osiria
/

bert-italian-cased-question-answering

 ---
 license: apache-2.0
+language:
+- it
+datasets:
+- squad_it
+widget:
+- text: Quale libro fu scritto da Alessandro Manzoni?
+  context: Alessandro Manzoni pubblicò la prima versione de I Promessi Sposi nel 1827
+- text: In quali competizioni gareggia la Ferrari?
+  context: La Scuderia Ferrari è una squadra corse italiana di Formula 1 con sede a Maranello
+- text: Quale sport è riferito alla Serie A?
+  context: Il campionato di Serie A è la massima divisione professionistica del campionato italiano di calcio maschile
+model-index:
+- name: osiria/bert-italian-cased-question-answering
+  results:
+  - task:
+      type: question-answering
+      name: Question Answering
+    dataset:
+      name: squad_it
+      type: squad_it
+    metrics:
+    - type: exact-match
+      value: 0.6572
+      name: Exact Match
+    - type: f1
+      value: 0.7706
+      name: F1
+pipeline_tag: question-answering
 ---
+--------------------------------------------------------------------------------------------------
+<body>
+<span class="vertical-text" style="background-color:lightgreen;border-radius: 3px;padding: 3px;"> </span>
+<br>
+<span class="vertical-text" style="background-color:orange;border-radius: 3px;padding: 3px;">    Task: Question Answering</span>
+<br>
+<span class="vertical-text" style="background-color:lightblue;border-radius: 3px;padding: 3px;">    Model: BERT</span>
+<br>
+<span class="vertical-text" style="background-color:tomato;border-radius: 3px;padding: 3px;">    Lang: IT</span>
+<br>
+<span class="vertical-text" style="background-color:lightgrey;border-radius: 3px;padding: 3px;">  </span>
+<br>
+<span class="vertical-text" style="background-color:#CF9FFF;border-radius: 3px;padding: 3px;"> </span>
+</body>
+--------------------------------------------------------------------------------------------------
+<h3>Model description</h3>
+This is a <b>BERT</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>
+If you are looking for a more accurate (but slightly heavier) model, you can refer to: https://huggingface.co/osiria/deberta-italian-question-answering
+<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, [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) is used as a pre-trained model. When the double fine-tuning is completed, the embedding layer is then compressed as in [bert-base-italian-cased](https://huggingface.co/osiria/bert-base-italian-cased) 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:
+| EM | F1 |
+| ------ | ------ |
+| 65.72 | 77.06 |
+Testing notebook: https://huggingface.co/osiria/bert-italian-cased-question-answering/blob/main/osiria_bert_italian_cased_qa_evaluation.ipynb
+<h3>Quick usage</h3>
+```python
+from transformers import BertTokenizerFast, BertForQuestionAnswering
+from transformers import pipeline
+tokenizer = BertTokenizerFast.from_pretrained("osiria/bert-italian-cased-question-answering")
+model = BertForQuestionAnswering.from_pretrained("osiria/bert-italian-cased-question-answering")
+pipeline_qa = pipeline("question-answering", model = model, tokenizer = tokenizer)
+pipeline_qa(context = "Alessandro Manzoni è nato a Milano nel 1785", question = "Dove è nato Manzoni?")
+# {'score': 0.9922313690185547, 'start': 28, 'end': 34, 'answer': 'Milano'}
+```
+<h3>References</h3>
+[1] https://arxiv.org/abs/1810.04805
+[2] https://link.springer.com/chapter/10.1007/978-3-030-03840-3_29
+<h3>Limitations</h3>
+This model was trained SQuAD-IT which is mainly a machine translated version of the original SQuAD v1.1. This means that the quality of the training set is limited by the machine translation.
+Moreover, the model is meant to answer questions under the assumption that the required information is actually contained in the given context (which is the underlying assumption of SQuAD v1.1).
+If the assumption is violated, the model will try to return an answer in any case, which is going to be incorrect.
+<h3>License</h3>
+The model is released under <b>Apache-2.0</b> license