Update files from the datasets library (from 1.17.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.17.0

.gitattributes

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

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- en-US
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+paperswithcode_id: onestopqa
+pretty_name: OneStopQA
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+- extended|onestop_english
+task_categories:
+- question-answering
+task_ids:
+- multiple-choice-qa
+---
+
+# Dataset Card for OneStopQA
+
+## 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:** [OneStopQA repository](https://github.com/berzak/onestop-qa)
+- **Repository:** [OneStopQA repository](https://github.com/berzak/onestop-qa)
+- **Paper:** [STARC: Structured Annotations for Reading Comprehension](https://arxiv.org/abs/2004.14797)
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme. The reading materials are Guardian articles taken from the [OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. Each paragraph is annotated with three multiple choice reading comprehension questions. The reading comprehension questions can be answered based on any of the three paragraph levels.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+English (`en-US`).
+
+The original Guardian articles were manually converted from British to American English.
+
+## Dataset Structure
+
+### Data Instances
+
+An example of instance looks as follows.
+
+```json
+{
+  "title": "101-Year-Old Bottle Message",
+  "paragraph": "Angela Erdmann never knew her grandfather. He died in 1946, six years before she was born. But, on Tuesday 8th April, 2014, she described the extraordinary moment when she received a message in a bottle, 101 years after he had lobbed it into the Baltic Sea. Thought to be the world’s oldest message in a bottle, it was presented to Erdmann by the museum that is now exhibiting it in Germany.",
+  "paragraph_index": 1,
+  "level": "Adv",
+  "question": "How did Angela Erdmann find out about the bottle?", 
+  "answers": ["A museum told her that they had it", 
+              "She coincidentally saw it at the museum where it was held", 
+              "She found it in her basement on April 28th, 2014", 
+              "A friend told her about it"],
+  "a_span": [56, 70], 
+  "d_span": [16, 34]
+}
+```
+Where, 
+
+| Answer | Description                                                | Textual Span    |
+|--------|------------------------------------------------------------|-----------------|
+| a      | Correct answer.                                            | Critical Span   |
+| b      | Incorrect answer. A miscomprehension of the critical span. | Critical Span   |
+| c      | Incorrect answer. Refers to an additional span.            | Distractor Span |
+| d      | Incorrect answer. Has no textual support.                  | -               |
+
+The order of the answers in the `answers` list corresponds to the order of the answers in the table.
+
+### Data Fields
+
+- `title`: A `string` feature. The article title.
+- `paragraph`: A `string` feature. The paragraph from the article. 
+- `paragraph_index`: An `int` feature. Corresponds to the paragraph index in the article.
+- `question`: A `string` feature. The given question.
+- `answers`: A list of `string` feature containing the four possible answers.
+- `a_span`: A list of start and end indices (inclusive) of the critical span.
+- `d_span`: A list of start and end indices (inclusive) of the distractor span.
+
+*Span indices are according to word positions after whitespace tokenization. 
+
+**In the rare case where a span is spread over multiple sections, 
+the span list will contain multiple instances of start and stop indices in the format:
+  [start_1, stop_1, start_2, stop_2,...].
+
+
+### Data Splits
+
+Articles: 30  
+Paragraphs: 162  
+Questions: 486  
+Question-Paragraph Level pairs: 1,458  
+
+No preconfigured split is currently provided.
+
+
+## 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
+
+The annotation and piloting process of the dataset is described in Appendix A in 
+[STARC: Structured Annotations for Reading Comprehension](https://aclanthology.org/2020.acl-main.507.pdf).
+
+#### 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
+
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+
+### Citation Information
+
+[STARC: Structured Annotations for Reading Comprehension](http://people.csail.mit.edu/berzak/papers/acl2020.pdf)  
+```
+@inproceedings{starc2020,  
+      author    = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger},  
+      title     = {STARC: Structured Annotations for Reading Comprehension},  
+      booktitle = {ACL},  
+      year      = {2020},  
+      publisher = {Association for Computational Linguistics} 
+      }
+```
+### Contributions
+
+Thanks to [@scaperex](https://github.com/scaperex) for adding this dataset.

