Transformers Enable Contextual Word Embeddings

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🤯 Did You Know (click to read)

Models like BERT and GPT generate different embeddings for the same word depending on its sentence context.

Unlike static embeddings such as Word2Vec or GloVe, Transformer-based embeddings vary with context. Each token representation integrates information from the entire input sequence, capturing polysemy and semantic nuances.

💥 Impact (click to read)

Contextual embeddings improve downstream NLP tasks like sentiment analysis, question answering, and named entity recognition.

For researchers, contextual embeddings allow more precise semantic understanding and better performance across multiple NLP benchmarks.

Source

Devlin et al., 2018 - BERT

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