Update README.md

README.md

@@ -10,7 +10,7 @@ The approach is simple:
 2. Finetune several SOTA transformers of different sizes (20m parameters to 300m parameters) on the combined data.
 3. Evaluate on challenging NLI datasets.
 
-This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. It is based on [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large). 
+This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. It is based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base). 
 
 ### Data
 20+ NLI datasets were combined to train a binary classification model. The `contradiction` and `neutral` labels were combined to form a `non-entailment` class.