dataset_infos.json

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+{"default": {"description": "OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC (Structured Annotations for Reading Comprehension) scheme. The reading materials are Guardian articles taken from the [OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. Each paragraph is annotated with three multiple choice reading comprehension questions. The reading comprehension questions can be answered based on any of the three paragraph levels.\n", "citation": "@inproceedings{starc2020,\n      author    = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger},\n      title     = {STARC: Structured Annotations for Reading Comprehension},\n      booktitle = {ACL},\n      year      = {2020},\n      publisher = {Association for Computational Linguistics}\n      }\n", "homepage": "https://github.com/berzak/onestop-qa", "license": "Creative Commons Attribution-ShareAlike 4.0 International License", "features": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "paragraph": {"dtype": "string", "id": null, "_type": "Value"}, "level": {"num_classes": 3, "names": ["Adv", "Int", "Ele"], "names_file": null, "id": null, "_type": "ClassLabel"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "paragraph_index": {"dtype": "int32", "id": null, "_type": "Value"}, "answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}, "a_span": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "d_span": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [], "builder_name": "one_stop_qa", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1423090, "num_examples": 1458, "dataset_name": "one_stop_qa"}}, "download_checksums": {"https://github.com/berzak/onestop-qa/raw/master/annotations/onestop_qa.zip": {"num_bytes": 118173, "checksum": "4e9baf4c09797505f7841466d06f521c3d675f7855a987b96c1b3d1fe9ada1ff"}}, "download_size": 118173, "post_processing_size": null, "dataset_size": 1423090, "size_in_bytes": 1541263}}

dummy/1.1.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5257657f0c7165dfe1852d70ece55270ddf6b2b1e0219a8fb4ad0a210a54e94f
+size 20260

onestop_qa.py

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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.
+"""OneStopQA - a multiple choice reading comprehension dataset annotated
+according to the STARC (Structured Annotations for Reading Comprehension) scheme"""
+
+
+import json
+import os
+
+import datasets
+
+
+# from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{starc2020,
+      author    = {Berzak, Yevgeni and Malmaud, Jonathan and Levy, Roger},
+      title     = {STARC: Structured Annotations for Reading Comprehension},
+      booktitle = {ACL},
+      year      = {2020},
+      publisher = {Association for Computational Linguistics}
+      }
+"""
+
+_DESCRIPTION = """\
+OneStopQA is a multiple choice reading comprehension dataset annotated according to the STARC \
+(Structured Annotations for Reading Comprehension) scheme. \
+The reading materials are Guardian articles taken from the \
+[OneStopEnglish corpus](https://github.com/nishkalavallabhi/OneStopEnglishCorpus). \
+Each article comes in three difficulty levels, Elementary, Intermediate and Advanced. \
+Each paragraph is annotated with three multiple choice reading comprehension questions. \
+The reading comprehension questions can be answered based on any of the three paragraph levels.
+"""
+
+_HOMEPAGE = "https://github.com/berzak/onestop-qa"
+
+_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International License"
+
+# 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/berzak/onestop-qa/raw/master/annotations/onestop_qa.zip"
+
+
+class OneStopQA(datasets.GeneratorBasedBuilder):
+    """OneStopQA - a multiple choice reading comprehension dataset annotated
+    according to the STARC (Structured Annotations for Reading Comprehension) scheme"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "title": datasets.Value("string"),
+                "paragraph": datasets.Value("string"),
+                "level": datasets.ClassLabel(names=["Adv", "Int", "Ele"]),
+                "question": datasets.Value("string"),
+                "paragraph_index": datasets.Value("int32"),
+                "answers": datasets.features.Sequence(datasets.Value("string"), length=4),
+                "a_span": datasets.features.Sequence(datasets.Value("int32")),
+                "d_span": datasets.features.Sequence(datasets.Value("int32")),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # 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,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+            task_templates=[]
+            #     QuestionAnsweringExtractive(
+            #         question_column="question", context_column="context", answers_column="answers"
+            #     )
+            # ], # When issue #2434 is resolved uncomment task_templates and the QuestionAnsweringExtractive (or similar)
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "onestop_qa.json"),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """Yields examples as (key, example) tuples."""
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+        # Based on the squad dataset
+        logger.info("generating examples from = %s", filepath)
+        key = 0
+        with open(filepath, encoding="utf-8") as f:
+            onestop_qa = json.load(f)
+            for article in onestop_qa["data"]:
+                title = article.get("title", "")
+                for paragraph_index, paragraph in enumerate(article["paragraphs"]):
+                    for level in ["Adv", "Int", "Ele"]:
+                        paragraph_context_and_spans = paragraph[level]
+                        paragraph_context = paragraph_context_and_spans["context"]
+                        a_spans = paragraph_context_and_spans["a_spans"]
+                        d_spans = paragraph_context_and_spans["d_spans"]
+                        qas = paragraph["qas"]
+                        for qa, a_span, d_span in zip(qas, a_spans, d_spans):
+                            yield key, {
+                                "title": title,
+                                "paragraph": paragraph_context,
+                                "question": qa["question"],
+                                "paragraph_index": paragraph_index,
+                                "answers": qa["answers"],
+                                "level": level,
+                                "a_span": a_span,
+                                "d_span": d_span,
+                            },
+
+                            key += 1