Exploring the concept of high dimensional spaces reveals how subword tokens are positioned based on their meanings. As dimensions increase, visualizing relationships becomes challenging, yet it offers fascinating insights into model training. The proximity of similar subwords in this space indicates the effectiveness of the model, highlighting the intricate connections between language and machine learning.
