Update README.md

README.md

@@ -52,6 +52,10 @@ 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>, using <b>DeBERTa-ITALIAN</b> ([deberta-base-italian](https://huggingface.co/osiria/deberta-base-italian)) as a pre-trained model.
 
+<b>Update: Version 2.0</b>
+
+This 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 initial learning rate of 3e-5) then further fine-tuned on the Italian SQuAD (2 epochs, initial learning rate of 3e-5, but no warmup)
+
 <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.
@@ -59,9 +63,11 @@ The model is trained to perform question answering, given a context and a questi
 
 The performances on the test set are reported in the following table:
 
+(version 2.0 performances)
+
 | EM | F1 |
 | ------ | ------ |
-| 68.80 | 80.08 |
+| 70.04 | 80.97 |
 
 Testing notebook: https://huggingface.co/osiria/deberta-italian-question-answering/blob/main/osiria_deberta_italian_qa_evaluation.ipynb