Convert dataset to Parquet (#4)

- Convert dataset to Parquet (24c2ae4e21b066725683a3acdd80c8454acb7453)
- Delete loading script (f0019503d0edfbcef3ce534c26a7a646595854e9)

README.md

@@ -5,8 +5,6 @@ language_creators:
 - machine-generated
 language:
 - it
-language_bcp47:
-- it-IT
 license:
 - unknown
 multilinguality:
@@ -22,6 +20,8 @@ task_ids:
 - extractive-qa
 paperswithcode_id: squad-it
 pretty_name: SQuAD-it
+language_bcp47:
+- it-IT
 dataset_info:
   features:
   - name: id
@@ -38,13 +38,20 @@ dataset_info:
       dtype: int32
   splits:
   - name: train
-    num_bytes: 50864824
+    num_bytes: 50864680
     num_examples: 54159
   - name: test
-    num_bytes: 7858336
+    num_bytes: 7858312
     num_examples: 7609
-  download_size: 8776531
-  dataset_size: 58723160
+  download_size: 13797580
+  dataset_size: 58722992
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for "squad_it"

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:72eb99d7e396079f5f97d2a4a0322afd9f186539318d22c522456fc966802f5d
+size 1624373

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61afb4156ad32e349e33e4d82bb97bca8f2195863bf070c2fdd6d00c4b1c76bd
+size 12173207

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,
-                            },
-                        }