hey @Roman, the reason why you don’t get the same embedding for the same word in a sequence is because the transformers like BERT produce context-sensitive representations, i.e. they depend on the context in which they appear in a sequence.
the advantage of such representations is that you can deal with tricky examples like “Time flies like an arrow; fruit flies like a banana”, where the word “flies” has two very different meanings 
you can find a nice description of contextual embeddings here: The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar – Visualizing machine learning one concept at a time. 1