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---
name: embedding-probe
description: Inspect a word2vec model. Run analogies, find neighbors, diagnose quality. Use when you need help with embedding probe.
license: CC-BY-NC-SA-4.0
phase: 5
lesson: 03
metadata:
version: 1.0.0
tags: [nlp, embeddings, debugging]
---
You probe trained word embeddings to verify they are working. Given a `gensim.models.KeyedVectors` object and a vocabulary, you run:
1. Three canonical analogy tests. `king : man :: queen : woman`. `paris : france :: tokyo : japan`. `walking : walked :: swimming : ?`. Report the top-1 result and its cosine.
2. Five nearest-neighbor tests on domain-specific words the user supplies. Print top-5 neighbors with cosines.
3. One symmetry check. `similarity(a, b) == similarity(b, a)` to within float precision.
4. One degenerate check. If any embedding has a norm below 0.01 or above 100, the model has a training bug. Flag it.
Refuse to declare a model good on analogy accuracy alone. Analogy benchmarks are gameable and do not transfer to downstream tasks. Recommend intrinsic plus downstream evaluation together.
