Convert dataset to Parquet

README.md

@@ -47,8 +47,18 @@ dataset_info:
   - name: test
     num_bytes: 36397776
     num_examples: 23936
-  download_size: 10491714
+  download_size: 19770316
   dataset_size: 116002270
+configs:
+- config_name: sberquad
+  data_files:
+  - split: train
+    path: sberquad/train-*
+  - split: validation
+    path: sberquad/validation-*
+  - split: test
+    path: sberquad/test-*
+  default: true
 ---
 
 

sberquad.py

@@ -1,104 +0,0 @@
-# coding=utf-8
-"""SberQUAD: Sber Question Answering Dataset."""
-
-import os
-import json
-
-import datasets
-from datasets.tasks import QuestionAnsweringExtractive
-
-
-logger = datasets.logging.get_logger(__name__)
-
-_CITATION = """\
-@article{Efimov_2020,
-   title={SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis},
-   ISBN={9783030582197},
-   ISSN={1611-3349},
-   url={http://dx.doi.org/10.1007/978-3-030-58219-7_1},
-   DOI={10.1007/978-3-030-58219-7_1},
-   journal={Experimental IR Meets Multilinguality, Multimodality, and Interaction},
-   publisher={Springer International Publishing},
-   author={Efimov, Pavel and Chertok, Andrey and Boytsov, Leonid and Braslavski, Pavel},
-   year={2020},
-   pages={3–15}
-}
- """
-
-
-_DESCRIPTION = """\
-Sber Question Answering Dataset (SberQuAD) is a reading comprehension \
-dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
-articles, where the answer to every question is a segment of text, or span, \
-from the corresponding reading passage, or the question might be unanswerable. \
-Russian original analogue presented in Sberbank Data Science Journey 2017.
-"""
-
-_URLS = {"train": os.path.join("data", "train_v1.0.json.gz"), "dev": os.path.join("data", "dev_v1.0.json.gz"), "test": os.path.join("data", "origin_test.json.gz")}
-
-
-class Sberquad(datasets.GeneratorBasedBuilder):
-    """SberQUAD: Sber Question Answering Dataset. Version 1.0."""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [datasets.BuilderConfig(name="sberquad", version=VERSION, description=_DESCRIPTION)]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("int32"),
-                    "title": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="",
-            citation=_CITATION,
-            task_templates=[
-                QuestionAnsweringExtractive(
-                    question_column="question", context_column="context", answers_column="answers"
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        key = 0
-        with open(filepath, encoding="utf-8") as f:
-            squad = json.load(f)
-            for article in squad["data"]:
-                title = article.get("title", "")
-                for paragraph in article["paragraphs"]:
-                    context = paragraph["context"]
-                    for qa in paragraph["qas"]:
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"] for answer in qa["answers"]]
-                        yield key, {
-                            "title": title,
-                            "context": context,
-                            "question": qa["question"],
-                            "id": qa["id"],
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }
-                        key += 1

data/dev_v1.0.json.gz → sberquad/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a9145c18e890e84fd070272fcaa607a688efd61debf36b89bc2b96654ac77d18
-size 1927670
+oid sha256:6e92c4d24f2fb2d70d4046237ed8f277b4889b9a424e41eebddf97e3d62f9ac9
+size 4927641

data/train_v1.0.json.gz → sberquad/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5a05221fe35eb1b4fcd17b8acca1d957e12d098783d8e8a2ebb2348f6c437b0a
-size 5838123
+oid sha256:9c0ec202bab5e3a0ebfeb9d09943b88777fa2ddce4feb8f43b4c2e766f3dd095
+size 11416696

data/origin_test.json.gz → sberquad/validation-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5d24ee67e71ea95163342b72d8fb36914fa7d0b261b3552ed5824f355fe76963
-size 2725921
+oid sha256:6a2617d1212cb32e26e5a19f95a3e36a9c89fca8e60f6bcc9fe363ea78610d6f
+size 3425979