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Transformer Network in Pytorch from scratch
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Step by step implementation of “Attention is all you need” with animated explanations.
This is a supplementary post to the medium article Transformers in Cheminformatics.
Named entity recognition with simple Attention
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NER implementation hosted within browser using Tensorflow-JS.
Definition from Wikipedia
Named Entity Recognition is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, etc. See demo below. Continue reading for model explanation and code.
Encoder - Attention - Decoder
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Explaining Attention Network in Encoder-Decoder setting using Recurrent Neural Networks
Encoder-Decoder paradigm has become extremely popular in deep learning particularly in the space of natural language processing. Attention modules complement encoder-decoder architecture to make learning more close to humans way. I present a gentle introduction to encode-attend-decode. I provide motivation for each block and explain the math governing the model. Further, I break down the code into digestible bits for each mathematical equation. While there are good explanations to attention mechanism for machine translation task, I will try to explain the same for a sequence tagging task (Named Entity Recognition).
Encode-Attend-Decode ArchitectureIn the next part of the series, I will use the architecture explained here to solve the problem of Named Entity Recognition
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