Search Query Quality Classifier

We build on the work by Manaal Faruqui and Dipanjan Das from Google AI Language team to train a search query classifier of well-formed search queries. Our model offers a 10% improvement over Google’s classifier by utilising ALBERT architecture instead of LSTM. With accuracy of 80%, the model is production ready and has already been deployed in Dejan AI’s query processing pipeline. The role of the model is to help identify query expansion candidates by flagging ambiguous queries retrieved via Google Search Console API.

Model Files

Model can be downloaded as a zip file.

Archive:  model_query_quality_classifier.zip
  Length      Date    Time    Name
---------  ---------- -----   ----
      792  08-31-2024 03:48   model/config.json
 46743912  08-31-2024 03:48   model/model.safetensors
      301  08-31-2024 03:48   model/special_tokens_map.json
   760289  08-31-2024 03:48   model/spiece.model
     1304  08-31-2024 03:48   model/tokenizer_config.json
---------                     -------
 47506598                     5 files

Training Data

For training we use Google’s training dataset and partially data provided by Owayo.

Model Demo

You can see the model in action by trying natural question versus keyword-based queries.