ragarwal
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deberta-v3-base-nli-mixer-binary

Text Classification

text-embeddings-inference

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ragarwal commited on May 20, 2022

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@@ -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).
 ### 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.

 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-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.