--- license: mit language: - it datasets: - squad_it widget: - text: Quale libro fu scritto da Alessandro Manzoni? context: Alessandro Manzoni pubblicò la prima versione dei 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/deberta-italian-question-answering results: - task: type: question-answering name: Question Answering dataset: name: squad_it type: squad_it metrics: - type: exact-match value: 0.7004 name: Exact Match - type: f1 value: 0.8097 name: F1 pipeline_tag: question-answering --- --------------------------------------------------------------------------------------------------
    Task: Question Answering
    Model: DeBERTa
    Lang: IT
  
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Model description

This is a DeBERTa [1] model for the Italian language, fine-tuned for Extractive Question Answering on the [SQuAD-IT](https://huggingface.co/datasets/squad_it) dataset [2], using DeBERTa-ITALIAN ([deberta-base-italian](https://huggingface.co/osiria/deberta-base-italian)) as a pre-trained model. update: version 2.0 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 procedure, [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) is now directly 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

Training and Performances

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.
The dataset includes 54.159 training instances and 7.609 test instances The performances on the test set are reported in the following table: (version 2.0 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

Quick usage

In order to get the best possible outputs from the model, it is recommended to use the following pipeline ```python from transformers import DebertaV2TokenizerFast, DebertaV2ForQuestionAnswering import re import string from transformers.pipelines import QuestionAnsweringPipeline tokenizer = DebertaV2TokenizerFast.from_pretrained("osiria/deberta-italian-question-answering") model = DebertaV2ForQuestionAnswering.from_pretrained("osiria/deberta-italian-question-answering") class osiria_qa(QuestionAnsweringPipeline): def __init__(self, punctuation = ',;.:!?()[\]{}', **kwargs): QuestionAnsweringPipeline.__init__(self, **kwargs) self.post_regex_left = "^[\s" + punctuation + "]+" self.post_regex_right = "[\s" + punctuation + "]+$" def postprocess(self, output): output = QuestionAnsweringPipeline.postprocess(self, model_outputs=output) output_length = len(output["answer"]) output["answer"] = re.sub(self.post_regex_left, "", output["answer"]) output["start"] = output["start"] + (output_length - len(output["answer"])) output_length = len(output["answer"]) output["answer"] = re.sub(self.post_regex_right, "", output["answer"]) output["end"] = output["end"] - (output_length - len(output["answer"])) return output pipeline_qa = osiria_qa(model = model, tokenizer = tokenizer) pipeline_qa(context = "Alessandro Manzoni è nato a Milano nel 1785", question = "Dove è nato Manzoni?") # {'score': 0.9899800419807434, 'start': 28, 'end': 34, 'answer': 'Milano'} ```

References

[1] https://arxiv.org/abs/2111.09543 [2] https://link.springer.com/chapter/10.1007/978-3-030-03840-3_29

Limitations

This model was trained on the English SQuAD v2 and on 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.

License

The model is released under MIT license