text: Title: Suggestions for building semantic search engine
  Body: Hello! I'm looking for suggestions of using BERT (and BERT-as-service) in my case. Sorry if such is off-topic here. I'm building kind of information retrieval system and trying to use BERT as semantic search engine. In my DB I have objects with descriptions like "pizza", "falafel", "Chinese restaurant", "I bake pies", "Chocolate Factory Roshen" and I want all these objects to be retrieved by a search query "food" or "I'm hungry" - with some score of semantic relatedness, of course. 
  
  First of all, does it look like semantic sentence similarity task or more like word similarity? I expect max_seq_len to be 10-15, on average up to 5. So that, should I look into fine-tuning and if yes, on what task? GLUE? Or maybe on my own data creating dataset like STS-B? Or maybe it's better to extract ELMo-like contextual word embedding and then average them?
  
  Really appreciate any suggestion. Thanks in advance!
  
  **Prerequisites**
  
  > Please fill in by replacing `[ ]` with `[x]`.
  
  * [x] Are you running the latest `bert-as-service`?
  * [x] Did you follow [the installation](https://github.com/hanxiao/bert-as-service#install) and [the usage](https://github.com/hanxiao/bert-as-service#usage) instructions in `README.md`?
  * [x] Did you check the [FAQ list in `README.md`](https://github.com/hanxiao/bert-as-service#speech_balloon-faq)?
  * [x] Did you perform [a cursory search on existing issues](https://github.com/hanxiao/bert-as-service/issues)?