Tomas Mikolov and Google colleagues released Word2Vec, popularizing dense embeddings and king–man+woman≈queen analogies.
In 2013, Tomas Mikolov’s team at Google published Word2Vec (skip-gram and CBOW), learning dense word vectors at large scale with notable efficiency.
Vector analogies captured public imagination and became an NLP building block. GloVe and FastText expanded the ecosystem.
Though contextual LLMs replaced static embeddings, Word2Vec taught a generation to think of meaning as geometry.
Devlin et al. released BERT, popularizing language-model fine-tuning via masked LM and next sentence prediction.
EmbeddingGemma 2, built on Gemma 4 and released under the Apache 2.0 license, unifies text, code, images, video and audio in a single 768-dimension vector space, quadruples context to 8,192 tokens and gains 9.92 points on MTEB Code, with modular setups starting at 270 million parameters.
IBM’s statistical machine-translation models based on alignments marked NLP’s data-driven turn.