Update README.md

README.md

@@ -52,17 +52,17 @@ pipeline_tag: question-answering
 
 This is a <b>DeBERTa</b> <b>[1]</b> model for the <b>Italian</b> language, fine-tuned for <b>Extractive Question Answering</b> on the [SQuAD-IT](https://huggingface.co/datasets/squad_it) dataset <b>[2]</b>.
 
+<h3>Training and Performances</h3>
+
+The model is trained to perform question answering, given a context and a question (under the assumption that the context contains the answer to the question). It has been fine-tuned for Extractive Question Answering, using the SQuAD-IT dataset, for 2 epochs with a linearly decaying learning rate starting from 3e-5, maximum sequence length of 384 and document stride of 128.
+<br>The dataset includes 54.159 training instances and 7.609 test instances
+
 <b>update: version 2.0</b>
 
 The 2.0 version further improves the performances by exploiting a 2-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)
 
 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
 
-<h3>Training and Performances</h3>
-
-The model is trained to perform question answering, given a context and a question (under the assumption that the context contains the answer to the question). It has been fine-tuned for Extractive Question Answering, using the SQuAD-IT dataset, for 2 epochs with a linearly decaying learning rate starting from 3e-5, maximum sequence length of 384 and document stride of 128.
-<br>The dataset includes 54.159 training instances and 7.609 test instances
-
 The performances on the test set are reported in the following table:
 
 (<b>version 2.0</b> performances)