Word2Vec (large)

Training compute

3.9×10¹⁶ FLOP

Parameters

692M

Published

Oct 16, 2013

Word2Vec (large) is an AI model developed by Google (United States), first published in October 2013. It works in the language domain, on tasks such as semantic embedding.

Training it took an estimated 3.9×10¹⁶ FLOP of compute (estimation method: third-party estimation). The model has 692,000,000 parameters. It was trained on roughly 330B datapoints.

The reference paper has 35,201 citations. Epoch AI rates the confidence of this record as likely.

Full record

Organization: Google
Country of organization: United States
Domain: Language
Task: Semantic embedding
Training compute: 3.9×10¹⁶ FLOP
Compute estimation method: Third-party estimation
Parameters: 692,000,000
Dataset size: 330B
Training time: 24 h
Citations: 35,201
Epoch confidence: Likely