The Transformers architecture was proposed as a purely attention based sequence to sequence model. Due to its ability of processing the text parallely and its advantage of solving the long sequence dependency problem of RNNs, this architecture has become very popular among researchers for performance improvements. This article discusses about a special part of the architecture, which is the Positional Embeddings.
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Natural Language Processing, or NLP, for short can be explained as the automated manipulation, or processing of language. This article will walk you through an important concept in NLP, that is Tokenization and different methods and types of tokenizers used.
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This is in continuation of the previous article and talks about various methods of tokenization
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