In this repository, I have collected different sources, visualizations and code examples of BERT. I had been doing this during learning material of BERT. Perhaps, I should call this repository like "how-I-understand-bert" 🤔
So, I have started my long way in BERT with blog posts and videos produced by Chris McCormick. After watching the first video, I clearly understood that it is necessary to read more about Attention and Transformer.
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Attention
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[en] Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) ❤️
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[en] Attention is all you need - original paper
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[en] Attention is all you need - short video about this paper
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[en] Attention is all you need - short video about this paper with excellent explanation ❤️
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[ru] Attention, attention! - seminar organized by JetBrains Research
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Transformer
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[en] Awesome illustrations was created by Jay Alammar ❤️
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[en] The Narrated Transformer Language Model - explanation by Jay Alammar ❤️
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[en] Transformer (Attention is all you need) - short video with great visualizations
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[en] Just a short recap video
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also, in transformer folder you can find different implementations of this architecture ❤️🔥
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Next, let's talk about tokenization in context of BERT.
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[en] WordPiece Embeddings - II part of BERT Research by Chris McCormick ❤️
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[en] Tokenizers: How machines read ❤️
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also, in vocabulary folder you can find different examples ❤️🔥
And finally, let's read more about BERT.
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[en] The Illustrated BERT blog post ❤️
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[en] Google AI Blog post about open sourcing BERT
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[en] One more blog post explaining BERT
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[en] BERT Research - Fine Tuning - Chris McCormick tutorial ❤️
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[en] BERT classifier fine-tuning with PyTorch, HuggingFace, and Catalyst - Yury Kashnitsky tutorial ❤️
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in sequence-classification folder you can find different examples ❤️🔥
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in document-classification folder you can find different examples ❤️🔥
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in bert-finetuning-catalyst folder you can find great example based on Yury Kashnitsky tutorial ❤️🔥
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[en] Check this repository if you wanna more papers
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[en] DistillBERT overview (distillation will be covered later in our course) blog post
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[en] OpenAI blog post Better Language Models and Their Implications (GPT-2)
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[en] Great PyTorch library: pytorch-transformers
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[en] OpenAI API request