Update README.md

README.md

@@ -7,6 +7,9 @@ datasets:
 model-index:
 - name: DeBERTa-finetuned-SNLI2
   results: []
+metrics:
+- accuracy
+library_name: transformers
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -16,9 +19,23 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [gyeoldere/test_trainer](https://huggingface.co/gyeoldere/test_trainer) on the snli dataset.
 
+Test_trainer model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the snli dataset.
+
+This model achieves the following results on the evaluation set:
+- NLI accuracy : 0.86
+- MLM accuracy : 0.68
+
+
 ## Model description
 
-More information needed
+This model fine-tuned to perform 2 tasks simultaneously; NLI task and MLM task.
+
+Output vector of DeBERTa processed through two different fc layer to predict.
+I used layer structure introduced in BERT paper, which is implemented on huggingface transformers; DebertaForTokenClassification and DebertaForMaskedLM.
+[https://huggingface.co/docs/transformers/index]
+
+BinaryCrossEntrophyLoss are used for each class, and two losses are added to obtain final loss
+final_loss = MLM_loss + NLI_loss
 
 ## Intended uses & limitations
 
@@ -50,4 +67,4 @@ The following hyperparameters were used during training:
 - Transformers 4.26.0
 - Pytorch 1.13.1+cu116
 - Datasets 2.9.0
-- Tokenizers 0.13.2
+- Tokenizers 0.13.2