Thanks for you.

excuse me did you get a solution ?

excuse me did you solve it ?

excuse me did you find the solution for this problem, please

exucse me i read that the word embedding by concatenating the last four layers(word_emb_6), giving us a single word vector per token. Each vector will have a length 4 x 768 = 3,072. All other word embeddings have the 768 length vectors per token. I'm confused about sub-words and words embedding in BERT

thanks a lot for replying. my specific task if i need to represent the embedding layer for image captioning task i need to represent the vectors for each word in the sentence so if you please do you see that the second code is suitable for this task ? i updated my question too with my result

thank a lot for answering but excuse me do you mean that i need to loop in words not sentences right ? i mean here in this line for w in words: instead of for i in sentences: if so how can i get words in the sentences , does tokenization return them ?

thanks for replying to the post . i clarified my problem, please see it . Thanks