, 7.2, 19.6, 20.2]
-blueberry [-4.9, 3.6, 0.9, 7.8, 3.6]
+|-
-strcuture [60.1, -60.3, 10, -12.3, 9.2]
+| puppy || [1.7, -0.3, 6.9, 19.1, 21.1]
-motorbike [81.6, -72.1, 16, -20.2, 102]
+|-
-Q: fruit [-5.1, 2.9, 0.8, 7.9, 3.1]
+| orange || [-5.2, 3.1, 0.2, 8.1, 3.5]
+|-
+| blueberry || [-4.9, 3.6, 0.9, 7.8, 3.6]
+|-
+| strcuture || [60.1, -60.3, 10, -12.3, 9.2]
+|-
+| motorbike || [81.6, -72.1, 16, -20.2, 102]
+|-
+| fruit || [-5.1, 2.9, 0.8, 7.9, 3.1]
+|}
 Upon examining each of the 5 components of the vectors, it's evident that kitty and puppy are much closer than puppy and orange (we don't even have to determine the distances). Similarly, fruit is significantly closer to orange and blueberry compared to the other words, making them the top results for the "fruit" search.