Update README.md

README.md

@@ -22,10 +22,10 @@ model-index:
       type: squad_it
     metrics:
     - type: exact-match
-      value: 0.7019
+      value: 0.7004
       name: Exact Match
     - type: f1
-      value: 0.8101
+      value: 0.8097
       name: F1
 pipeline_tag: question-answering
 ---
@@ -63,11 +63,23 @@ The 2.0 version further improves the performances by exploiting a 2-phases fine-
 
 In order to maximize the benefits of the multilingual procedure, [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) is used as a pre-trained model. When the double fine-tuning is completed, the embedding layer is then compressed as in [deberta-base-italian](https://huggingface.co/osiria/deberta-base-italian) to obtain a mono-lingual model size
 
+The performances on the test set are reported in the following table:
+
+(<b>version 2.0</b> performances)
+
+| EM | F1 |
+| ------ | ------ |
+| 70.04 | 80.97 |
+
+Testing notebook: https://huggingface.co/osiria/deberta-italian-question-answering/blob/main/osiria_deberta_italian_qa_evaluation.ipynb
+
 <b>update: version 3.0 (LITEQA)</b>
 
 The 3.0 version, with the nickname LITEQA, further improves the performances by exploiting a 3-phases fine-tuning strategy: the model is first fine-tuned on the English SQuAD v2 (1 epoch, 20% warmup ratio, and max learning rate of 3e-5) then further fine-tuned on the Italian SQuAD (2 epochs, no warmup, initial learning rate of 3e-5) and lastly fine-tuned on the lowercase Italian SQuAD (1 epochs, no warmup, initial learning rate of 3e-5)
 This helps making the model generally more robust, but particularly in uncased settings.
 
+The 3.0 version can be downloaded from the <b>liteqa<b> branch of this repo
+
 The performances on the test set are reported in the following table:
 
 (<b>version 3.0</b> performances)