How can i use bert for finding word embeddings
Hi all,
Can I use pre-trained BERT for finding fixed sized word embeddings, like 300D Glove or Word2vec word embeddings?. 
  
I extract features like examples in extarct_features.py. But went I used these features(the last encoded_layers) as word embeddings, I got a worse result than using 300D Glove. I also used these features to compute the cos similarity for each word in  sentences, I found that all values were around 0.6<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>Hi. I really need someone's help regarding using the bert model to extract the word embeddings: This script: https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/extract_features.py doesn't exist anymore.

Any suggestions instead that replaced this script? 
