Update files from the datasets library (from 1.13.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.13.0

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README.md

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+---
+pretty_name: SberQuAD
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+- crowdsourced
+languages:
+- ru
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+paperswithcode_id: sberquad
+---
+
+
+# Dataset Card for sberquad
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [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:** [Needs More Information]
+- **Repository:** https://github.com/sberbank-ai/data-science-journey-2017
+- **Paper:** https://arxiv.org/abs/1912.09723
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+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.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+Russian
+
+## Dataset Structure
+
+### Data Instances
+```
+{
+    "context": "Первые упоминания о строении человеческого тела встречаются в Древнем Египте...",
+    "id": 14754,
+    "qas": [
+        {
+            "id": 60544,
+            "question": "Где встречаются первые упоминания о строении человеческого тела?",
+            "answers": [{"answer_start": 60, "text": "в Древнем Египте"}],
+        }
+    ]
+}
+```
+
+### Data Fields
+
+- id: a int32 feature
+- title: 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 |validation|test |
+|----------|-----:|---------:|-----|
+|plain_text|45328 | 5036     |23936|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+```
+@article{DBLP:journals/corr/abs-1912-09723,
+  author    = {Pavel Efimov and
+               Leonid Boytsov and
+               Pavel Braslavski},
+  title     = {SberQuAD - Russian Reading Comprehension Dataset: Description and
+               Analysis},
+  journal   = {CoRR},
+  volume    = {abs/1912.09723},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1912.09723},
+  eprinttype = {arXiv},
+  eprint    = {1912.09723},
+  timestamp = {Fri, 03 Jan 2020 16:10:45 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1912-09723.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+### Contributions
+
+Thanks to [@alenusch](https://github.com/Alenush) for adding this dataset.

dataset_infos.json

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+{"sberquad": {"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.\n", "citation": "@article{Efimov_2020,\n   title={SberQuAD \u2013 Russian Reading Comprehension Dataset: Description and Analysis},\n   ISBN={9783030582197},\n   ISSN={1611-3349},\n   url={http://dx.doi.org/10.1007/978-3-030-58219-7_1},\n   DOI={10.1007/978-3-030-58219-7_1},\n   journal={Experimental IR Meets Multilinguality, Multimodality, and Interaction},\n   publisher={Springer International Publishing},\n   author={Efimov, Pavel and Chertok, Andrey and Boytsov, Leonid and Braslavski, Pavel},\n   year={2020},\n   pages={3\u201315}\n}\n ", "homepage": "", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "title": {"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": "sberquad", "config_name": "sberquad", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 71631661, "num_examples": 45328, "dataset_name": "sberquad"}, "validation": {"name": "validation", "num_bytes": 7972977, "num_examples": 5036, "dataset_name": "sberquad"}, "test": {"name": "test", "num_bytes": 36397848, "num_examples": 23936, "dataset_name": "sberquad"}}, "download_checksums": {"https://sc.link/PNWl": {"num_bytes": 38616884, "checksum": "861b55219f1549139e64b2eed325b54ce9c9c63b792a2c2b3cfbec997aa3d88e"}, "https://sc.link/W6oX": {"num_bytes": 8807953, "checksum": "247bede36a27f076f607117632f39eedb9bb1d80c34d93bbfaeda71fd30fd382"}, "https://sc.link/VOn9": {"num_bytes": 18622439, "checksum": "7793d389208271a76ab38a5dba5cebc98e72f45e99196a99e14b5a37c401c66f"}}, "download_size": 66047276, "post_processing_size": null, "dataset_size": 116002486, "size_in_bytes": 182049762}}

dummy/sberquad/1.0.0/dummy_data.zip

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+oid sha256:e1bffceb9f4759941a6b08ff3ae5ebe24f047b2411bb7280c788b76ee9780853
+size 1276

sberquad.py

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+# coding=utf-8
+"""SberQUAD: Sber Question Answering Dataset."""
+
+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": "https://sc.link/PNWl", "dev": "https://sc.link/W6oX", "test": "https://sc.link/VOn9"}
+
+
+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