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[update], Google has been leveraging BERT to better understand user searches.
The original English-language BERT has two models: (1) the BERTBASE: 12 Encoders with 12 bidirectional self-attention heads, and (2) the BERTLARGE: 24 Encoders with 24 bidirectional self-attention heads. Both models are pre-trained from unlabeled data extracted from the BooksCorpus with 800M words and English Wikipedia with 2,500M words.
- GLUE (General Language Understanding Evaluation) task set (consisting of 9 tasks)
- SQuAD (Stanford Question Answering Dataset) v1.1 and v2.0
- SWAG (Situations With Adversarial Generations)
The reasons for BERT's state-of-the-art performance on these natural language understanding tasks are not yet well understood. Current research has focused on investigating the relationship behind BERT's output as a result of carefully chosen input sequences, analysis of internal vector representations through probing classifiers, and the relationships represented by attention weights.
BERT has its origins from pre-training contextual representations including Semi-supervised Sequence Learning, Generative Pre-Training, ELMo, and ULMFit. Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary, where BERT takes into account the context for each occurrence of a given word. For instance, whereas the vector for "running" will have the same word2vec vector representation for both of its occurrences in the sentences "He is running a company" and "He is running a marathon", BERT will provide a contextualized embedding that will be different according to the sentence.
On October 25, 2019, Google Search announced that they had started applying BERT models for English language search queries within the US. On December 9, 2019, it was reported that BERT had been adopted by Google Search for over 70 languages. In October 2020, almost every single English-based query was processed by BERT.
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- "Understanding searches better than ever before". Google. 2019-10-25. Retrieved 2019-11-27.
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- Nayak, Pandu (25 October 2019). "Understanding searches better than ever before". Google Blog. Retrieved 10 December 2019.
- Montti, Roger (10 December 2019). "Google's BERT Rolls Out Worldwide". Search Engine Journal. Search Engine Journal. Retrieved 10 December 2019.
- "Google: BERT now used on almost every English query". Search Engine Land. 2020-10-15. Retrieved 2020-11-24.
- "Best Paper Awards". NAACL. 2019. Retrieved Mar 28, 2020.
- Rogers, Anna; Kovaleva, Olga; Rumshisky, Anna (2020). "A Primer in BERTology: What we know about how BERT works". arXiv:2002.12327.