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README.md

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----
-annotations_creators:
-- machine-generated
-language_creators:
-- machine-generated
-language:
-- it
-language_bcp47:
-- it-IT
-license:
-- unknown
-multilinguality:
-- monolingual
-size_categories:
-- unknown
-source_datasets:
-- extended|squad
-task_categories:
-- question-answering
-task_ids:
-- open-domain-qa
-- extractive-qa
-paperswithcode_id: squad-it
-pretty_name: SQuAD-it
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: context
-    dtype: string
-  - name: question
-    dtype: string
-  - name: answers
-    sequence:
-    - name: text
-      dtype: string
-    - name: answer_start
-      dtype: int32
-  splits:
-  - name: train
-    num_bytes: 50864824
-    num_examples: 54159
-  - name: test
-    num_bytes: 7858336
-    num_examples: 7609
-  download_size: 8776531
-  dataset_size: 58723160
----
-
-# Dataset Card for "squad_it"
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://github.com/crux82/squad-it](https://github.com/crux82/squad-it)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 8.37 MB
-- **Size of the generated dataset:** 56.07 MB
-- **Total amount of disk used:** 64.44 MB
-
-### Dataset Summary
-
-SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
-into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
- The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is
- split into training and test sets to support the replicability of the benchmarking of QA systems:
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Dataset Structure
-
-### Data Instances
-
-#### default
-
-- **Size of downloaded dataset files:** 8.37 MB
-- **Size of the generated dataset:** 56.07 MB
-- **Total amount of disk used:** 64.44 MB
-
-An example of 'train' looks as follows.
-```
-This example was too long and was cropped:
-
-{
-    "answers": "{\"answer_start\": [243, 243, 243, 243, 243], \"text\": [\"evitare di essere presi di mira dal boicottaggio\", \"evitare di essere pres...",
-    "context": "\"La crisi ha avuto un forte impatto sulle relazioni internazionali e ha creato una frattura all' interno della NATO. Alcune nazi...",
-    "id": "5725b5a689a1e219009abd28",
-    "question": "Perchè le nazioni europee e il Giappone si sono separati dagli Stati Uniti durante la crisi?"
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-#### default
-- `id`: a `string` feature.
-- `context`: a `string` feature.
-- `question`: a `string` feature.
-- `answers`: a dictionary feature containing:
-  - `text`: a `string` feature.
-  - `answer_start`: a `int32` feature.
-
-### Data Splits
-
-| name    | train | test |
-| ------- | ----: | ---: |
-| default | 54159 | 7609 |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-@InProceedings{10.1007/978-3-030-03840-3_29,
-	author="Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto",
-	editor="Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
-	title="Neural Learning for Question Answering in Italian",
-	booktitle="AI*IA 2018 -- Advances in Artificial Intelligence",
-	year="2018",
-	publisher="Springer International Publishing",
-	address="Cham",
-	pages="389--402",
-	isbn="978-3-030-03840-3"
-}
-
-```
-
-
-### Contributions
-
-Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset\ninto Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.\n The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is\n split into training and test sets to support the replicability of the benchmarking of QA systems:\n", "citation": "@InProceedings{10.1007/978-3-030-03840-3_29,\n    author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto},\n    editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo\",\n    title={Neural Learning for Question Answering in Italian},\n    booktitle={AI*IA 2018 -- Advances in Artificial Intelligence},\n    year={2018},\n    publisher={Springer International Publishing},\n    address={Cham},\n    pages={389--402},\n    isbn={978-3-030-03840-3}\n}\n", "homepage": "https://github.com/crux82/squad-it", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "question-answering-extractive", "question_column": "question", "context_column": "context", "answers_column": "answers"}], "builder_name": "squad_it", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 50864824, "num_examples": 54159, "dataset_name": "squad_it"}, "test": {"name": "test", "num_bytes": 7858336, "num_examples": 7609, "dataset_name": "squad_it"}}, "download_checksums": {"https://github.com/crux82/squad-it/raw/master/SQuAD_it-train.json.gz": {"num_bytes": 7725286, "checksum": "75d4d2832961f7a0f76a43d7e919e56a880ccc55de434ec90ae82cd67bec5d25"}, "https://github.com/crux82/squad-it/raw/master/SQuAD_it-test.json.gz": {"num_bytes": 1051245, "checksum": "25986c617cc7d58e82e916755b8a5684e5efae69835332858a6534a304cd293c"}}, "download_size": 8776531, "post_processing_size": null, "dataset_size": 58723160, "size_in_bytes": 67499691}}

default/squad_it-test.parquet

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default/squad_it-train.parquet

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squad_it.py

@@ -1,116 +0,0 @@
-"""TODO(squad_it): Add a description here."""
-
-
-import json
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-# TODO(squad_it): BibTeX citation
-_CITATION = """\
-@InProceedings{10.1007/978-3-030-03840-3_29,
-    author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto},
-    editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
-    title={Neural Learning for Question Answering in Italian},
-    booktitle={AI*IA 2018 -- Advances in Artificial Intelligence},
-    year={2018},
-    publisher={Springer International Publishing},
-    address={Cham},
-    pages={389--402},
-    isbn={978-3-030-03840-3}
-}
-"""
-
-# TODO(squad_it):
-_DESCRIPTION = """\
-SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
-into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
- The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is
- split into training and test sets to support the replicability of the benchmarking of QA systems:
-"""
-
-_URL = "https://github.com/crux82/squad-it/raw/master/"
-_URLS = {
-    "train": _URL + "SQuAD_it-train.json.gz",
-    "test": _URL + "SQuAD_it-test.json.gz",
-}
-
-
-class SquadIt(datasets.GeneratorBasedBuilder):
-    """TODO(squad_it): Short description of my dataset."""
-
-    # TODO(squad_it): Set up version.
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # TODO(squad_it): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://github.com/crux82/squad-it",
-            citation=_CITATION,
-            task_templates=[
-                QuestionAnsweringExtractive(
-                    question_column="question", context_column="context", answers_column="answers"
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(squad_it): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(squad_it): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for example in data["data"]:
-                for paragraph in example["paragraphs"]:
-                    context = paragraph["context"].strip()
-                    for qa in paragraph["qas"]:
-                        question = qa["question"].strip()
-                        id_ = qa["id"]
-
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"].strip() for answer in qa["answers"]]
-
-                        yield id_, {
-                            "context": context,
-                            "question": question,
-                            "id": id_,
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }