Is BERT actually good for semantic similarity.. check the example
See original GitHub issueAs 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)
Issue Analytics
- State:
- Created 4 years ago
- Comments:5 (2 by maintainers)
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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, notifications@github.com wrote:
@sabirdvd What would be a better approach than using the sentence representation from bert for Semantic Similarity…?