readme: ---
  license: cc
  ---
  # MARS Encoder for Multi-agent Response Selection
  This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class and is the model used in the paper [One Agent To Rule Them All: Towards Multi-agent Conversational AI](https://csclarke.com/assets/pdf/ACL_2022.pdf).
  
  ## Training Data
  This model was trained on the [BBAI dataset](https://github.com/ChrisIsKing/black-box-multi-agent-integation/tree/main/data). The model will predict a score between 0 and 1 ranking the correctness of a response to a user question from a conversational agent. 
  
  
  ## Usage and Performance
  
  Pre-trained models can be used like this:
  ```python
  from sentence_transformers import CrossEncoder
  model = CrossEncoder('csclarke/MARS-Encoder')
  scores = model.predict([('question 1', 'response 1'), ('question 1', 'response 2')])
  ```
  
  The model will predict scores for the pairs `('question 1', 'response 1')` and `('question 1', 'response 2')`.
  
  You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
  