Compare Phrases
Enter two words or phrases to calculate their semantic similarity and view their vector embeddings.
Note on Static Embeddings Comparison
I originally intended to provide a live comparison between Word2Vec (static) and Gemini (contextual) embeddings. However, due to the large size of Word2Vec models and the lack of lightweight public APIs suitable for edge environments like Cloudflare Workers, a live demo wasn't feasible. The comparison below is provided as a conceptual illustration instead.
Understanding Contextual Embeddings
Static Embeddings (Word2Vec/GloVe)
In static models, each word has a fixed vector representation regardless of context. The word "bank" has the same vector whether you're talking about a river bank or a financial bank.
"River bank" → [0.1, 0.5, -0.2]
"Money bank" → [0.1, 0.5, -0.2]
Vectors are identical! ❌
Contextual Embeddings (Gemini/BERT)
Contextual models generate embeddings based on the surrounding words. The vector for "bank" dynamically changes to capture the specific meaning in that sentence.
"River bank" → [0.8, -0.1, 0.3]
"Money bank" → [-0.4, 0.9, 0.1]
Vectors are distinct! ✅