Toolkit Word Embeddings

Word Embeddings Workbench

Explore vector arithmetic, semantic relationships, and bias in word embeddings.

Word Vector Arithmetic

Enter words to explore semantic relationships through vector operations. E.g., King − Man + Woman ≈ Queen

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How It Works

1

Vector Representation

Each word is mapped to a high-dimensional vector that captures its semantic meaning.

2

Arithmetic Operations

Subtracting and adding vectors reveals semantic relationships (e.g., royalty, gender).

3

Nearest Neighbor Search

We find words whose vectors are closest (by cosine similarity) to the result vector.

Cosine Similarity

Try These Examples

Loading GloVe Embeddings

Real Pre-trained Vectors • Four model sizes

LOADING

Loading GloVe 6B pre-trained word vectors (100d, ~20,000 words). All computation is client-side. No data is sent to any server.

Model
GloVe 6B
Dimension
100d
Vocabulary
~20,000
File Size
~7.8 MB
Loading Progress 0%