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1. huggingface.co › docs › transformersBERT - Hugging Face

   huggingface.co › docs › transformers

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   We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a ...

2. en.wikipedia.org › wiki › BERT_(language_model)BERT (language model) - Wikipedia

   en.wikipedia.org › wiki › BERT_(language_model)

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   Bidirectional Encoder Representations from Transformers (BERT) is a language model based on the transformer architecture, notable for its dramatic improvement over previous state of the art models. It was introduced in October 2018 by researchers at Google.

3. arxiv.org › abs › 1810BERT: Pre-training of Deep Bidirectional Transformers for...

   arxiv.org › abs › 1810

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   11. Okt. 2018 · BERT is a deep bidirectional transformer that pre-trains on unlabeled text and fine-tunes for various natural language processing tasks. It achieves state-of-the-art results on eleven tasks, such as question answering and language inference.

   • Autor: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova

   • Publish Year: 2018

4. towardsdatascience.com › keeping-up-with-the-berts-5b7beb92766BERT Explained: What it is and how does it work? | Towards Data...

   towardsdatascience.com › keeping-up-with-the-berts-5b7beb92766

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   BERT is a powerful NLP model by Google that uses bidirectional pre-training and fine-tuning for various tasks. Learn about its architecture, pre-training tasks, inputs, outputs and applications in this article.

5. huggingface.co › blog › bert-101BERT 101 State Of The Art NLP Model Explained - Hugging Face

   huggingface.co › blog › bert-101

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   2. März 2022 · Learn what BERT is, how it works, and why it's a game-changer for natural language processing. BERT is a bidirectional transformer model that can perform 11+ common language tasks, such as sentiment analysis and question answering.

   • Yes! Our experts at Hugging Face have open-sourced the PyTorch transformers repository on GitHub . Pro Tip: Lewis Tunstall, Leandro von...
   • Yes! You can use Tensorflow as the backend of Transformers.
   • The 2 original BERT models were trained on 4(BERTbase) and 16(BERTlarge) Cloud TPUs for 4 days.
   • For common NLP tasks discussed above, BERT takes between 1-25mins on a single Cloud TPU or between 1-130mins on a single GPU.
   • BERT was one of the first models in NLP that was trained in a two-step way: BERT was trained on massive amounts of unlabeled data (no hum...

6. github.com › google-research › bertGitHub - google-research/bert: TensorFlow code and pre-trained...

   github.com › google-research › bert

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   BERT is a pre-trained language representation model that can be fine-tuned for various natural language tasks. This repository contains the official TensorFlow implementation of BERT, as well as pre-trained models, tutorials, and research papers.

7. www.geeksforgeeks.org › explanation-of-bert-model-nlpExplanation of BERT Model - NLP - GeeksforGeeks

   www.geeksforgeeks.org › explanation-of-bert-model-nlp

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   10. Jan. 2024 · BERT is a transformer-based framework for natural language processing that uses bidirectional context and pre-training on large data. Learn how BERT works, its training strategies, and its applications in various NLP tasks.