readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  license: cc-by-nc-sa-4.0
  ---
  
  # query2query
  
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps queries to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search over queries.
  
  Checkout this announcing blogpost for more information: https://neeva.com/blog/state-of-the-art-query2query-similarity(https://neeva.com/blog/state-of-the-art-query2query-similarity)
  
  **Note: we are releasing this under a license which prevents commercial use. If you want to use it for commercial purposes, please reach out to contact@neeva.co or rajhans@neeva.co with a brief description of what you want to use it for and we will try our best to respond very quickly.**
  <!--- Describe your model here -->
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
  
  ```
  pip install -U sentence-transformers
  ```
  
  Then you can use the model like this:
  
  ```python
  from sentence_transformers import SentenceTransformer
  queries = ["flight cost from nyc to la", "ticket prices from nyc to la"]
  
  model = SentenceTransformer('neeva/query2query')
  embeddings = model.encode(queries)
  print(embeddings)
  ```
  
  ## Training
  The model was trained for 1M steps with a batch size of 1024 at a learning rate of 2e-5 using a cosine learning rate scheduler with 10000 warmup steps.
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: DataParallel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
    (2): Normalize()
  )
  ```