# 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

{"license": "cc"}
transformers pytorch safetensors roberta text-classification license:cc autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #license-cc #autotrain_compatible #endpoints_compatible #region-us 

# MARS Encoder for Multi-Agent Response Selection
This model was trained using SentenceTransformers Cross-Encoder class and is the model used in the paper One Agent To Rule Them All: Towards Multi-agent Conversational AI.

## Training Data
This model was trained on the BBAI dataset. 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:


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

# MARS Encoder for Multi-Agent Response Selection
This model was trained using SentenceTransformers Cross-Encoder class and is the model used in the paper One Agent To Rule Them All: Towards Multi-agent Conversational AI. ## Training Data
This model was trained on the BBAI dataset. 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:


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
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #license-cc #autotrain_compatible #endpoints_compatible #region-us 
 # MARS Encoder for Multi-Agent Response Selection
This model was trained using SentenceTransformers Cross-Encoder class and is the model used in the paper One Agent To Rule Them All: Towards Multi-agent Conversational AI. ## Training Data
This model was trained on the BBAI dataset. 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:


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
passage: TAGS
#transformers #pytorch #safetensors #roberta #text-classification #license-cc #autotrain_compatible #endpoints_compatible #region-us 
# MARS Encoder for Multi-Agent Response Selection
This model was trained using SentenceTransformers Cross-Encoder class and is the model used in the paper One Agent To Rule Them All: Towards Multi-agent Conversational AI.## Training Data
This model was trained on the BBAI dataset. 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:


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