BERT (language model)
Bidirectional Encoder Representations from Transformers (BERT) is a transformer-based machine learning technique for natural language processing (NLP) pre-training developed by Google. BERT was created and published in 2018 by Jacob Devlin and his colleagues from Google. As of 2019, Google has been leveraging BERT to better understand user searches.
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Activation functionBERT (Language model)BertCOVID-19 misinformationComputational humorELMoGPT-2GloVe (machine learning)Impact of the COVID-19 pandemic on science and technologyLanguage modelRectifier (neural networks)Search engine optimizationSpaCySpark NLPTransformer (machine learning model)Winograd schema challengeWord embedding
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BERT (language model)
Bidirectional Encoder Representations from Transformers (BERT) is a transformer-based machine learning technique for natural language processing (NLP) pre-training developed by Google. BERT was created and published in 2018 by Jacob Devlin and his colleagues from Google. As of 2019, Google has been leveraging BERT to better understand user searches.
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Bidirectional Encoder Represen ...... h Wikipedia with 2,500M words.
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62,026,514
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1,026,274,849
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Bidirectional Encoder Represen ...... tter understand user searches.
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BERT (language model)
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