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Converting Documents to Vectors (using BERT)?

1) I've heard the term "BERT embeddings" used a lot. Is this similar to Word2Vec or Doc2Vec embeddings used in NLP? I need to convert articles of text into one single vector as an input for my model. I have tried summing up the word embeddings scaled down to get a final vector. Is there a better way to convert documents to vectors? How about gensim's Doc2Vec?
2) Should I even be converting entire documents to vectors for classification problems like binary sentiment analysis?
3) Or should I try to use Convolutional Neural Networks on each word, like this tutorial? Is LSTM preferred over CNN for positive-negative text classification?
4) Is there any way to combine a Deep Learning model like CNN or RNN along with an ensemble of weak learning models to give a final output? (eg. I take in a few features like username, comment length, etc as inputs for my Ensemble Learning, but for the actual text I use a Neural Network)
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convert document with numbers and names into two column spreadsheet

I have a list of numbers in the format +447000000000,Name. Can someone please help me with how I can copy them all into google docs in such a way that I'll end up with two columns? One column for Number and one for Name.
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