| 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: | |
| ``` | |
| 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 | |