Update README.md

README.md

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----
-license: cc
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-# 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
+---
+# 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