Translations:BERT Pre-training of Deep Bidirectional Transformers/14/en: Difference between revisions

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    Input representation combines token embeddings, segment embeddings (indicating sentence A or B), and positional embeddings. BERT uses WordPiece tokenization with a 30,000-token vocabulary.
    Input representation combines token {{Term|embedding|embeddings}}, segment {{Term|embedding|embeddings}} (indicating sentence A or B), and positional {{Term|embedding|embeddings}}. BERT uses WordPiece {{Term|tokenization}} with a 30,000-token vocabulary.

    Latest revision as of 21:37, 27 April 2026

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    Message definition (BERT Pre-training of Deep Bidirectional Transformers)
    Input representation combines token {{Term|embedding|embeddings}}, segment {{Term|embedding|embeddings}} (indicating sentence A or B), and positional {{Term|embedding|embeddings}}. BERT uses WordPiece {{Term|tokenization}} with a 30,000-token vocabulary.

    Input representation combines token embeddings, segment embeddings (indicating sentence A or B), and positional embeddings. BERT uses WordPiece tokenization with a 30,000-token vocabulary.

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