Why cosine similarity of BERT, ALBERT, Robert is so big, almost near 1.0?
## ❓ Questions & Help

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I tried to use bert models to do similarity comparison of words/sentences, but I found that the cosine similarities are all very high, even for very different words/sentences in meaning. Why?

Does all the vector are located in a small portion the vector-space?
BERT was not designed to produce useful word / sentence embeddings that can be used with cosine similarities. Cosine-similarity treats all dimensions equally which puts high requirements for the created embeddings.

BERT as not intended for this. See this post by Jacob Devlin:
https://github.com/UKPLab/sentence-transformers/issues/80#issuecomment-565388257

If you want to use BERT with cosine similarities, you need to fine-tune it on suitable data. You can find data, code and examples in our repository:
https://github.com/UKPLab/sentence-transformers<|||||>@nreimers I have read your paper, it's great and thanks for the answer!<|||||>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.
