Michael Brenndoerfer | Data & AI, Private Equity, Technology

Michael Brenndoerfer

Skip-gram Model › Visualizing Training Dynamics and Embedding Structure

Michael BrenndoerferApril 2, 20252 min read

Language AI HandbookAll chapters

Part 4 of 67Word Embeddings

Chapter 19Skip-gram Model › Visualizing Training Dynamics and Embedding Structure

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Watching the loss curve as training progresses reveals how the model learns. The curve below shows the characteristic shape: rapid initial improvement as the model moves from random embeddings to something meaningful, followed by slower refinement as the model optimizes finer details.

Out[14]:

Visualization

Line plot of cross-entropy loss decreasing steeply at first then leveling off over 500 training epochs. — Skip-gram training loss over 500 epochs. The steep initial descent reflects rapid early learning as the model moves from random embeddings toward contextually meaningful representations. The curve levels off as the model approaches convergence, with diminishing returns from additional gradient steps. The initial loss near log(V) confirms the model starts from an uninformative uniform distribution.

After training, we can visualize the embedding space by computing cosine similarities between all pairs of words. This reveals which words the model has learned to treat as distributionally similar:

Out[15]:

Visualization

Heatmap of cosine similarity scores between word pairs, with darker blue indicating higher similarity. — Cosine similarity matrix between word embeddings learned by Skip-gram on a small cat-and-dog corpus. Semantically related words (cat/dog, cat/mouse) show higher similarity than unrelated pairs, which demonstrates that the model has learned to group words that appear in overlapping contexts. Diagonal entries are always 1.0 (self-similarity). Even on only six training sentences, meaningful semantic clusters are visible.