So far in the series, we have accomplished several tasks: In Part 1, we prepared our dataset for BERT training. In Part 2a, we prepared fixed input embeddings for the BERT. Following that, in Part…

AI in Plain English

The article provides a step-by-step guide to building a BERT model with PyTorch. It covers topics such as residual connections, encoder blocks, and the BERT Transformer class. The purpose of a residual connection is to allow information to flow directly from the input of a layer to its output, without going through all of the intermediate computations of the layer. The Encoder stack in the Transformer architecture updates input embeddings to produce representations that encode contextual information in the sequence.

A Step-by-Step Guide to building a BERT model with PyTorch (Part 2c)

DataScience/13 - NLP/C04 - BERT (Pytorch Scratch).ipynb at main · ChanCheeKean/DataScience

A Step-by-Step Guide to Preparing Datasets for BERT implementation with PyTorch (Part 1)

A Step-by-Step Guide to building a BERT model with PyTorch (Part 2a)

A Step-by-Step Guide to building a BERT model with PyTorch (Part 2b)