Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bin.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+  ar:
+  - ar
+  de:
+  - de
+  el:
+  - el
+  en:
+  - en
+  es:
+  - es
+  hi:
+  - hi
+  ru:
+  - ru
+  th:
+  - th
+  tr:
+  - tr
+  vi:
+  - vi
+  zh:
+  - zh
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|squad
+- extended|xquad
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+---
+
+# Dataset Card for [Dataset Name]
+
+## 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)
+
+## Dataset Description
+
+- **Homepage:** [LAReQA](https://github.com/google-research-datasets/lareqa)
+- **Repository:** [XQuAD-R](https://github.com/google-research-datasets/lareqa)
+- **Paper:** [LAReQA: Language-agnostic answer retrieval from a multilingual pool](https://arxiv.org/pdf/2004.05484.pdf)
+- **Point of Contact:** [Noah Constant](mailto:nconstant@google.com)
+
+
+### Dataset Summary
+ 
+XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive
+QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset,  where each
+question appears in 11 different languages and has 11 parallel correct answers
+across the languages.
+
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The dataset can be found with the following languages:
+* Arabic: `xquad-r/ar.json`
+* German: `xquad-r/de.json`
+* Greek: `xquad-r/el.json`
+* English: `xquad-r/en.json`
+* Spanish: `xquad-r/es.json`
+* Hindi: `xquad-r/hi.json`
+* Russian: `xquad-r/ru.json`
+* Thai: `xquad-r/th.json`
+* Turkish: `xquad-r/tr.json`
+* Vietnamese: `xquad-r/vi.json`
+* Chinese: `xquad-r/zh.json`
+
+## Dataset Structure
+
+[More Information Needed]
+
+### Data Instances
+
+The number of questions and candidate sentences for each language for XQuAD-R is shown in the table below:
+
+|     | XQuAD-R   |            |
+|-----|-----------|------------|
+|     | questions | candidates |
+| ar |      1190 |       1222 |
+| de |      1190 |       1276 |
+| el |      1190 |       1234 |
+| en |      1190 |       1180 |
+| es |      1190 |       1215 |
+| hi |      1190 |       1244 |
+| ru |      1190 |       1219 |
+| th |      1190 |        852 |
+| tr |      1190 |       1167 |
+| vi |      1190 |       1209 |
+| zh |      1190 |       1196 |
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+[More Information Needed]
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+[More Information Needed]
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+[More Information Needed]
+
+### Dataset Curators
+
+The dataset was initially created by Uma Roy, Noah Constant, Rami Al-Rfou, Aditya Barua, Aaron Phillips and Yinfei Yang, during work done at Google Research.
+
+### Licensing Information
+
+XQuAD-R is distributed under the [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/legalcode).
+
+### Citation Information
+
+```
+@article{roy2020lareqa,
+  title={LAReQA: Language-agnostic answer retrieval from a multilingual pool},
+  author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei},
+  journal={arXiv preprint arXiv:2004.05484},
+  year={2020}
+}
+```

dataset_infos.json

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TODO: Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{roy2020lareqa,
+  title={LAReQA: Language-agnostic answer retrieval from a multilingual pool},
+  author={Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei},
+  journal={arXiv preprint arXiv:2004.05484},
+  year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question appears in 11 different languages and has 11 parallel correct answers across the languages.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/google-research-datasets/lareqa"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "https://github.com/google-research-datasets/lareqa/raw/master/xquad-r/"
+_LANG = ["ar", "de", "zh", "vi", "en", "es", "hi", "el", "th", "tr", "ru"]
+
+
+class XquadRConfig(datasets.BuilderConfig):
+
+    """ BuilderConfig for XquadR"""
+
+    def __init__(self, lang, **kwargs):
+        """
+        Args:
+            lang: string, language for the input text
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(XquadRConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.lang = lang
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class XquadR(datasets.GeneratorBasedBuilder):
+    """TODO(xquad-r): Short description of my dataset."""
+
+    # TODO(xquad-r): Set up version.
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIGS = [XquadRConfig(name="{}".format(lang), description=_DESCRIPTION, lang=lang) for lang in _LANG]
+
+    def _info(self):
+        # TODO(xquad-r): 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"),
+                        }
+                    ),
+                }
+            ),
+            # 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=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(xquad-r): 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 = {lang: _URL + "{}.json".format(lang) for lang in _LANG}
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": downloaded_files[self.config.lang]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(xquad-r): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for article in data["data"]:
+                for paragraph in article["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"]]
+
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        yield id_, {
+                            "context": context,
+                            "question": question,
+                            "id": id_,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                        }