LABEL_0
---
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
