Add evaluation results on the squad_v2 config of squad_v2

Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\
Your model has been evaluated on the squad_v2 config of the [squad_v2](https://huggingface.co/datasets/squad_v2) dataset by

@nbroad

, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-a5d9cc45-11645552).\
Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=squad_v2).\
Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=squad_v2).

README.md

@@ -1,41 +1,77 @@
----
-license: cc-by-4.0
-widget:
-- context: "DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our paper. Please check the official repository for more implementation details and updates."
-  example_title: "DeBERTa v3 Q1"
-  text: "How is DeBERTa version 3 different than previous ones?"
-- context: "DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our paper. Please check the official repository for more implementation details and updates."
-  example_title: "DeBERTa v3 Q2"
-  text: "Where do I go to see new info about DeBERTa?"
-datasets:
-- squad_v2
-metrics:
-- f1
-- exact
-tags:
-- question-answering
-language: en
-model-index:
-- name: DeBERTa v3 xsmall squad2 
-  results:
-  - task: 
-      name: Question Answering
-      type: question-answering
-    dataset:
-      name: SQuAD2.0
-      type: question-answering
-    metrics:
-       - name: f1
-         type: f1
-         value: 81.5
-       - name: exact
-         type: exact
-         value: 78.3
----
-
-
-# DeBERTa v3 xsmall SQuAD 2.0
-
-[Microsoft reports that this model can get 84.8/82.0](https://huggingface.co/microsoft/deberta-v3-xsmall#fine-tuning-on-nlu-tasks) on f1/em on the dev set. 
-
-I got 81.5/78.3 but I only did one run and I didn't use the official squad2 evaluation script. I will do some more runs and show the results on the official script soon.
+---
+license: cc-by-4.0
+widget:
+- context: DeBERTa improves the BERT and RoBERTa models using disentangled attention
+    and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa
+    on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further
+    improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient
+    Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly
+    improves the model performance on downstream tasks. You can find more technique
+    details about the new model from our paper. Please check the official repository
+    for more implementation details and updates.
+  example_title: DeBERTa v3 Q1
+  text: How is DeBERTa version 3 different than previous ones?
+- context: DeBERTa improves the BERT and RoBERTa models using disentangled attention
+    and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa
+    on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further
+    improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient
+    Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly
+    improves the model performance on downstream tasks. You can find more technique
+    details about the new model from our paper. Please check the official repository
+    for more implementation details and updates.
+  example_title: DeBERTa v3 Q2
+  text: Where do I go to see new info about DeBERTa?
+datasets:
+- squad_v2
+metrics:
+- f1
+- exact
+tags:
+- question-answering
+language: en
+model-index:
+- name: DeBERTa v3 xsmall squad2
+  results:
+  - task:
+      name: Question Answering
+      type: question-answering
+    dataset:
+      name: SQuAD2.0
+      type: question-answering
+    metrics:
+    - name: f1
+      type: f1
+      value: 81.5
+    - name: exact
+      type: exact
+      value: 78.3
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad_v2
+      type: squad_v2
+      config: squad_v2
+      split: validation
+    metrics:
+    - name: Exact Match
+      type: exact_match
+      value: 78.5341
+      verified: true
+    - name: F1
+      type: f1
+      value: 81.6408
+      verified: true
+    - name: total
+      type: total
+      value: 11870
+      verified: true
+---
+
+
+
+# DeBERTa v3 xsmall SQuAD 2.0
+
+[Microsoft reports that this model can get 84.8/82.0](https://huggingface.co/microsoft/deberta-v3-xsmall#fine-tuning-on-nlu-tasks) on f1/em on the dev set. 
+
+I got 81.5/78.3 but I only did one run and I didn't use the official squad2 evaluation script. I will do some more runs and show the results on the official script soon.