Is BERT actually good for semantic similarity.. check the example

As per score below , second case is more semantic similar than other one. But in actual it is just the opposite.

>>> model.predict([("he is an indian", "he has indian citizenship")])
array([3.2054904], dtype=float32)
>>> model.predict([("he is an indian", "he is  not an indian")])
array([3.590286], dtype=float32)

[ghost]

Hi, The implementation may be correct but does it worth if results are wrong. I belive bert embedding is not made for this purpose. Why to put a code in public which will not give intended results as the name suggests or the Readme page promises. Please correct me if I am wrong on BERT usage for this purpose

On Wed, 24 Jul, 2019, 3:48 AM Andriy Mulyar, [e-mail] wrote:

The model implemented is identical to the model in the BERT paper reporting .89 annotator correlation.

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[timpal0l]

@sabirdvd What would be a better approach than using the sentence representation from bert for Semantic Similarity…?