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Eli Bendersky: Reproducing word2vec with JAX
- The word2vec model, proposed in a 2013 paper by Google researchers, introduced embeddings - dense vector representation of words for language models.
- This article focuses on reproducing word2vec results using JAX and discusses the original C code for word2vec as well.
- Embeddings are dense vectors of floats that encode the meaning of words in a high-dimensional space.
- The word2vec architecture includes CBOW (Continuous Bag Of Words) and Continuous Skip Gram, with a focus on CBOW in this post.
- CBOW model teaches to predict a word from surrounding words using a window size.
- A JAX implementation of the word2vec model includes forward pass and loss computation.
- Training involves subsampling common words, creating a vocabulary, and training the model with a dataset through multiple epochs.
- Post-training, extracting embeddings from the model allows finding word similarities using trained weights.
- Modern LLMs train embedding matrices as part of larger models compared to word2vec for better task tuning and efficiency.
- The article provides a thorough explanation of word2vec, JAX implementation, training process, and comparison with modern text embeddings.
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