Update files from the datasets library (from 1.2.0)

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

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en
+licenses:
+- apache-2-0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- question-answering
+task_ids:
+- closed-domain-qa
+- extractive-qa
+---
+
+
+# Dataset Card for COVID-QA
+
+## 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
+
+- **Repository:** https://github.com/deepset-ai/COVID-QA
+- **Paper:** https://openreview.net/forum?id=JENSKEEzsoU
+- **Point of Contact:** [deepset AI](https://github.com/deepset-ai)
+
+### Dataset Summary
+
+COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19.
+A total of 147 scientific articles from the CORD-19 dataset were annotated by 15 experts.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The text in the dataset is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+**What do the instances that comprise the dataset represent?**
+Each represents a question, a context (document passage from the CORD19 dataset) and an answer.
+
+**How many instances are there in total?**
+2019 instances
+
+**What data does each instance consist of?**
+Each instance is a question, a set of answers, and an id associated with each answer.
+
+[More Information Needed]
+
+### Data Fields
+
+The data was annotated in SQuAD style fashion, where each row contains:
+
+* **question**: Query question
+* **context**: Context text to obtain the answer from
+* **document_id** The document ID of the context text
+* **answer**: Dictionary containing the answer string and the start index
+
+### Data Splits
+
+**data/COVID-QA.json**: 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19.
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The inital data collected comes from 147 scientific articles from the CORD-19 dataset. Question and answers were then
+annotated afterwards.
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+While annotators were volunteers, they were required to have at least a Master’s degree in biomedical sciences. 
+The annotation team was led by a medical doctor (G.A.R.) who vetted the volunteer’s credentials and 
+manually verified each question/answer pair produced. We used an existing, web-based annotation tool that had been 
+created by deepset and is available at their Neural Search framework [haystack](https://github.com/deepset-ai/haystack).
+
+#### Who are the annotators?
+
+The annotators are 15 volunteer biomedical experts on scientific articles related to COVID-19.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The dataset aims to help build question answering models serving clinical and scientific researchers, public health authorities, and frontline workers.
+These QA systems can help them find answers and patterns in research papers by locating relevant answers to common questions from scientific articles.
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+## Additional Information
+
+The listed authors in the homepage are maintaining/supporting the dataset. 
+
+### Dataset Curators
+
+[More Information Needed]
+
+The Proto_qa dataset is licensed under 
+the [Apache License 2.0](https://github.com/deepset-ai/COVID-QA/blob/master/LICENSE)
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@inproceedings{moller2020covid,
+  title={COVID-QA: A Question Answering Dataset for COVID-19},
+  author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},
+  booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},
+  year={2020}
+}
+```

covid_qa_deepset.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.
+"""COVID-QA: A Question Answering Dataset for COVID-19."""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import logging
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{moller2020covid,
+  title={COVID-QA: A Question Answering Dataset for COVID-19},
+  author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},
+  booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},
+  year={2020}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical \
+experts on scientific articles related to COVID-19.
+"""
+
+_HOMEPAGE = "https://github.com/deepset-ai/COVID-QA"
+
+_LICENSE = "Apache License 2.0"
+
+_URL = "https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/question-answering/"
+_URLs = {"covid_qa_deepset": _URL + "COVID-QA.json"}
+
+
+class CovidQADeepset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="covid_qa_deepset", version=VERSION, description="COVID-QA deepset"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "document_id": datasets.Value("int32"),
+                "context": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "is_impossible": datasets.Value("bool"),
+                "id": datasets.Value("int32"),
+                "answers": datasets.features.Sequence(
+                    {
+                        "text": datasets.Value("string"),
+                        "answer_start": datasets.Value("int32"),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        url = _URLs[self.config.name]
+        downloaded_filepath = dl_manager.download_and_extract(url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": downloaded_filepath},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logging.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            covid_qa = json.load(f)
+            for article in covid_qa["data"]:
+                for paragraph in article["paragraphs"]:
+                    context = paragraph["context"].strip()
+                    document_id = paragraph["document_id"]
+                    for qa in paragraph["qas"]:
+                        question = qa["question"].strip()
+                        is_impossible = qa["is_impossible"]
+                        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_, {
+                            "document_id": document_id,
+                            "context": context,
+                            "question": question,
+                            "is_impossible": is_impossible,
+                            "id": id_,
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                        }

dataset_infos.json

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+{"covid_qa_deepset": {"description": "COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19.\n", "citation": "@inproceedings{moller2020covid,\n  title={COVID-QA: A Question Answering Dataset for COVID-19},\n  author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},\n  booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},\n  year={2020}\n}\n", "homepage": "https://github.com/deepset-ai/COVID-QA", "license": "Apache License 2.0", "features": {"document_id": {"dtype": "int32", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "is_impossible": {"dtype": "bool", "id": null, "_type": "Value"}, "id": {"dtype": "int32", "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, "builder_name": "covid_qa_deepset", "config_name": "covid_qa_deepset", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 65151262, "num_examples": 2019, "dataset_name": "covid_qa_deepset"}}, "download_checksums": {"https://raw.githubusercontent.com/deepset-ai/COVID-QA/master/data/question-answering/COVID-QA.json": {"num_bytes": 4418117, "checksum": "291abf17f4bc2bd343838fd8ef5debb6278bbbb61b262db1f1bd58048fff76b9"}}, "download_size": 4418117, "post_processing_size": null, "dataset_size": 65151262, "size_in_bytes": 69569379}}

dummy/covid_qa_deepset/1.0.0/dummy_data.zip

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